# New MS/PhD Student: Alyazeed Basyoni

Alyazeed Basyoni just arrived at KAUST to start his MS/PhD studies under my supervision. Welcome!!!

In 2019, Alyazeed obtained his BS in Computer Science from Carnegie Mellon University. Desiting to learn more, Alyazeed ended up taking many graduate level courses, inlcuding courses in Probability Theory, Deep Reinforcement Learning, Convex Optimization, Machine Learning, Randomized Algorithms, Probabilistic Combinatorics, and Measure and Integration.

Alyazeed already has varied industrial experience:
- At Ansatz, he implemented a fast, low cost, futures execution engine (it was deployed)
- At Dropbox, he implemented a tool that allows clients to search, preview, select and embed content from third-party providers into Paper.
- At Petuum, he contributed to the open source Dynamic Neural Network package, DyNet.

When Alyazeed is bored, he writes OS kernels (in C, from scratch), helps the USA mathematics olympiad team by grading mock exams and delivering short lectures, programs games, and fools around with C, Python, SML, OCaml, and Go.

Alyazeed has a Silver Medal from the 53rd International Mathematics Olympiad (held in Mar del Plata, Argentina in 2012), where he represented Saudi Arabia. By the way, at the same Olympiad, my student Alibek Sailanbayev got a Bronze Medal. What a coincidence! Alyazeed was the first Saudi to win a Silver medal at IMO.

At KAUST, you will find Alyazeed in Building 1, Level 2.

# New MS/PhD Student: Slavomír Hanzely

Slavomír Hanzely just arrived at KAUST to start his MS/PhD studies under my supervision. Welcome!!!

In 2019, Slavomír ("Slavo") obtained his BS degree in Computer Science from Comenius University, Slovakia. This, by the way, is also where I studied back in the day. Slavo was eager to learn faster than the study program required, and ended up taking many more courses than necessary - all without sacrificing his grades.

Throughout his high schools and university studies, Slavo has been active in various mathematical and computer science olympiads and competitions, at regional, national and international level. Here are some highlights from his achievements:
- 2017, 8-10th Place in Vojtech Jarnik International Mathematical Competition (1st place among Czech and Slovak contestants)
- 2016, represented Slovakia at the 57th International Mathematical Olympiad (held in Hong Kong)
- 2016, 3rd Place at the Slovak National Mathematical Olympiad
- 2016, 1st Place at Slovak Mathematical Olympiad, Regional Round
- 2016, 1st Place at Slovak Informatics Olympiad, Regional Round
- 2015, Bronze Medal, Middle European Mathematical Olympiad
- 2015, 2nd Place at Slovak Informatics Olympiad, Regional Round
- 2014, 1st Place at Slovak Mathematical Olympiad, Regional Round
- 2013, 1st Place at Slovak Mathematical Olympiad, Regional Round

Slavo has been active with marking solutions for the Slovak National Mathematical Olympiad, preparing the Slovak team for the International Mathematical Olympiad, marking solutions of various correspondence contests in mathematics and computer science, and organizing summer camps for highly talented Slovak pupils in mathematics and computer science.

At KAUST, you will find Slavo in Building 1, Level 2.

Disambiguation: Slavo's older brother Filip is also at KAUST, studying towards his PhD in my group.

# 2 Interviews in 1 Day

I have been interviewed twice today. First by David Murphy for a KAUST article related to the "Distinguished Speaker Award" I received at ICCOPT earlier this month, and then by Ľubica Hargašová (who was kind enough to travel to meet me) for her RTVS (Slovak Radio and Television) radio show "Naši a Svetoví" ("Ours and of the World") about Slovaks who found success abroad. The former interview will lead to a written piece (in English), while the latter interview was recorded and should air at some point in September (in Slovak).

[By the way - I was officially on vacation today...]

# 2 Postdoc Positions

I have two postdoc positions open in the area of optimization and/or machine learning, to be filled by January 2020. If interested, send me an email! Include your CV and explain why you are interested.

Position start: By January 2020

Duration: 1 to 3 years (based on agreement)

Conditions: Very competitive salary and benefits; Travel funding and access to state-of-the-art facilities; On-campus accommodation. The KAUST campus is home of around 7,000 people, and comprises a land area of 36 km2. Includes restaurants, schools, shops, cinema, two private beaches, recreation centers, supermarket, medical center, etc.

Application process: Send an email to me (peter dot richtarik at kaust dot edu dot sa), explain why you are interested in the position, and enclose your CV. If your CV catches my attention, I may ask for reference letters and extra materials. Alternatively, you may instruct your letter writers to send letters to me (by email) right away. Shortlisted candidates will progress to a Skype interview.

# My Group @ ICCOPT

Many members of my (combined KAUST-Edinburgh-MIPT) group attended ICCOPT. Here is info on their talks plus links to the underlying papers and slides (if available):

Several former members of my KAUST and Edinburgh groups attended as well:

It's 18 people in total (and I am not counting students/postdocs of my former students)! We had a distinct presence, and most importantly, had fun at the event!

# ICCOPT Summer School Slides

My ICCOPT summer school course slides are here:

Here are supplementary (flashy Powerpoint) slides about SGD-SR and SEGA.

I was pleasantly surprised to have received a "distinguished speaker" award:

The bear probably represents the speed with which I delivered the lectures... ;-)

# On my way to Berlin for ICCOPT

I am on my way to Berlin to first teach in the ICCOPT Summer School, and then to attend the ICCOPT conference. On August 3rd I will deliver a 1 day (4 x 1.5 hours) short course entitled "A Guided Walk Through the ZOO of Stochastic Gradient Descent Methods". Here is what the course is going to be about:

Stochastic gradient descent (SGD) in one of its many variants is the workhorse method for training modern supervised machine learning models. However, the world of SGD methods is vast and expanding, which makes it hard to understand its landscape and inhabitants. In this tutorial I will offer a guided walk through the ZOO of SGD methods. I will chart the landscape of this beautiful world, and make it easier to understand its inhabitants and their properties. In particular, I will introduce a unified analysis of a large family of variants of proximal stochastic gradient descent (SGD) which so far have required different intuitions, convergence analyses, have different applications, and which have been developed separately in various communities. This framework includes methods with and without the following tricks, and their combinations: variance reduction, data sampling, coordinate sampling, importance sampling, mini-batching and quantization. As a by-product, the presented framework offers the first unified theory of SGD and randomized coordinate descent (RCD) methods, the first unified theory of variance reduced and non-variance-reduced SGD methods, and the first unified theory of quantized and non-quantized methods.

# NeurIPS reviews came in

NeurIPS reviews came in. As usual, most reviewers assigned to evaluate my papers are not quite at home in my area, or simply provide an educated guess only. This leads to many rather meaningless and noisy reviews (this is in sharp contrast with journal submissions in top journals where more often than not the reviewers are knowledgeable). This is something that took me some time to get used to back in the day... The reason for this? A trade-off between the quality of the reviews and the speed of the accept/reject decision. Thanks to the few reviewers who actually understood our results and were able to provide useful feedback! Now we have until July 31 to prepare author response, aka a "rebuttal".

An interesting innovation this year: a system was put in place to automatically flag some papers with a common subset of authors as potentially being a "dual submission". A dual submission is essentially a single set of results presented as two (usually slightly) different papers, which is a trick aimed to increase chances of acceptance. When incentives are high, people are inventive... Some of my work got flagged this way, and incorrectly so. The problem I can see right away is that some reviewers, already busy with many reviews and other tasks, apparently consider this as a convenient excuse to spend less time reviewing and simply taking the flag at face value, which allows them to simply claim dual submission without providing any supporting evidence. Do we really want AI to do reviews for us as well? No, we do not! This is a big danger to the serious researchers in the community; and it is not at all clear to me whether this issue was considered before the system was launched. Do the benefits outweigh the costs? People like me who would never think of a dual submission will be on the losing side. This would not have to happen if the reviewers took their job seriously and evaluated the papers properly. But perhaps this new system will eliminate some of the genuine dual submissions - and I have seen some in the past. What's worse, we are now forced to compare the two papers flagged as potentially dual submission in the rebuttal. This on its own is a great idea - but not delivered correctly because no extra space is given to write the author response. We already have just a single page to respond, which I never found to be enough. Now, there is even less space to respond to the actual review comments - which almost by definition will lead to such papers to be rejected. After all, the reviewer will not get a response to all criticism, and will interpret this in the obvious way. To sum this up: I am not happy with this new system, and the community should not be either.

# Konstantin @ Stanford

Konstantin is visiting Stephen Boyd at Stanford.

# Konstantin @ Frontiers of Deep Learning

Konstantin is attending the Simons Institute (Berkeley) workshop Frontiers of Deep Learning. The schedule and videos of the talks will become available here.

# ICIAM 2019 - Valencia, Spain

I am attending ICIAM 2019 - the largest scientific meeting of industrial and applied mathematicians; taking place once every four years. I am giving a 30 min talk on Wednesday in an invited session on optimization (11am-1pm). I will be leaving Valencia on Saturday.

# Accelerating the grapevine effect

My recent work with Nicolas Loizou on randomized gossip algorithms is featured in the KAUST Discovery magazine. You can read the article online here.

# Martin Takáč giving a talk in Bratislava

Today, my former PhD student Martin Takáč (and now an Assistant Professor at Lehigh University, USA) is giving a popular science talk in Bratislava, Slovakia. The talk is entitled: "Current trends in big data and artificial intelligence". I understand the talk will be delivered in Slovak language.

### July 9, 2019

Filip Hanzely started a research internship at Google, New York. He will be back at KAUST in early October.

# Nature index: KAUST #52 globally and #4 in western Asia

The 2019 Nature index rankings were published. Here is what Nature says about its new "fractional count" rankings, "Our measure, fractional count (FC), is based on the share of articles published in 82 prestigious scientific journals, selected by an independent panel of scientists and tracked by the Nature Index database." The full story can be found here.

In the western Asia region, among academic institutions, and in the "nature & science" area, KAUST was ranked #4. Here is a list of the top 20 institutions:

01.    Weizmann Institute of Science (WIS)
02.    Technion-Israel Institute of Technology (IIT)
03.    Tel Aviv University (TAU)
04.    King Abdullah University of Science and Technology (KAUST)
05.    Hebrew University of Jerusalem (HUJI)
06.    New York University Abu Dhabi (NYUAD)
07.    Sharif University of Technology (SUT)
08.    Ben-Gurion University of the Negev (BGU)
09.    Bar-Ilan University (BIU)
10.    King Saud University (KSU)
11.    Istanbul University
12.    The University of Jordan
13.    E. A. Buketov Karaganda State University (KSU)
14.    University of Haifa (HU)
15.    Nazarbayev University (NU)
16.    S. Toraighyrov Pavlodar State University (PSU)
17.    University of Tehran (UT)
18.    Middle East Technical University (METU)
19.    A. A. Baitursynov Kostanay State University
20.    Koç University (KU)

Globally, also among academic institutions, KAUST ranked #52 in the area "nature & science" (article count)
and #79 in the area "physical sciences" (fractional count).

# 2019 Shanghai rankings

In the 2019 Shanghai rankings, KAUST was ranked 101-150 in Computer Science and Engineering. This is quite some achievement for a university that did not yet exist 10 years ago, and one that currently has about 150 faculty only! We are still growing, and plan to reach full capacity in about 5 years.

Here are notable rankings in some other fields:

25. Energy Science & Engineering
32. Nanoscience & Nanotechnology
33. Materials Science & Engineering
33. Mechanical Engineering
38. Chemical Engineering
50. Telecommunication Engineering
51-75. Chemistry
51-75. Water Resources
101-150. Computer Science & Engineering
101-150. Environmental Science & Engineering
201-300. Earth Sciences
301-400. Mathematics
301-400. Electrical & Electronic Engineering

Overall, KAUST is ranked 201-300 globally. Four years ago, when KAUST was 6 years old, our ranking was 301-400. Five years ago, KAUST was ranked 401-500.

# Promotion to full professor

I have been promoted to full professor.

What does this mean? Some people thought about this quite a bit [1, 2, 3]. In my case, the most immediate and obvious changes are:

i) I now have a 5 year rolling contract at KAUST. That means that each year my contract gets automatically extended by one year (until it does not - which I do not expect will happen - at which point I will have 5 years to find another job).

ii) My KAUST baseline research funding will increase (I do not yet know by how much; but I expect a roughly 40-50% increase). This means I can either grow the group, or do more with the current group. In any case, this is an excellent boost which will have a positive effect one way or another.

iii) My salary will increase.

I will reflect on this in more depth at some point in the future.

# Amazon internship

Samuel has started his research internship in Machine Learning Science group at Amazon, Berlin, Germany.

# Nicolas Loizou: thesis defense

Nicolas Loizou successfully defended his PhD thesis "Randomized iterative methods for linear systems: momentum, inexactness and gossip" today. Congratulations!!! Nicolas is the last student graduating from my Edinburgh group. He will join MILA, Montréal, in the Fall.

# Dmitry, Adil and Elnur @ DS3 2019

Dmitry Kovalev, Adil Salim and Elnur Gasanov are attending the Data Science Summer School (DS3) at École Polytechnique, Paris, France.

# Paper accepted

The paper "New convergence aspects of stochastic gradient algorithms", joint work with Lam M. Nguyen, Phuong Ha Nguyen, Katya Scheinberg, Martin Takáč and Marten van Dijk, was accepted to JMLR.

# Paper accepted

The paper "Randomized projection methods for convex feasibility problems: conditioning and convergence rates", joint work with Ion Necoara and Andrei Patrascu, was accepted to SIAM Journal on Optimization.

# Dmitry @ Summer School in Voronovo

Dmitry Kovalev is attending "Control, Information and Optimization" Summer School in Voronovo, Moscow region, Russia.

Update: Dmitry won the Best Poster Award for his poster describing the paper "Stochastic distributed learning with gradient quantization and variance reduction". Congratulations!!! The paper was co-autored by Samuel Horváth, Dmitry Kovalev, Konstantin Mishchenko, myself and Sebastian Stich.

# Workshop at the Isaac Newton Institute, Cambridge

I am at the Isaac Newton Institute for Mathematical Sciences at the University of Cambridge, attending the workshop "Approximation, Sampling, and Compression in High Dimensional Problems". My talk is on Thursday June 20; I will speak about JacSketch.

# Konstantin @ Bath

Konstantin Mishchenko is visiting Matthias J. Ehrhardt at University of Bath, United Kingdom.

# ICML workshops started

The main ICML conference is over; the workshops start today and continue tomorrow.

# KAUST President @ ICML 2019

KAUST president, Tony Chan, attended ICML yesterday. I have shown him around and we have jointly attended a number of interesting talks and sessions.

# ICML 2019 Talks

We have given three talks today; one by Samuel and two by me. Here are the slides:

Slides for "Nonconvex Variance Reduced Optimization with Arbitrary Sampling" (5 min oral)
Slides for "SGD: General Analysis and Improved Rates" (20 min oral)
Slides for "SAGA with Arbitrary Sampling" (5 min oral)

# ICML 2019

I am in Los Angeles, attending ICML 2019. I am here until June 16; and will attend the workshops as well. Nicolas, Konstantin, Alibek, Samuel, Adil, Aritra, and El Houcine are here, too.

KAUST has a booth at ICML - check out booth #212! We are hiring! We have openings for MS/PhD positions, postdocs, research scientists, assistant professors, associate professor and full professors.

# New intern arrived: Ahmed Ragab from Cairo

Ahmed Khaled Ragab (Cairo University) just arrived to KAUST for a research internship. Welcome!

# ICML 2019 posters

We have prepared posters for our ICML 2019 papers:

"Nonconvex Variance Reduced Optimization with Arbitrary Sampling"
oral talk, Tuesday June 11 @ 11:35-11:40am in Room 104 (schedule)
poster, Tuesday June 11 @ 6:30pm-9:00pm in Pacific Ballroom #95 (schedule)

"SGD: General Analysis and Improved Rates"
20 min oral talk, Tuesday June 11 @ 2:40-3:00pm in Room 103 (schedule)
poster, Tuesday June 11 @ 6:30pm-9:00pm in Pacific Ballroom #195 (schedule)

"SAGA with Arbitrary Sampling"
oral talk, Tuesday June 11 @ 3:15-3:20pm in Room 103 (schedule)
poster, Tuesday June 11 @ 6:30pm-9:00pm in Pacific Ballroom #199 (schedule)

Here are the posters:

# New paper

New paper out: "L-SVRG and L-Katyusha with arbitrary sampling" - joint work with Xun Qian and Zheng Qu.

Abstract: We develop and analyze a new family of nonaccelerated and accelerated loopless variance-reduced methods for finite sum optimization problems. Our convergence analysis relies on a novel expected smoothness condition which upper bounds the variance of the stochastic gradient estimation by a constant times a distance-like function. This allows us to handle with ease arbitrary sampling schemes as well as the nonconvex case. We perform an in-depth estimation of these expected smoothness parameters and propose new importance samplings which allow linear speedup when the expected minibatch size is in a certain range. Furthermore, a connection between these expected smoothness parameters and expected separable overapproximation (ESO) is established, which allows us to exploit data sparsity as well. Our results recover as special cases the recently proposed loopless SVRG and loopless Katyusha methods.

# New paper

New paper out: "MISO is making a comeback with better proofs and rates" - joint work with Xun Qian, Alibek Sailanbayev and Konstantin Mishchenko.

Abstract: MISO, also known as Finito, was one of the first stochastic variance reduced methods discovered, yet its popularity is fairly low. Its initial analysis was significantly limited by the so-called Big Data assumption. Although the assumption was lifted in subsequent work using negative momentum, this introduced a new parameter and required knowledge of strong convexity and smoothness constants, which is rarely possible in practice. We rehabilitate the method by introducing a new variant that needs only smoothness constant and does not have any extra parameters. Furthermore, when removing the strong convexity constant from the stepsize, we present a new analysis of the method, which no longer uses the assumption that every component is strongly convex. This allows us to also obtain so far unknown nonconvex convergence of MISO. To make the proposed method efficient in practice, we derive minibatching bounds with arbitrary uniform sampling that lead to linear speedup when the expected minibatch size is in a certain range. Our numerical experiments show that MISO is a serious competitor to SAGA and SVRG and sometimes outperforms them on real datasets.

# Elnur visiting Grenoble

Elnur Gasanov is visiting Jérôme Malick and his group in Grenoble. He will stat there until the end of June.

Update (June 29): Elnur's visit was extended until until July 19.

# New paper

New paper out: "A stochastic derivative free optimization method with momentum" - joint work with Eduard Gorbunov, Adel Bibi, Ozan Sezer and El Houcine Bergou.

Abstract: We consider the problem of unconstrained minimization of a smooth objective function in R^d in setting where only function evaluations are possible. We propose and analyze stochastic zeroth-order method with heavy ball momentum. In particular, we propose SMTP - a momentum version of the stochastic three-point method (STP) of Bergou et al (2018). We show new complexity results for non-convex, convex and strongly convex functions. We test our method on a collection of learning to continuous control tasks on several MuJoCo environments with varying difficulty and compare against STP, other state-of-the-art derivative-free optimization algorithms and against policy gradient methods. SMTP significantly outperforms STP and all other methods that we considered in our numerical experiments. Our second contribution is SMTP with importance sampling which we call SMTP_IS. We provide convergence analysis of this method for non-convex, convex and strongly convex objectives.

# New paper

New paper out: "On stochastic sign descent methods" - joint work with Mher Safaryan.

Abstract: Various gradient compression schemes have been proposed to mitigate the communication cost in distributed training of large scale machine learning models. Sign-based methods, such as signSGD, have recently been gaining popularity because of their simple compression rule and connection to adaptive gradient methods, like ADAM. In this paper, we perform a general analysis of sign-based methods for non-convex optimization. Our analysis is built on intuitive bounds on success probabilities and does not rely on special noise distributions nor on the boundedness of the variance of stochastic gradients. Extending the theory to distributed setting within a parameter server framework, we assure variance reduction with respect to number of nodes, maintaining 1-bit compression in both directions and using small mini-batch sizes. We validate our theoretical findings experimentally.

# Tong Zhang @ KAUST

Tong Zhang is visiting me at KAUST. He is giving a talk at noon today in the ML Hub Seminar Series.

# New paper

New paper out: "Stochastic proximal Langevin algorithm: potential splitting and nonasymptotic rates" - joint work with Adil Salim and Dmitry Kovalev.

Abstract: We propose a new algorithm---Stochastic Proximal Langevin Algorithm (SPLA)---for sampling from a log concave distribution. Our method is a generalization of the Langevin algorithm to potentials expressed as the sum of one stochastic smooth term and multiple stochastic nonsmooth terms. In each iteration, our splitting technique only requires access to a stochastic gradient of the smooth term and a stochastic proximal operator for each of the nonsmooth terms. We establish nonasymptotic sublinear and linear convergence rates under convexity and strong convexity of the smooth term, respectively, expressed in terms of the KL divergence and Wasserstein distance. We illustrate the efficiency of our sampling technique through numerical simulations on a Bayesian learning task.

# New paper

New paper out: "Direct nonlinear acceleration" - joint work with Aritra Dutta, El Houcine Bergou, Yunming Xiao and Marco Canini.

Abstract: Optimization acceleration techniques such as momentum play a key role in state-of-the-art machine learning algorithms. Recently, generic vector sequence extrapolation techniques, such as regularized nonlinear acceleration (RNA) of Scieur et al., were proposed and shown to accelerate fixed point iterations. In contrast to RNA which computes extrapolation coefficients by (approximately) setting the gradient of the objective function to zero at the extrapolated point, we propose a more direct approach, which we call direct nonlinear acceleration (DNA). In DNA, we aim to minimize (an approximation of) the function value at the extrapolated point instead. We adopt a regularized approach with regularizers designed to prevent the model from entering a region in which the functional approximation is less precise. While the computational cost of DNA is comparable to that of RNA, our direct approach significantly outperforms RNA on both synthetic and real-world datasets. While the focus of this paper is on convex problems, we obtain very encouraging results in accelerating the training of neural networks.

# New paper

New paper out: "A stochastic decoupling method for minimizing the sum of smooth and non-smooth functions" - joint work with Konstantin Mishchenko.

Abstract: We consider the problem of minimizing the sum of three convex functions: i) a smooth function $f$ in the form of an expectation or a finite average, ii) a non-smooth function $g$ in the form of a finite average of proximable functions $g_j$, and iii) a proximable regularizer $R$. We design a variance reduced method which is able progressively  learn the proximal operator of $g$ via the computation of the proximal operator of a single randomly selected function $g_j$ in each iteration only. Our method can provably and efficiently accommodate many strategies for the estimation of the gradient of $f$, including via standard and variance-reduced stochastic estimation, effectively decoupling the smooth part of the problem from the non-smooth part. We prove a number of iteration complexity results, including a general $O(1/t)$ rate, $O(1/t^2)$ rate in the case of strongly convex $f$, and several linear rates in special cases, including accelerated linear rate. For example, our method achieves a linear rate for the problem of minimizing a strongly convex function $f$ under linear constraints under no assumption on the constraints beyond consistency. When combined with SGD or SAGA estimators for the gradient of $f$, this  leads to  a very efficient method for empirical risk minimization with large linear constraints.  Our method generalizes several existing algorithms, including forward-backward splitting, Douglas-Rachford splitting, proximal SGD, proximal SAGA, SDCA, randomized Kaczmarz and Point-SAGA. However, our method leads to many new specific methods in special cases; for instance,  we obtain the first randomized variant of the Dykstra's method for projection onto the intersection of closed convex sets.

# New paper

New paper out: "Revisiting stochastic extragradient" - joint work with Konstantin Mishchenko, Dmitry Kovalev, Egor Shulgin and Yura Malitsky.

Abstract: We consider a new extension of the extragradient method that is motivated by approximating implicit updates. Since in the recent work of Chavdarova et al (2019) it was shown that the existing stochastic extragradient algorithm (called mirror-prox) of Juditsky et al (2011) diverges on a simple bilinear problem, we prove guarantees for solving variational inequality that are more general. Furthermore, we illustrate numerically that the proposed variant converges faster than many other methods on the example of Chavdarova et al (2019). We also discuss how extragradient can be applied to training Generative Adversarial Networks (GANs). Our experiments on GANs demonstrate that the introduced approach may make the training faster in terms of data passes, while its higher iteration complexity makes the advantage smaller. To further accelerate method's convergence on problems such as bilinear minimax, we combine the extragradient step with the negative momentum of Gidel et al (2018) and discuss the optimal momentum value.

# New paper

New paper out: "One method to rule them all: variance reduction for data, parameters and many new methods" - joint work with Filip Hanzely.

Abstract: We propose a remarkably general variance-reduced method suitable for solving regularized empirical risk minimization problems with either a large number of training examples, or a large model dimension, or both. In special cases, our method reduces to several known and previously thought to be unrelated methods, such as SAGA, LSVRG, JacSketch, SEGA and ISEGA, and their arbitrary sampling and proximal generalizations. However, we also highlight a large number of new specific algorithms with interesting properties. We provide a single theorem establishing linear convergence of the method under smoothness and quasi strong convexity assumptions. With this theorem we recover best-known and sometimes improved rates for known methods arising in special cases. As a by-product, we provide the first unified method and theory for stochastic gradient and stochastic coordinate descent type methods.

# New paper

New paper out: "A unified theory of SGD: variance reduction, sampling, quantization and coordinate descent" - joint work with Eduard Gorbunov and Filip Hanzely.

Abstract: In this paper we introduce a unified analysis of a large family of variants of proximal stochastic gradient descent (SGD) which so far have required different intuitions, convergence analyses, have different applications, and which have been developed separately in various communities. We show that our framework includes methods with and without the following tricks, and their combinations: variance reduction, importance sampling, mini-batch sampling, quantization, and coordinate sub-sampling.  As a by-product, we obtain the first unified theory of SGD and randomized coordinate descent (RCD) methods,  the first unified theory of variance reduced and non-variance-reduced SGD methods, and the first unified theory of quantized and non-quantized methods. A key to our approach is a parametric assumption on the iterates and stochastic gradients. In a single theorem we establish a linear convergence result under this assumption and strong-quasi convexity of the loss function. Whenever we recover an existing method as a special case, our theorem gives the best known complexity result. Our approach can be used to motivate the development of new useful methods, and offers pre-proved convergence guarantees. To illustrate the strength of our approach, we develop five new variants of SGD, and through numerical experiments demonstrate some of their properties.

# New paper

New paper out: "Natural compression for distributed deep learning" - joint work with Samuel Horváth, Chen-Yu Ho, Ľudovít Horváth, Atal Narayan Sahu and Marco Canini.

Abstract: Due to their hunger for big data, modern deep learning models are trained in parallel, often in distributed environments, where communication of model updates is the bottleneck. Various update compression (e.g., quantization, sparsification, dithering) techniques have been proposed in recent years as a successful tool to alleviate this problem. In this work, we introduce a new, remarkably simple and theoretically and practically effective compression technique, which we call natural compression (NC). Our technique is applied individually to all entries of the to-be-compressed update vector and works by randomized rounding to the nearest (negative or positive) power of two. NC is "natural" since the nearest power of two of a real expressed as a float can be obtained without any computation, simply by ignoring the mantissa. We show that compared to no compression, NC increases the second moment of the compressed vector by the tiny factor 9/8 only, which means that the effect of NC on the convergence speed of popular training algorithms, such as distributed SGD, is negligible. However, the communications savings enabled by NC are substantial, leading to 3-4x improvement in overall theoretical running time. For applications requiring more aggressive compression, we generalize NC to natural dithering, which we prove is exponentially better than the immensely popular random dithering technique. Our compression operators can be used on their own or in combination with existing operators for a more aggressive combined effect. Finally, we show that NC is particularly effective for the in-network aggregation (INA) framework for distributed training, where the update aggregation is done on a switch, which can only perform integer computations.

# New paper

New paper out: "Randomized Subspace Newton" - joint work with Robert Mansel Gower, Dmitry Kovalev and Felix Lieder.

Abstract: We develop a randomized Newton method capable of solving learning problems with huge dimensional feature spaces, which is a common setting in applications such as medical imaging, genomics and seismology. Our method leverages randomized sketching in a new way, by finding the Newton direction constrained to the space spanned by a random sketch. We develop a simple global linear convergence theory that holds for practically all sketching techniques, which gives the practitioners the freedom to design custom sketching approaches suitable for particular applications. We perform numerical experiments which demonstrate the efficiency of our method as compared to accelerated gradient descent and the full Newton method. Our method can be seen as a refinement and randomized extension of the results of Karimireddy, Stich, and Jaggi (2019).

# New paper

New paper out: "Best pair formulation & accelerated scheme for non-convex principal component pursuit" - joint work with Aritra Dutta, Filip Hanzely and Jingwei Liang.

Abstract: The best pair problem aims to find a pair of points that minimize the distance between two disjoint sets. In this paper, we formulate the classical robust principal component analysis (RPCA) as the best pair; which was not considered before. We design an accelerated proximal gradient scheme to solve it, for which we show global convergence, as well as the local linear rate. Our extensive numerical experiments on both real and synthetic data suggest that the algorithm outperforms relevant baseline algorithms in the literature.

# Filip @ Berkeley

As of today, Filip Hanzely is visiting Michael Mahoney at UC Berkeley. He will stay there until June 18.

# New paper

New paper out: "Revisiting randomized gossip algorithms: general framework, convergence rates and novel block and accelerated protocols" - joint work with Nicolas Loizou.

Abstract: In this work we present a new framework for the analysis and design of randomized gossip algorithms for solving the average consensus problem. We show how classical randomized iterative methods for solving linear systems can be interpreted as gossip algorithms when applied to special systems encoding the underlying network and explain in detail their decentralized nature. Our general framework recovers a comprehensive array of well-known gossip algorithms as special cases, including the pairwise randomized gossip algorithm and path averaging gossip, and allows for the development of provably faster variants. The flexibility of the new approach enables the design of a number of new specific gossip methods. For instance, we propose and analyze novel block and the first provably accelerated randomized gossip protocols, and dual randomized gossip algorithms. From a numerical analysis viewpoint, our work is the first that explores in depth the decentralized nature of randomized iterative methods for linear systems and proposes them as methods for solving the average consensus problem. We evaluate the performance of the proposed gossip protocols by performing extensive experimental testing on typical wireless network topologies.

# Nicolas @ ICASSP 2019

Nicolas Loizou is attending ICASSP 2019 (2019 IEEE International Conference on Acoustics, Speech and Signal Processing) in Brighton, UK, where is presenting the paper "Provably accelerated randomized gossip algorithms", joint work with Michael Rabbat and me.

# Samuel visiting Michael Jordan @ Berkeley

Starting today, Samuel Horváth is visiting Michael I. Jordan at UC Berkeley. He will stay there for a month.

# PhD proposal defense

Filip Hanzely defended his PhD proposal and progressed to PhD candidacy. Congratulations!

# PhD proposal defense

Konstantin Mishchenko defended his PhD proposal and progressed to PhD candidacy. Congratulations!

# Xavier Bresson @ KAUST

I invited Xavier Bresson to KAUST; he arrived yesterday. Today he is giving an ML Hub seminar talk on "Convolutional Neural Networks on Graphs". On April 24 & 25 he will be teaching his Industrial Short Course on Deep Learning and Latest AI Algorithms.

# Four papers accepted to ICML 2019

The long-awaited decisions just came! We've had four papers accepted:

"Nonconvex variance reduced optimization with arbitrary sampling" - joint work with Samuel Horváth.

Abstract: We provide the first importance sampling variants of variance reduced algorithms for empirical risk minimization with non-convex loss functions. In particular, we analyze non-convex versions of SVRG, SAGA and SARAH. Our methods have the capacity to speed up the training process by an order of magnitude compared to the state of the art on real datasets. Moreover, we also improve upon current mini-batch analysis of these methods by proposing importance sampling for minibatches in this setting. Surprisingly, our approach can in some regimes lead to superlinear speedup with respect to the minibatch size, which is not usually present in stochastic optimization. All the above results follow from a general analysis of the methods which works with arbitrary sampling, i.e., fully general randomized strategy for the selection of subsets of examples to be sampled in each iteration. Finally, we also perform a novel importance sampling analysis of SARAH in the convex setting.

"SGD: General analysis and improved rates" - joint work with Robert Mansel Gower, Nicolas Loizou, Xun Qian, Alibek Sailanbayev and Egor Shulgin.

Abstract: We propose a general yet simple theorem describing the convergence of SGD under the arbitrary sampling paradigm. Our theorem describes the convergence of an infinite array of variants of SGD, each of which is associated with a specific probability law governing the data selection rule used to form mini-batches. This is the first time such an analysis is performed, and most of our variants of SGD were never explicitly considered in the literature before. Our analysis relies on the recently introduced notion of expected smoothness and does not rely on a uniform bound on the variance of the stochastic gradients. By specializing our theorem to different mini-batching strategies, such as sampling with replacement and independent sampling, we derive exact expressions for the stepsize as a function of the mini-batch size. With this we can also determine the mini-batch size that optimizes the total complexity, and show explicitly that as the variance of the stochastic gradient evaluated at the minimum grows, so does the optimal mini-batch size. For zero variance, the optimal mini-batch size is one. Moreover, we prove insightful stepsize-switching rules which describe when one should switch from a constant to a decreasing stepsize regime.

"SAGA with arbitrary sampling" - joint work with Xun Qian and Zheng Qu.

Abstract: We study the problem of minimizing the average of a very large number of smooth functions, which is of key importance in training supervised learning models. One of the most celebrated methods in this context is the SAGA algorithm. Despite years of research on the topic, a general-purpose version of SAGA---one that would include arbitrary importance sampling and minibatching schemes---does not exist. We remedy this situation and propose a general and flexible variant of SAGA following the arbitrary sampling paradigm. We perform an iteration complexity analysis of the method, largely possible due to the construction of new stochastic Lyapunov functions. We establish linear convergence rates in the smooth and strongly convex regime, and under a quadratic functional growth condition (i.e., in a regime not assuming strong convexity). Our rates match those of the primal-dual method Quartz for which an arbitrary sampling analysis is available, which makes a significant step towards closing the gap in our understanding of complexity of primal and dual methods for finite sum problems.

"Stochastic gradient push for distributed deep learning" - this is the work of my student Nicolas Loizou, joint with his Facebook coauthors Mahmoud Assran, Nicolas Ballas and Michael Rabbat.

Abstract: Distributed data-parallel algorithms aim to accelerate the training of deep neural networks by parallelizing the computation of large mini-batch gradient updates across multiple nodes. Approaches that synchronize nodes using exact distributed averaging (e.g., via AllReduce) are sensitive to stragglers and communication delays. The PushSum gossip algorithm is robust to these issues, but only performs approximate distributed averaging. This paper studies Stochastic Gradient Push (SGP), which combines PushSum with stochastic gradient updates. We prove that SGP converges to a stationary point of smooth, non-convex objectives at the same sub-linear rate as SGD, that all nodes achieve consensus, and that SGP achieves a linear speedup with respect to the number of compute nodes. Furthermore, we empirically validate the performance of SGP on image classification (ResNet-50, ImageNet) and machine translation (Transformer, WMT'16 En-De) workloads. Our code will be made publicly available.

# Filip @ AISTATS 2019

Today, Filip Hanzely is travelling to Naha, Okinawa, Japan, to attend AISTATS 2019. He will present our paper "Accelerated coordinate descent with arbitrary sampling and best rates for minibatches". Here is the poster for the paper:

# New paper

New paper out: "Stochastic distributed learning with gradient quantization and variance reduction" - joint work with Samuel Horváth, Dmitry Kovalev, Konstantin Mishchenko, and Sebastian Stich.

# Alexey Kroshnin @ KAUST

Alexey Kroshnin arrived at KAUST today and will stay here until the end of April. Alexey's research interests include fundamental theory of optimal transport, geometry of Wasserstein spaces, Wasserstein barycenters, dynamical systems on Wasserstein spaces, probability theory, measure theory, functional analysis and computational complexity theory.

Alexey will work with Konstantin Mishchenko and me on randomized methods for feasibility problems.

# Nicolas Loizou @ KAUST

Nicolas Loizou arrived at KAUST today and will stay here until mid-May. He is finishing writing up his PhD thesis, and plans to defend in the Summer. Once he is done with the thesis, we will work do some work towards NeurIPS 2019. Nicolas got several job offers and chose to join MILA as a postdoc in September 2019.

# New paper

New paper out: "Convergence analysis of inexact randomized iterative methods" - joint work with Nicolas Loizou.

Abstract: In this paper we present a convergence rate analysis of inexact variants of several randomized iterative methods. Among the methods studied are: stochastic gradient descent, stochastic Newton, stochastic proximal point and stochastic subspace ascent. A common feature of these methods is that in their update rule a certain sub-problem needs to be solved exactly. We relax this requirement by allowing for the sub-problem to be solved inexactly. In particular, we propose and analyze inexact randomized iterative methods for solving three closely related problems: a convex stochastic quadratic optimization problem, a best approximation problem and its dual, a concave quadratic maximization problem. We provide iteration complexity results under several assumptions on the inexactness error. Inexact variants of many popular and some more exotic methods, including randomized block Kaczmarz, randomized Gaussian Kaczmarz and randomized block coordinate descent, can be cast as special cases. Numerical experiments demonstrate the benefits of allowing inexactness.

# Dmitry in Moscow

As of today, Dmitry Kovalev is visiting Moscow - he will stay there for two weeks and will give two research talks while there (one in Boris Polyak's group and another at MIPT).

# Zheng Qu @ KAUST

Zheng Qu (The University of Hong Kong) is visiting me at KAUST this week. She will stay for a week, and will give the Machine Learning Hub seminar on Thursday.

# New paper

New paper out: "Scaling distributed machine learning with in-network aggregation" - joint work with Amedeo Sapio, Marco Canini, Chen-Yu Ho, Jacob Nelson, Panos Kalnis, Changhoon Kim, Arvind Krishnamurthy, Masoud Moshref, and Dan R. K. Ports.

Abstract: Training complex machine learning models in parallel is an increasingly important workload. We accelerate distributed parallel training by designing a communication primitive that uses a programmable switch dataplane to execute a key step of the training process. Our approach, SwitchML, reduces the volume of exchanged data by aggregating the model updates from multiple workers in the network. We co-design the switch processing with the end-host protocols and ML frameworks to provide a robust, efficient solution that speeds up training by up to 300%, and at least by 20% for a number of real-world benchmark models.

# Ľubomír Baňas @ KAUST

Ľubomír Baňas (Bielefeld) is arriving today at KAUST for a research visit; he will stay for a week. He will give an AMCS seminar talk on Wednesday.

# Atal joining KAUST as a PhD student

My former intern, Atal Sahu (IIT Kanpur), joined KAUST as an MS student in the group of Marco Canini.

Atal: Welcome back!

# Senior PC Member for IJCAI 2019

I have accepted an invite to serve as a Senior Program Committee Member at the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019). The conference will take place in Macao, China, during August 10-16, 2019. The first IJCAI conference was held in 1969.

# I am in Vienna

I am in Vienna, visiting the .

On February 22 I am teaching a one-day (5 hrs) doctoral course on randomized methods in convex optimization. I offered two possible courses to the students, and they picked (almost unanimously) this one.

During February 25-March 1, I am attending the workshop Numerical Algorithms in Nonsmooth Optimization. My talk is on February 26; I am speaking about the "SEGA" paper (NeurIPS 2018) - joint work with Filip Hanzely and Konstantin Mishchenko. My SEGA slides are here (click on the image to get the pdf file):

# Konstantin @ EPFL

As of today, Konstantin Mishchenko is visiting Martin Jaggi's Machine Learning and Optimization Laboratory at EPFL. He will stay there for a month.

Update (March 17): Konstantin is back at KAUST now.

# New paper

New paper out: "Stochastic three points method for unconstrained smooth minimization" - joint work with El Houcine Bergou and Eduard Gorbunov.

Abstract: In this paper we consider the unconstrained minimization problem of a smooth function in R^n in a setting where only function evaluations are possible. We design a novel randomized direct search method based on stochastic three points (STP) and analyze its complexity. At each iteration, STP generates a random search direction according to a certain fixed probability law. Our assumptions on this law are very mild: roughly speaking, all laws which do not concentrate all measure on any halfspace passing through the origin will work. For instance, we allow for the uniform distribution on the sphere and also distributions that concentrate all measure on a positive spanning set. Given a current iterate x, STP compares the objective function at three points: x, x+αs and x−αs, where α>0 is a stepsize parameter and s is the random search direction. The best of these three points is the next iterate. We analyze the method STP under several stepsize selection schemes (fixed, decreasing, estimated through finite differences, etc). We study non-convex, convex and strongly convex cases.  We also propose a parallel version for STP, with iteration complexity bounds which do not depend on the dimension n.

Comment: The paper was finalized in March 2018; but we only put it online now.

# Internships available in my group

I always have research internships available in my group @ KAUST throughout the year for outstanding and highly motivated students. If you are from Europe, USA, Canada, Australia or New Zealand, you are eligible for the Visiting Student Research Program (VSRP). These internships are a minimum 3 months and a maximum 6 months in duration. We have a different internship program dedicated to applicants from elsewhere. Shorter internships are possible with this program. Drop me an email if you are interested in working with me, explaining why you are interested, attaching your CV and complete transcript of grades.

# Group photo

This is my research group:

People on the photo:

Postdocs: Aritra Dutta, El-Houcine Bergou, Xun Qian

PhD students: Filip Hanzely, Konstantin Mishchenko, Alibek Sailanbayev, Samuel Horváth

MS/PhD students: Elnur Gasanov, Dmitry Kovalev

interns: Eduard Gorbunov, Dmitry Kamzolov, Igor Sokolov, Egor Shulgin, Vladislav Elsukov (all belong to my group at MIPT where I am a visiting professor), Ľudovít Horváth (from Comenius University)

Comment: Nicolas Loizou (Edinburgh) is not on the photo; we will photoshop him in once he comes for a visit in April...

# New paper

New paper out: "A stochastic derivative-free optimization method with importance sampling" - joint work with Adel Bibi, El Houcine Bergou, Ozan Sener and Bernard Ghanem.

Abstract: We consider the problem of unconstrained minimization of a smooth objective function in R^n in a setting where only function evaluations are possible. While importance sampling is one of the most popular techniques used by machine learning practitioners to accelerate the convergence of their models when applicable, there is not much existing theory for this acceleration in the derivative-free setting. In this paper, we propose an importance sampling version of the stochastic three points (STP) method proposed by Bergou et al. and derive new improved complexity results on non-convex, convex and λ-strongly convex functions. We conduct extensive experiments on various synthetic and real LIBSVM datasets confirming our theoretical results. We further test our method on a collection of continuous control tasks on several MuJoCo environments with varying difficulty. Our results suggest that STP is practical for high dimensional continuous control problems. Moreover, the proposed importance sampling version results in a significant sample complexity improvement.

# New paper

New paper out: "99% of parallel optimization is inevitably a waste of time" - joint work with Konstantin Mishchenko and Filip Hanzely.

Abstract: It is well known that many optimization methods, including SGD, SAGA, and Accelerated SGD for over-parameterized models, do not scale linearly in the parallel setting. In this paper, we present a new version of block coordinate descent that solves this issue for a number of methods. The core idea is to make the sampling of coordinate blocks on each parallel unit independent of the others. Surprisingly, we prove that the optimal number of blocks to be updated by each of $n$ units in every iteration is equal to $m/n$, where $m$ is the total number of blocks. As an illustration, this means that when $n=100$ parallel units are used, 99% of work is a waste of time. We demonstrate that with $m/n$ blocks used by each unit the iteration complexity often remains the same. Among other applications which we mention, this fact can be exploited in the setting of distributed optimization to break the communication bottleneck. Our claims are justified by numerical experiments which demonstrate almost a perfect match with our theory on a number of datasets.

# New paper

New paper out: "Distributed learning with compressed gradient differences" - joint work with Konstantin Mishchenko, Eduard Gorbunov and Martin Takáč.

Abstract: Training very large machine learning models requires a distributed computing approach, with communication of the model updates often being the bottleneck. For this reason, several methods based on the compression (e.g., sparsification and/or quantization) of the updates were recently proposed, including QSGD (Alistarh et al., 2017), TernGrad (Wen et al., 2017), SignSGD (Bernstein et al., 2018), and DQGD (Khirirat et al., 2018). However, none of these methods are able to learn the gradients, which means that they necessarily suffer from several issues, such as the inability to converge to the true optimum in the batch mode, inability to work with a nonsmooth regularizer, and slow convergence rates. In this work we propose a new distributed learning method---DIANA---which resolves these issues via compression of gradient differences. We perform a theoretical analysis in the strongly convex and nonconvex settings and show that our rates are vastly superior to existing rates. Our analysis of block quantization and differences between l2 and l∞ quantization closes the gaps in theory and practice. Finally, by applying our analysis technique to TernGrad, we establish the first convergence rate for this method.

# Filip and Aritra @ AAAI 2019 in Hawaii

Filip Hanzely and Aritra Dutta are on their way to AAAI 2019, to be held during Jan 27-Feb 1, 2019 in Honolulu, Hawaii.

# New paper

New paper out: "SGD: general analysis and improved rates" - joint work with Robert Mansel Gower, Nicolas Loizou, Xun Qian, Alibek Sailanbayev and Egor Shulgin.

Abstract: We propose a general yet simple theorem describing the convergence of SGD under the arbitrary sampling paradigm. Our theorem describes the convergence of an infinite array of variants of SGD, each of which is associated with a specific probability law governing the data selection rule used to form minibatches. This is the first time such an analysis is performed, and most of our variants of SGD were never explicitly considered in the literature before. Our analysis relies on the recently introduced notion of expected smoothness and does not rely on a uniform bound on the variance of the stochastic gradients. By specializing our theorem to different mini-batching strategies, such as sampling with replacement and independent sampling, we derive exact expressions for the stepsize as a function of the mini-batch size. With this we can also determine the mini-batch size that optimizes the total complexity, and show explicitly that as the variance of the stochastic gradient evaluated at the minimum grows, so does the optimal mini-batch size. For zero variance, the optimal mini-batch size is one. Moreover, we prove insightful stepsize-switching rules which describe when one should switch from a constant to a decreasing stepsize regime.

# Two new papers

New paper out: "Don’t jump through hoops and remove those loops: SVRG and Katyusha are better without the outer loop" - joint work with Dmitry Kovalev and Samuel Horváth.

Abstract: The stochastic variance-reduced gradient method (SVRG) and its accelerated variant (Katyusha) have attracted enormous attention in the machine learning community in the last few years due to their superior theoretical properties and empirical behaviour on training supervised machine learning models via the empirical risk minimization paradigm. A key structural element in both of these methods is the inclusion of an outer loop at the beginning of which a full pass over the training data is made in order to compute the exact gradient, which is then used to construct a variance-reduced estimator of the gradient. In this work we design loopless variants of both of these methods. In particular, we remove the outer loop and replace its function by a coin flip performed in each iteration designed to trigger, with a small probability, the computation of the gradient. We prove that the new methods enjoy the same superior theoretical convergence properties as the original methods. However, we demonstrate through numerical experiments that our methods have substantially superior practical behavior.

New paper out: "SAGA with arbitrary sampling" - joint work with Xun Qian and Zheng Qu.

Abstract: We study the problem of minimizing the average of a very large number of smooth functions, which is of key importance in training supervised learn- ing models. One of the most celebrated methods in this context is the SAGA algorithm of Defazio et al. (2014). Despite years of research on the topic, a general-purpose version of SAGA—one that would include arbitrary importance sampling and minibatching schemes—does not exist. We remedy this situation and propose a general and flexible variant of SAGA following the arbitrary sampling paradigm. We perform an iteration complexity analysis of the method, largely possible due to the construction of new stochastic Lyapunov functions. We establish linear convergence rates in the smooth and strongly convex regime, and under a quadratic functional growth condition (i.e., in a regime not assuming strong convexity). Our rates match those of the primal-dual method Quartz (Qu et al., 2015) for which an arbitrary sampling analysis is available, which makes a significant step towards closing the gap in our understanding of complexity of primal and dual methods for finite sum problems.

# El Houcine moving on to a new position

El Houcine Bergou's 1 year postdoc contract in my group ended; he now a postdoc in Panos Kalnis' group here at KAUST. I am looking forward to further collaboration with El Houcine and Panos.

### January 14, 2019

ICML deadline is upon us (on Jan 23)... Everyone in my group is working hard towards the deadline.

### January 10, 2019

I've been asked to lead an Aritificial Intelligence Committee at KAUST whose role is to prepare a strategic plan for growing AI research and activities at KAUST over the next 5 years. This will be a substantial investment, and will involve a large number of new faculty, research scientist, postdoc and PhD and MS/PhD positions; investment into computing infrastructure and more. (The committee started its work in 2018; I am positing the news with some delay...)

Independently to this, Bernard Ghanem, Marco Canini, Panos Kalnis and me have established the Machine Learning Hub at KAUST, with the aim to advance ML research and training activities for the benefit of the entire KAUST community. The website is only visible from within the KAUST network at the moment.

# Back at KAUST: people counting

I am back at KAUST. El Houcine, Konstantin and Xun are here. Aritra is on his way to WACV 2019, Hawaii. Samuel and Filip will come back tomorrow. Alibek and Elnur are arriving soon, too.

I will have several interns/research visitors from my group at MIPT visiting me at KAUST during January-February:

- Egor Shulgin (Jan 6 - Feb 21)
- Dmitry Kamzolov (Jan 10 - Feb 18)
- Vladislav Elsukov (Jan 11 - Feb 15)
- Eduard Gorbunov (Jan 13 - Feb 24)
- Igor Sokolov (Jan 18 - Feb 25)

### January 3, 2019

I am visiting Radoslav Harman @ Comenius University, Slovakia.

# Vacation

I am on vacation until the end of the year.

# Paper accepted to AISTATS 2019

The paper "Accelerated coordinate descent with arbitrary sampling and best rates for minibatches", coauthored with Filip Hanzely, was accepted to the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019). The conference will take place in Naha, Okinawa, Japan, during April 16-18, 2019. The acceptance email said: "There were 1,111 submissions for AISTATS this year, of which the program committee accepted 360 for presentation at the conference; among these, 28 papers were accepted for oral presentation, and 332 for poster presentation."

# I will deliver summer school lectures @ ICCOPT 2019

I have accepted an invite to deliver half-a-day worth of summer school lectures on optimization in machine learning at the International Conference on Continuous Optimization (ICCOPT 2019). The Summer School and the main conference take place in Berlin in August 2019. The Summer School precedes the main event, and spans two days: August 3-4. The main conference runs from August 5 until August 8.

ICCOPT is the flagship conference series of the Mathematical Optimization Society (MOS) on continuous optimization, covering a wide range of topics in the field. The individual conferences are typically held once every three years. The last three editions of the conference took place in Tokyo, Japan (2016), Lisbon, Portugal (2013), and Santiago, Chile (2010). I attended all three.

There are two more key conferences in optimization that take place once in three years; each runs in a differenty year, so that one takes place every year. They are: ISMP (International Symposium on Mathematical Programming) and OP (SIAM Conference on Optimization). The last ISMP took place in Bordeaux in Summer 2018. The next OP conference will be in Hong Kong during May 26-29, 2020. I am a member of the organizing committee for OP2020 which is collectively responsible for the selection of invited plenary and tutorial speakers, summer school lecturers, and the organization of mini-symposia.

### December 14, 2018

Alibek and Samuel received their MS degrees today. Congratulations! Both will continue as PhD students in my group as of January 2019.

Earlier today, I had the great pleasure and honor to meet with Kai-Fu Lee (CEO of Sinovation Ventures; former president of Google China; founder & former managing director of Microsoft Research Asia) for a 2hr discussion about AI. I recommend that you watch some of his videos

TED Talk 2018: How AI Can Save Humanity
'AI Superpowers': A Conversation With Kai-Fu Lee
The Future of AI with Kai-Fu Lee: Udacity Talks
The Race for AI: Book Talk with Dr. Kai-Fu Lee

and read his most recent book:

AI Superpowers: China, Silicon Valley and the New World Order

# Konstantin and Filip back from their internships

Konstantin and Filip are back (from Amazon internship / Microsoft Research visit, respectively). They stopped by NeurIPS on their way back.

# Robert Gower @ KAUST

The final exam for CS 390FF course is today. Robert Gower arrived at KAUST for a research visit; he will stay until December 20.

# Back @ KAUST

I am back at KAUST now.

# Attending NeurIPS 2018

I have arrived in Montréal to attend the NeurIPS (formerly known as NIPS) conference. I was welcome with rain, which this is a good thing as far as I am concerned!. Tutorials are starting tomorrow; after that we have three days of the main conference and then two days of workshops. My group is presening three papers accepted to the main conference (paper SEGA, ASBFGS and SSCD) and one paper accepted to a workshop.

I am using the conference Whova app; feel free to get in touch! I am leaving on Thursday evening, so catch me before then... I've posted a few job openings we have at KAUST through the app: internships in my lab (apply by sending me your cv and transcript of university grades), postdoc and research scientist positions (apply by sending a cv + motivation letter), and machine learning faculty positions at all ranks (women and junior applicants are particularly encouraged to apply).

# New paper

New paper out: "New convergence aspects of stochastic gradient algorithms" - joint work with Lam M. Nguyen, Phuong Ha Nguyen, Katya Scheinberg, Martin Takáč, and Marten van Dijk.

Abstract: The classical convergence analysis of SGD is carried out under the assumption that the norm of the stochastic gradient is uniformly bounded. While this might hold for some loss functions, it is violated for cases where the objective function is strongly convex. In Bottou et al. (2016), a new analysis of convergence of SGD is performed under the assumption that stochastic gradients are bounded with respect to the true gradient norm. We show that for stochastic problems arising in machine learning such bound always holds; and we also propose an alternative convergence analysis of SGD with diminishing learning rate regime, which results in more relaxed conditions than those in Bottou et al. (2016). We then move on the asynchronous parallel setting, and prove convergence of Hogwild! algorithm in the same regime in the case of diminished learning rate. It is well-known that SGD converges if a sequence of learning rates {ηt} satisfies t=0ηt and t=0η2t<. We show the convergence of SGD for strongly convex objective function without using bounded gradient assumption when {ηt} is a diminishing sequence and t=0ηt. In other words, we extend the current state-of-the-art class of learning rates satisfying the convergence of SGD.

# Nicolas Loizou looking for jobs

Nicolas Loizou is on the job market; he will get is PhD in 2019. He is looking for research positions in academia (Assistant Prof / postdoc) and industry (Research Scientist). Nicolas will be at NeurIPS next week, presenting his work on privacy-preserving randomized gossip algorithms in the PPML workshop. At the moment, Nicolas is interning at Facebook AI Research (FAIR), where he has done some great work on decentralized training of deep learning models, and on accelerated decentralized gossip communication protocols.

# NeurIPS 2018 posters

Here are the posters of our papers accepted to this year's NeurIPS:

[paper on arXiv]

[paper on arXiv]

[paper on arXiv]

The poster for our Privacy Preserving Machine Learning NeurIPS workshop paper was not finalized yet. I will include a link here once it is ready. Update (November 28): The poster is now ready:

[full-length paper on arXiv]

# New postdoc: Xun Qian

Xun QIAN just joined my group at KAUST as a postdoc. He has a PhD in Mathematics (August 2017) from Hong Kong Baptist University. His PhD thesis is on "Continuous methods for convex programming and convex semidefinite programming" (pdf), supervised by Li-Zhi Liao.

Some of Xun's papers:

H. W. Yue, Li-Zhi Liao, and Xun Qian. Two interior point continuous trajectory models for convex quadratic programming with bound constraints, to appear in Pacific Journal on Optimization

Xun Qian, Li-Zhi Liao, Jie Sun and Hong Zhu. The convergent generalized central paths for linearly constrained convex programming, SIAM Journal on Optimization 28(2):1183-1204, 2018

Xun Qian and Li-Zhi Liao. Analysis of the primal affine scaling continuous trajectory for convex programming, Pacific Journal on Optimization 14(2):261-272, 2018

Xun Qian and Li-Zhi Liao and Jie Sun. Analysis of some interior point continuous trajectories for convex programming, Optimization 66(4):589-608, 2017

# Nicolas visiting MILA

Nicolas Loizou is giving a talk today at Mila, University of Montréal. He is speaking about "Momentum and Stochastic Momentum for Stochastic Gradient, Newton, Proximal Point and Subspace Descent Methods".

# Nicolas visiting McGill

Nicolas Loizou is giving a talk today in the Mathematics in Machine Learning Seminar at McGill University. He is speaking about "Momentum and Stochastic Momentum for Stochastic Gradient, Newton, Proximal Point and Subspace Descent Methods", joint work with me.

# Statistics and Data Science Workshop @ KAUST

Today I am giving a talk at the Statistics and Data Science Workshop held here at KAUST. I am speaking about the JacSketch paper. Here is a YouTube video of the same talk, one I gave in September at the Simons Institute.

# Paper accepted to WACV 2019

The paper "Online and batch incremental video background estimation", joint work with Aritra Dutta, has just been accepted to IEEE Winter Conference on Applications of Computer Vision (WACV 2019). The conference will take place during January 7-January 11, 2019 in Honolulu, Hawaii.

# Back @ KAUST

I am back from annual leave.

# Paper accepted to PPML 2018

The paper "A Privacy Preserving Randomized Gossip Algorithm via Controlled Noise Insertion", joint work with Nicolas Loizou, Filip Hanzely, Jakub Konečný and Dmitry Grishchenko, has been accepted to the NIPS Workshop on Privacy-Preserving Machine Learning (PPML 2018). The full-length paper, which includes a number of additional algorithms and results, can be found on arXiv here.

The acceptance email said: "We received an astonishing number of high quality submissions to the Privacy Preserving Machine Learning workshop and we are delighted to inform you that your submission A Privacy Preserving Randomized Gossip Algorithm via Controlled Noise Insertion (57) was accepted to be presented at the workshop."

# New paper

New paper out: "A stochastic penalty model for convex and nonconvex optimization with big constraints" - joint work with Konstantin Mishchenko.

Abstract: The last decade witnessed a rise in the importance of supervised learning applications involving big data and big models. Big data refers to situations where the amounts of training data available and needed causes difficulties in the training phase of the pipeline. Big model refers to situations where large dimensional and over-parameterized models are needed for the application at hand. Both of these phenomena lead to a dramatic increase in research activity aimed at taming the issues via the design of new sophisticated optimization algorithms. In this paper we turn attention to the big constraints scenario and argue that elaborate machine learning systems of the future will necessarily need to account for a large number of real-world constraints, which will need to be incorporated in the training process. This line of work is largely unexplored, and provides ample opportunities for future work and applications. To handle the big constraints regime, we propose a stochastic penalty formulation which reduces the problem to the well understood big data regime. Our formulation has many interesting properties which relate it to the original problem in various ways, with mathematical guarantees. We give a number of results specialized to nonconvex loss functions, smooth convex functions, strongly convex functions and convex constraints. We show through experiments that our approach can beat competing approaches by several orders of magnitude when a medium accuracy solution is required.

# Aritra and El Houcine @ 2018 INFORMS Annual Meeting

Aritra Dutta and El Houcine Bergou are on their way to Phoenix, Arizona, to give talks at the 2018 INFORMS Annual Meeting.

# New paper

New paper out: "Provably accelerated randomized gossip algorithms" - joint work with Nicolas Loizou and
Michael G. Rabbat.

Abstract: In this work we present novel provably accelerated gossip algorithms for solving the average consensus problem. The proposed protocols are inspired from the recently developed accelerated variants of the randomized Kaczmarz method - a popular method for solving linear systems. In each gossip iteration all nodes of the network update their values but only a pair of them exchange their private information. Numerical experiments on popular wireless sensor networks showing the benefits of our protocols are also presented.

# Paper accepted to AAAI 2019

The paper "A nonconvex projection method for robust PCA", joint work with Aritra Dutta and Filip Hanzely, has been accepted to the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19). The conference will take place during January 27-February 1, 2019, in Honolulu, Hawaii, USA.

The acceptance email said: "We had a record number of over 7,700 submissions this year. Of those, 7,095 were reviewed, and due to space limitations we were only able to accept 1,150 papers, yielding an acceptance rate of 16.2%. There was especially stiff competition this year because of the number of submissions, and you should be proud of your success."

Abstract: Robust principal component analysis (RPCA) is a well-studied problem with the goal of decomposing a matrix into the sum of low-rank and sparse components. In this paper, we propose a nonconvex feasibility reformulation of RPCA problem and apply an alternating projection method to solve it. To the best of our knowledge, we are the first to propose a method that solves RPCA problem without considering any objective function, convex relaxation, or surrogate convex constraints. We demonstrate through extensive numerical experiments on a variety of applications, including shadow removal, background estimation, face detection, and galaxy evolution, that our approach matches and often significantly outperforms current state-of-the-art in various ways.

# Paper accepted to JASA

The paper "A randomized exchange algorithm for computing optimal approximate designs of experiments", joint work with Radoslav Harman and Lenka Filová, has been accepted to Journal of the American Statistical Association (JASA).

Abstract: We propose a class of subspace ascent methods for computing optimal approximate designs that covers both existing as well as new and more efficient algorithms. Within this class of methods, we construct a simple, randomized exchange algorithm (REX). Numerical comparisons suggest that the performance of REX is comparable or superior to the performance of state-of-the-art methods across a broad range of problem structures and sizes. We focus on the most commonly used criterion of D-optimality that also has applications beyond experimental design, such as the construction of the minimum volume ellipsoid containing a given set of data-points. For D-optimality, we prove that the proposed algorithm converges to the optimum. We also provide formulas for the optimal exchange of weights in the case of the criterion of A-optimality. These formulas enable one to use REX for computing A-optimal and I-optimal designs.

# Annual leave

I am about to go on an annual leave to an island in the Indian ocean. I will likely have no functioning internet, and will not be reading my emails (maybe I'll read one or two *if* I get internet over there, but do not expect me to respond as the purpose of annual leave is to relax and recharge). I will be back at KAUST and operational on November 4, teaching at 9am.

# Sebastian Stich @ KAUST

Sebastian Stich is visiting me at KAUST. He will stay here for three weeks, and will give a CS seminar talk on November 12.

# Filip @ MSR

Filip Hanzely is visiting Lin Xiao at Microsoft Research in Redmond, Washington. He will be back roughly in a month. While in the US, he will also drop by Phoenix to give a talk at the 2018 INFORMS Annual Meeting.

# New paper

New paper out: "Stochastic spectral and conjugate descent methods" - joint work with Dmitry Kovalev, Eduard Gorbunov and Elnur Gasanov.

Abstract: The state-of-the-art methods for solving optimization problems in big dimensions are variants of randomized coordinate descent (RCD). In this paper we introduce a fundamentally new type of acceleration strategy for RCD based on the augmentation of the set of coordinate directions by a few spectral or conjugate directions. As we increase the number of extra directions to be sampled from, the rate of the method improves, and interpolates between the linear rate of RCD and a linear rate independent of the condition number. We develop and analyze also inexact variants of these methods where the spectral and conjugate directions are allowed to be approximate only. We motivate the above development by proving several negative results which highlight the limitations of RCD with importance sampling.

# Optimization & Big Data 2018 started

OBD 2018 is starting!
The KAUST Workshop on Optimization and Big Data just started. We have 19 amazing speakers and 21 deluxe e-posters lined up.

Update (February 12): Thanks for all who participated in the workshop, thanks you to this was an excellent event! Group photos:

# Optimization & Big Data 2018

KAUST Research Workshop on Optimization and Big Data is starting tomorrow! We have 19 amazing speakers, and 21 deluxe poster talks and ePoster presentations.

This year, Tamás Terlaky (Lehigh) is the keynote speaker.

Thanks to the KAUST Office for Sponsored Research, The Alan Turing Institute and KICP.

# Nicolas @ KAUST

Nicolas Loizou is back at KAUST on a research visit. Welcome!

# Aritra, Alibek and Samuel @ EPFL

Aritra Dutta (postdoc), Alibek Sailanbayev (MS/PhD student) and Samuel Horvath (MS/PhD student) are attending Applied Machine Learning Days at EPFL, Lausanne, Switzerland.

# Two new MS students and a new intern

Let me welcome Dmitry Kovalev and Elnur Gasanov (master students visiting from MIPT, Moscow) and Slavomír Hanzely (undergraduate student at Comenius University), who arrived at KAUST about a week ago and are working with me as interns. They will be here for about a month.

# New paper

New paper out: "A randomized exchange algorithm for computing optimal approximate designs of experiments" - joint work with Radoslav Harman and Lenka Filová.

Abstract: We propose a class of subspace ascent methods for computing optimal approximate designs that covers both existing as well as new and more efficient algorithms. Within this class of methods, we construct a simple, randomized exchange algorithm (REX). Numerical comparisons suggest that the performance of REX is comparable or superior to the performance of state-of-the-art methods across a broad range of problem structures and sizes. We focus on the most commonly used criterion of D-optimality that also has applications beyond experimental design, such as the construction of the minimum volume ellipsoid containing a given set of datapoints. For D-optimality, we prove that the proposed algorithm converges to the optimum. We also provide formulas for the optimal exchange of weights in the case of the criterion of A-optimality. These formulas enable one to use REX for computing A-optimal and I-optimal designs.

# New intern, visitor and postdoc

I was travelling and am back at KAUST now.

Let me welcome Eduard Gorbunov (a master's student visiting from MIPT, Moscow; will be here until Feb 8), Matthias Ehrhardt (visiting from Cambridge, UK, until February 10) and Elhoucine Bergou (new postdoc in my group, starting today).

# New paper

New paper out: "Randomized projection methods for convex feasibility problems: conditioning and convergence rates" - joint work with Ion Necoara and Andrei Patrascu.

Abstract: Finding a point in the intersection of a collection of closed convex sets, that is the convex feasibility problem, represents the main modeling strategy for many computational problems. In this paper we analyze new stochastic reformulations of the convex feasibility problem in order to facilitate the development of new algorithmic schemes. We also analyze the conditioning problem parameters using certain (linear) regularity assumptions on the individual convex sets. Then, we introduce a general random projection algorithmic framework, which extends to the random settings many existing projection schemes, designed for the general convex feasibility problem. Our general random projection algorithm allows to project simultaneously on several sets, thus providing great flexibility in matching the implementation of the algorithm on the parallel architecture at hand. Based on the conditioning parameters, besides the asymptotic convergence results, we also derive explicit sublinear and linear convergence rates for this general algorithmic framework.

# Old news

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