April 1, 2019

From Edinburgh to KAUST

After having spent 9.5 years at the University of Edinburgh (the last two of which I was on leave), I have decided to move on and pursue new opportunities: I will continue my academic career at KAUST. The city of Edinburgh has been an amazing place to live, and the School of Mathematics and the University of Edinburgh have been an excellent academic home to me. I will miss my many colleagues and friends; I hope to be returning often for visits! Edinburgh is for me the place of many firsts: I have supervised the first MSc thesis, graduated my first PhD student, collaborated with my first postdoc and got my first grant there, among many other academic and non-academic firsts.

I am enthusiastically looking forward to new opportunities and worthy challenges that my new academic life at KAUST will bring. KAUST is a place with the highest ambitions, and with second-to-none infrastructure and support for academic research. I am most lucky to have been able to attract some of the most talented students and postdocs to join my Optimization and Machine Learning Lab at KAUST over the last two years. If you wish to join my group as an intern, MS or PhD student, postdoc or a research scientist, please read this. We have established the Machine Learning Hub at KAUST and with the arrival of the new president Tony Chan, we will soon be launching a substantial AI initiative. We are hiring intensively in Artificial Intelligence and Machine Learning at all levels (Assistant, Associate and Full Professors); please get in touch if interested!

I will no longer be updating this website; my new website is here.

March 19, 2019

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.

March 18, 2019

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).

March 17, 2019

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.

March 9, 2019

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.

March 9, 2019

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.

March 4, 2019

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

Atal: Welcome back!

February 23, 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.

February 20, 2019

I am in Vienna, visiting the Erwin Schrödinger International Institute for Mathematics and Physics (ESI) which is the hosting a program on Modern Maximal Monotone Operator Theory: From Nonsmooth Optimization to Differential Inclusions.

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):

Click to download the


February 18, 2019

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.

February 12, 2019

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.

February 11, 2019

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.

February 8, 2019

This is my research group:

              research group (February 2019)

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...

February 4, 2019

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.

January 27, 2019

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.

January 26, 2019

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.

January 26, 2019

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

January 25, 2019

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.

January 24, 2019

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.

January 15, 2019

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.

January 6, 2019

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.