March 10, 2020

Area Chair for NeurIPS 2020

I will serve as an Area Chair for NeurIPS 2020, to be held during December 6-12, 2020 in Vancouver, Canada (same location as last year). For those not in the know, Google Scholar Metrics says that NeurIPS is the #1 conference in AI:

The review process has changed this year; here is a short and beautifully produced video explaining the key 5 changes:

March 9, 2020

Coronavirus at KAUST

No, Covid-19 did not catch up with anyone at KAUST yet. Still in luck. However, as in many places, its increasing omnipresence and gravitational pull is felt here as well.

For example, as of today, all KAUST lectures are moving online. And for a good reason I think: we have seen in Lombardy what the virus can do when unchecked. I am teaching my CS 390T Federated Learning course on Sundays (yes - the work week in Saudi spans Sunday-Thursday) and Tuesdays, and hence my first online lecture will take place on Sunday March 15. I hope, at least, as I need to decide how best to do it.

Conference travel has been limited for some time now, but the rules are even more strict now. This seems less than necessary as conferences drop like flies anyway. My planned travel between now and May includes a seminar talk at EPFL (Switzerland), a workshop keynote lecture at King Faisal University (Al-Ahsa, Saudi Arabia), presentation at ICLR (Addis Ababa, Ethiopia), and SIAM Conference on Optimization (Hong Kong) which I am helping to organize. Most of these events are cancelled, and those that survive will most probably go to sleep soon.

February 27, 2020

New Paper

New paper out: "On Biased Compression for Distributed Learning" - joint work with Aleksandr Beznosikov, Samuel Horváth, and Mher Safaryan.

Abstract: In the last few years, various communication compression techniques have emerged as an indispensable tool helping to alleviate the communication bottleneck in distributed learning. However, despite the fact biased compressors often show superior performance in practice when compared to the much more studied and understood unbiased compressors, very little is known about them. In this work we study three classes of biased compression operators, two of which are new, and their performance when applied to (stochastic) gradient descent and distributed (stochastic) gradient descent. We show for the first time that biased compressors can lead to linear convergence rates both in the single node and distributed settings. Our distributed SGD method enjoys the ergodic rate $O \left( \frac{\delta L \exp(-K) }{\mu} + \frac{(C + D)}{K\mu} \right)$, where $\delta$ is a compression parameter which grows when more compression is applied, $L$ and $\mu$ are the smoothness and strong convexity constants, $C$ captures stochastic gradient noise ($C=0$ if full gradients are computed on each node) and $D$ captures the variance of the gradients at the optimum ($D=0$ for over-parameterized models). Further, via a theoretical study of several synthetic and empirical distributions of communicated gradients, we shed light on why and by how much biased compressors outperform their unbiased variants. Finally, we propose a new highly performing biased compressor---combination of Top-$k$ and natural dithering---which in our experiments outperforms all other compression techniques.

February 26, 2020

New Paper

New paper out: "Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization" - joint work with Zhize Li, Dmitry Kovalev, and Xun Qian.

Abstract: Due to the high communication cost in distributed and federated learning problems, methods relying on compression of communicated messages are becoming increasingly popular. While in other contexts the best performing gradient-type methods invariably rely on some form of acceleration/momentum to reduce the number of iterations, there are no methods which combine the benefits of both gradient compression and acceleration. In this paper, we remedy this situation and propose the first accelerated compressed gradient descent (ACGD) methods.

February 26, 2020

New Paper

New paper out: "Fast Linear Convergence of Randomized BFGS" - joint work with Dmitry Kovalev, Robert M. Gower, and Alexander Rogozin.

Abstract: Since the late 1950’s when quasi-Newton methods first appeared, they have become one of the most widely used and efficient algorithmic paradigms for unconstrained optimization. Despite their immense practical success, there is little theory that shows why these methods are so efficient. We provide a semi-local rate of convergence for the randomized BFGS method which can be significantly better than that of gradient descent, finally giving theoretical evidence supporting the superior empirical performance of the method.

February 21, 2020

New Paper

New paper out: "Stochastic Subspace Cubic Newton Method" - joint work with Filip Hanzely, Nikita Doikov and Yurii Nesterov.

Abstract: In this paper, we propose a new randomized second-order optimization algorithm---Stochastic Subspace Cubic Newton (SSCN)---for minimizing a high dimensional convex function $f$. Our method can be seen both as astochastic extension of the cubically-regularized Newton method of Nesterov and Polyak (2006), and a second-order enhancement of stochastic subspace descent of Kozak et al. (2019). We prove that as we vary the minibatch size, the global convergence rate of SSCN interpolates between the rate of stochastic coordinate descent (CD) and the rate of cubic regularized Newton, thus giving new insights into the connection between first and second-order methods. Remarkably, the local convergence rate of SSCN matches the rate of stochastic subspace descent applied to the problem of minimizing the quadratic function $\frac{1}{2} (x-x^*)^\top \nabla^2 f(x^*)(x-x^*)$, where $x^*$ is the minimizer of $f$, and hence depends on the properties of $f$ at the optimum only. Our numerical experiments show that SSCN outperforms non-accelerated first-order CD algorithms while being competitive to their accelerated variants.

February 20, 2020

New MS/PhD Student: Egor Shulgin

Egor Vladimirovich Shulgin is back at KAUST - now as an MS/PhD student. Welcome!!!

Egor has co-authored 4 papers and a book (in Russian) entitled "Lecture Notes on Stochastic Processes". Here are the papers, in reverse chronological order:

- Uncertainty principle for communication compression in distributed and federated learning and the search for an optimal compressor
- Adaptive catalyst for smooth convex optimization
- Revisiting stochastic extragradient (AISTATS 2020)
- SGD: general analysis and improved rates (ICML 2019)

Egor has a bachelor degree in Applied Mathematics from the Department of Control and Applied Mathematics at MIPT, Dolgoprudny, Russia. He majored in Data Analysis. His CV mentions the following as his main subjects: Probability Theory, Random Processes, Convex Optimization, and Machine Learning.

Egor’s hobbies, according to his CV, are: hiking, alpine skiing, tennis, and judo. Notably, this list does not include table tennis. However, I know for a fact that he is very good in it!

February 20, 2020

New Paper

New paper out: "Uncertainty Principle for Communication Compression in Distributed and Federated Learning and the Search for an Optimal Compressor" - joint work with Mher Safaryan and Egor Shulgin.

Abstract: In order to mitigate the high communication cost in distributed and federated learning, various vector compression schemes, such as quantization, sparsification and dithering, have become very popular. In designing a compression method, one aims to communicate as few bits as possible, which minimizes the cost per communication round, while at the same time attempting to impart as little distortion (variance) to the communicated messages as possible, which minimizes the adverse effect of the compression on the overall number of communication rounds. However, intuitively, these two goals are fundamentally in conflict: the more compression we allow, the more distorted the messages become. We formalize this intuition and prove an {\em uncertainty principle} for randomized compression operators, thus quantifying this limitation mathematically, and {\em effectively providing lower bounds on what might be achievable with communication compression}. Motivated by these developments, we call for the search for the optimal compression operator. In an attempt to take a first step in this direction, we construct a new unbiased compression method inspired by the Kashin representation of vectors, which we call {\em Kashin compression (KC)}. In contrast to all previously proposed compression mechanisms, we prove that KC enjoys a {\em dimension independent} variance bound with an explicit formula even in the regime when only a few bits need to be communicate per each vector entry. We show how KC can be provably and efficiently combined with several existing optimization algorithms, in all cases leading to communication complexity improvements on previous state of the art.

February 14, 2020

New Paper

New paper out: "Federated Learning of a Mixture of Global and Local Models" - joint work with Filip Hanzely.

Abstract: We propose a new optimization formulation for training federated learning models. The standard formulation has the form of an empirical risk minimization problem constructed to find a single global model trained from the private data stored across all participating devices. In contrast, our formulation seeks an explicit trade-off between this traditional global model and the local models, which can be learned by each device from its own private data without any communication. Further, we develop several efficient variants of SGD (with and without partial participation and with and without variance reduction) for solving the new formulation and prove communication complexity guarantees. Notably, our methods are similar but not identical to federated averaging / local SGD, thus shedding some light on the essence of the elusive method. In particular, our methods do not perform full averaging steps and instead merely take steps towards averaging. We argue for the benefits of this new paradigm for federated learning.

February 12, 2020

New Paper

New paper out: "Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization" - joint work with Samuel Horváth, Lihua Lei and Michael I. Jordan.

Abstract: Adaptivity is an important yet under-studied property in modern optimization theory. The gap between the state-of-the-art theory and the current practice is striking in that algorithms with desirable theoretical guarantees typically involve drastically different settings of hyperparameters, such as step-size schemes and batch sizes, in different regimes. Despite the appealing theoretical results, such divisive strategies provide little, if any, insight to practitioners to select algorithms that work broadly without tweaking the hyperparameters. In this work, blending the "geometrization" technique introduced by Lei & Jordan 2016 and the SARAH algorithm of Nguyen et al., 2017, we propose the Geometrized SARAH algorithm for non-convex finite-sum and stochastic optimization. Our algorithm is proved to achieve adaptivity to both the magnitude of the target accuracy and the Polyak-Łojasiewicz (PL) constant if present. In addition, it achieves the best-available convergence rate for non-PL objectives simultaneously while outperforming existing algorithms for PL objectives.

February 11, 2020

New Paper

New paper out: "Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems" - joint work with Filip Hanzely and Dmitry Kovalev.

Abstract: We propose an accelerated version of stochastic variance reduced coordinate descent -- ASVRCD. As other variance reduced coordinate descent methods such as SEGA or SVRCD, our method can deal with problems that include a non-separable and non-smooth regularizer, while accessing a random block of partial derivatives in each iteration only. However, ASVRCD incorporates Nesterov's momentum, which offers favorable iteration complexity guarantees over both SEGA and SVRCD. As a by-product of our theory, we show that a variant of Allen-Zhu (2017) is a specific case of ASVRCD, recovering the optimal oracle complexity for the finite sum objective.

February 10, 2020

Konstantin Giving a Series of Talks in the US and UK

Konstantin Mishchenko is giving several talks in New York, London and Oxford. Here is his schedule:

February 10, Facebook Research, New York, "Adaptive Gradient Descent Without Descent"

February 12, Deepmind, London, "Adaptive Gradient Descent Without Descent"

February 12, UCL Gatsby Computational Neuroscience Unit, London, "Sinkhorn Algorithm as a Special Case of Stochastic Mirror Descent"

February 14, Oxford University, Oxford, "Adaptive Gradient Descent Without Descent"

February 14, Imperial College London, London, "Sinkhorn Algorithm as a Special Case of Stochastic Mirror Descent"

February 9, 2020

New Paper

New paper out: "Better Theory for SGD in the Nonconvex World" - joint work with Ahmed Khaled.

Abstract: Large-scale nonconvex optimization problems are ubiquitous in modern machine learning, and among practitioners interested in solving them, Stochastic Gradient Descent (SGD) reigns supreme. We revisit the analysis of SGD in the nonconvex setting and propose a new variant of the recently introduced expected smoothness assumption which governs the behaviour of the second moment of the stochastic gradient. We show that our assumption is both more general and more reasonable than assumptions made in all prior work. Moreover, our results yield the optimal $O(\epsilon^{-4})$ rate for finding a stationary point of nonconvex smooth functions, and recover the optimal $O(\epsilon^{-1})$ rate for finding a global solution if the Polyak-Łojasiewicz condition is satisfied. We compare against convergence rates under convexity and prove a theorem on the convergence of SGD under Quadratic Functional Growth and convexity, which might be of independent interest. Moreover, we perform our analysis in a framework which allows for a detailed study of the effects of a wide array of sampling strategies and minibatch sizes for finite-sum optimization problems. We corroborate our theoretical results with experiments on real and synthetic data.

February 8, 2020

Interview by for their Leaders in AI Platform

In December last year, while attending NeurIPS in Vancouver, I was interviewed by The video can be found here

and a podcast out of this is on soundcloud:

I am in excellent company:

Yoshua Bengio
Kai-Fu Lee
Max Welling
Christopher Manning

February 8, 2020

AAAI in New York

Konstantin is about to receive a Best Reviewer Award at AAAI 20 in New York. Adel is presenting our paper "A stochastic derivative-free optimization method with importance sampling", joint work with El Houcine Bergou, Ozan Sener, Bernard Ghanem and myself, at the event.

February 6, 2020

ICML Deadline Today!

I am a walking zombie, a being without a soul, a sleepless creature of the night. Do not approach me or you will meet your destiny. Wait three days and I shall be alive again.

February 3, 2020

Samuel in San Diego

Samuel Horváth is in San Diego, and will soon be presenting the paper "Don’t jump through hoops and remove those loops: SVRG and Katyusha are better without the outer loop", joint work with Dmitry Kovalev and myself, at ALT 2020 in San Diego.

Here is the list of all accepted papers.

Samuel will be back at KAUST in about 10 days.

February 2, 2020

Eduard Gorbunov is Visting Again

Eduard Gorbunov came for a research visit - this is his third time at KAUST. This time, he wil stay for about 2 months.

January 31, 2020

Poster for AAAI-20

Our paper "A stochastic derivative-free optimization method with importance sampling", joint work with Adel Bibi, El Houcine Bergou, Ozan Sener, and Bernard Ghanem, will be presented at AAAI 2020, which will be held in New York during February 7-12.

We have just prepared a poster, here it is:


January 20, 2019

Paper Accepted to SIMAX

The paper "Stochastic reformulations of linear systems: algorithms and convergence theory", joint work with Martin Takáč, was accepted to SIAM Journal on Matrix Analysis and Applications.

January 18, 2020

New Intern: Alexander Rogozin

A new intern arrived today: Alexander Rogozin. Alexander is a final-year MSc student in the department of Control and Applied Mathematics at the Moscow Institute of Physics and Technology (MIPT).

Some notable achievements of Alexander so far:

- Co-authored 3 papers in the area of decentralized optimization over time varying networks
- His GPA ranks him among the top 5% students at MIPT
- Tutor of Probability Theory at MIPT, 2018-now
- Finished the Yandex School for Data Analysis (2017-2019)
- Awardee of the Russian National Physics Olympiad, 2013
- Certificate of Honor at Russian National Mathematics Olympiad, 2012
- Winner of Russian National Physics Olympiad, 2012

In 2018, Alexander participated in the Moscow Half-Marathon. He is a holder of 4-kyu in Judo. Having studied the piano for 11 years, Alexander participated in city, regional, national and international musical festivals and competitions. He performed with a symphony orchestra as a piano soloist at festivals in his hometown.


January 16, 2020

Konstantin Among the Best Reviewers for AAAI-20

Congratulations Kostya! (AAAI Conference on Artificial Intelligence is one of the top AI conferences. The email below tells the story.)

Dear Konstantin,

On behalf of the Association for the Advancement of Artificial Intelligence and the AAAI-20 Program Committee, we are pleased to inform you that you have been selected as one of 12 Outstanding Program Committee members for 2020 in recognition of your outstanding service on this year's committee. Your efforts were characterized by exceptional care, thoroughness, and thoughtfulness in the reviews and discussions of the papers assigned to you.

In recognition of your achievement, you will be presented with a certificate by the AAAI-20 Program Cochairs, Vincent Conitzer and Fei Sha, during the AAAI-20 Award Ceremony on Tuesday, February 11 at 8:00am. There will also be an announcement of this honor in the program. Please let us know ( if you will be present at the award ceremony to accept your award.

Congratulations, and we look forward to seeing you in New York for AAAI-20, February 7-12.

Warmest regards,

Carol McKenna Hamilton
Executive Director, AAAI


Vincent Conitzer and Fei Sha
AAAI-20 Program Cochairs

January 13, 2020

Konstantin Visiting Francis Bach's Group at INRIA

Konstantin Mishchenko is visiting the SIERRA machine learning lab at INRIA, Paris, led by Francis Bach. He will give a talk on January 14 entitled "Adaptive Gradient Descent Without Descent" and based on this paper.

January 9, 2020

New Intern: Aleksandr Beznosikov

A new intern arrived today: Aleksandr Beznosikov. Aleksandr is a final-year BSc student in Applied Mathematics and Physics at Moscow Institute of Physics and Technology (MIPT).

Some notable achievements of Aleksandr so far:

- paper "Derivative-Free Method For Decentralized Distributed Non-Smooth Optimization", joint work with Eduard Gorbunov and Alexander Gasnikov
- Increased State Academic Scholarship for 4 year bachelor and master students at MIPT, 2018-2019
- Author of problems and organizer of the student olympiad in discrete mathematics, 2018-2019
- Abramov's Scholarship for students with the best grades at MIPT, 2017-2019
- First Prize at MIPT's team mathematical tournament, 2017
- Silver Medal at International Experimental Physics Olympiad, 2015
- Russian President’s Scholarship for High School Students, 2014-2015
- Prize-Winner, All-Russian School Physics Olympiad, Final Round, 2014 and 2015
- Winner, All-Russian School Programming Olympiad, Region Round, 2015-2016
- Winner, All-Russian School Physics Olympiad, Region Round, 2014-2016
- Winner, All-Russian School Maths Olympiad, Region Round, 2014-2016


January 7, 2020

Four Papers Accepted to AISTATS 2020

Some of the first good news of 2020: We've had four papers accepted to The 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020), which will be held during June 3-5, 2020, in Palermo, Sicily, Italy. Here they are:

1) "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.

2) "Tighter theory for local SGD on identical and heterogeneous data" - joint work with Ahmed Khaled and Konstantin Mishchenko.

Abstract: We provide a new analysis of local SGD, removing unnecessary assumptions and elaborating on the difference between two data regimes: identical and heterogeneous. In both cases, we improve the existing theory and provide values of the optimal stepsize and optimal number of local iterations. Our bounds are based on a new notion of variance that is specific to local SGD methods with different data. The tightness of our results is guaranteed by recovering known statements when we plug $H=1$, where $H$ is the number of local steps. The empirical evidence further validates the severe impact of data  heterogeneity on the  performance of local SGD.

3) "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 a 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 than in Juditsky et al (2011). 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 negative momentum Gidel et al (2018) and discuss the optimal momentum value.

4) "DAve-QN: A distributed averaged quasi-Newton method with local superlinear convergence rate" - work of S. Soori, K. Mishchenko, A. Mokhtari, M. Dehnavi, and M. Gürbüzbalaban.

Abstract: In this paper, we consider distributed algorithms for solving the empirical risk minimization problem under the master/worker communication model. We develop a distributed asynchronous quasi-Newton algorithm that can achieve superlinear convergence. To our knowledge, this is the first distributed asynchronous algorithm with superlinear convergence guarantees. Our algorithm is communication-efficient in the sense that at every iteration the master node and workers communicate vectors of size O(p), where p is the dimension of the decision variable. The proposed method is based on a distributed asynchronous averaging scheme of decision vectors and gradients in a way to effectively capture the local Hessian information of the objective function. Our convergence theory supports asynchronous computations subject to both bounded delays and unbounded delays with a bounded time-average. Unlike in the majority of asynchronous optimization literature, we do not require choosing smaller stepsize when delays are huge. We provide numerical experiments that match our theoretical results and showcase significant improvement comparing to state-of-the-art distributed algorithms.

January 7, 2020

Visiting ESET in Bratislava

I am on a visit to ESET - a leading internet security company headquartered in Bratislava, Slovakia. I have given a couple of talks on stochastic gradient descent and have spoken to several very interesting people.

January 5, 2020

Filip Visiting Francis Bach's Group at INRIA

Filip Hanzely is visiting the SIERRA machine learning lab at INRIA, Paris, led by Francis Bach. He will give a talk on January 7 entitled "One method to rule them all: variance reduction for data, parameters and many new methods", and based on a paper of the same title. Here are his slides.

January 5, 2020

New Intern: Grigory Malinovsky

A new intern arrived today: Grigory Malinovsky from Moscow Institute of Physics and Technology (MIPT). Grigory wrote his BS thesis "Averaged Heavy Ball Method" under the supervision of Boris Polyak. He is now pursuing an MS degree at MIPT in Machine Learning.

Among Grigory's successes belong:

- Abramov's cholarship for tudents with the best grades at MIPT, 2016
- Participant in the final round of All-Russian Physics Olympiad, 2014
- Bronze medal at International Zhautykov Olympiad in Physics, 2014
- Prize winner in the final round of All-Russian Physics Olympiad, 2013


Old News

Read old news (2017 and earlier)