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Classical optimization techniques have found widespread use in machine learning. Convex optimization has occupied the center-stage and significant effort continues to be still devoted to it. New problems constantly emerge in machine learning, e.g., structured learning and semi-supervised learning, while at the same time fundamental problems such as clustering and classification continue to be better understood. Moreover, machine learning is now very important for real-world problems with massive datasets, streaming inputs, the need for distributed computation, and complex models. These challenging characteristics of modern problems and datasets indicate that we must go beyond the ""traditional optimization"" approaches common in machine learning. What is needed is optimization ""tuned"" for machine learning tasks. For example, techniques such as non-convex optimization (for semi-supervised learning, sparsity constraints), combinatorial optimization and relaxations (structured learning), stochastic optimization (massive datasets), decomposition techniques (parallel and distributed computation), and online learning (streaming inputs) are relevant in this setting. These techniques naturally draw inspiration from other fields, such as operations research, polyhedral combinatorics, theoretical computer science, and the optimization community.
Author Information
Suvrit Sra (MIT)
Suvrit Sra is a faculty member within the EECS department at MIT, where he is also a core faculty member of IDSS, LIDS, MIT-ML Group, as well as the statistics and data science center. His research spans topics in optimization, matrix theory, differential geometry, and probability theory, which he connects with machine learning --- a key focus of his research is on the theme "Optimization for Machine Learning” (http://opt-ml.org)
Sebastian Nowozin (DeepMind)
Vishwanathan S V N (National ICT Australia)
More from the Same Authors
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2022 Poster: CCCP is Frank-Wolfe in disguise »
Alp Yurtsever · Suvrit Sra -
2022 Poster: Efficient Sampling on Riemannian Manifolds via Langevin MCMC »
Xiang Cheng · Jingzhao Zhang · Suvrit Sra -
2021 Poster: Can contrastive learning avoid shortcut solutions? »
Joshua Robinson · Li Sun · Ke Yu · Kayhan Batmanghelich · Stefanie Jegelka · Suvrit Sra -
2021 Poster: Three Operator Splitting with Subgradients, Stochastic Gradients, and Adaptive Learning Rates »
Alp Yurtsever · Alex Gu · Suvrit Sra -
2020 : Invited speaker: SGD without replacement: optimal rate analysis and more, Suvrit Sra »
Suvrit Sra -
2020 Poster: SGD with shuffling: optimal rates without component convexity and large epoch requirements »
Kwangjun Ahn · Chulhee Yun · Suvrit Sra -
2020 Spotlight: SGD with shuffling: optimal rates without component convexity and large epoch requirements »
Kwangjun Ahn · Chulhee Yun · Suvrit Sra -
2020 Poster: Why are Adaptive Methods Good for Attention Models? »
Jingzhao Zhang · Sai Praneeth Karimireddy · Andreas Veit · Seungyeon Kim · Sashank Reddi · Sanjiv Kumar · Suvrit Sra -
2020 Poster: Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes »
Yi Tian · Jian Qian · Suvrit Sra -
2020 Spotlight: Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes »
Yi Tian · Jian Qian · Suvrit Sra -
2019 Poster: Flexible Modeling of Diversity with Strongly Log-Concave Distributions »
Joshua Robinson · Suvrit Sra · Stefanie Jegelka -
2019 Poster: Are deep ResNets provably better than linear predictors? »
Chulhee Yun · Suvrit Sra · Ali Jadbabaie -
2019 Poster: Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity »
Chulhee Yun · Suvrit Sra · Ali Jadbabaie -
2019 Spotlight: Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity »
Chulhee Yun · Suvrit Sra · Ali Jadbabaie -
2018 Workshop: Smooth Games Optimization and Machine Learning »
Simon Lacoste-Julien · Ioannis Mitliagkas · Gauthier Gidel · Vasilis Syrgkanis · Eva Tardos · Leon Bottou · Sebastian Nowozin -
2018 Poster: Direct Runge-Kutta Discretization Achieves Acceleration »
Jingzhao Zhang · Aryan Mokhtari · Suvrit Sra · Ali Jadbabaie -
2018 Spotlight: Direct Runge-Kutta Discretization Achieves Acceleration »
Jingzhao Zhang · Aryan Mokhtari · Suvrit Sra · Ali Jadbabaie -
2018 Poster: Exponentiated Strongly Rayleigh Distributions »
Zelda Mariet · Suvrit Sra · Stefanie Jegelka -
2018 Tutorial: Negative Dependence, Stable Polynomials, and All That »
Suvrit Sra · Stefanie Jegelka -
2017 Workshop: OPT 2017: Optimization for Machine Learning »
Suvrit Sra · Sashank J. Reddi · Alekh Agarwal · Benjamin Recht -
2017 Poster: The Numerics of GANs »
Lars Mescheder · Sebastian Nowozin · Andreas Geiger -
2017 Spotlight: The Numerics of GANs »
Lars Mescheder · Sebastian Nowozin · Andreas Geiger -
2017 Poster: Elementary Symmetric Polynomials for Optimal Experimental Design »
Zelda Mariet · Suvrit Sra -
2017 Poster: Stabilizing Training of Generative Adversarial Networks through Regularization »
Kevin Roth · Aurelien Lucchi · Sebastian Nowozin · Thomas Hofmann -
2017 Poster: Polynomial time algorithms for dual volume sampling »
Chengtao Li · Stefanie Jegelka · Suvrit Sra -
2016 Workshop: OPT 2016: Optimization for Machine Learning »
Suvrit Sra · Francis Bach · Sashank J. Reddi · Niao He -
2016 : Discussion panel »
Ian Goodfellow · Soumith Chintala · Arthur Gretton · Sebastian Nowozin · Aaron Courville · Yann LeCun · Emily Denton -
2016 : Taming non-convexity via geometry »
Suvrit Sra -
2016 : Training Generative Neural Samplers using Variational Divergence »
Sebastian Nowozin -
2016 Poster: Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling »
Chengtao Li · Suvrit Sra · Stefanie Jegelka -
2016 Poster: Kronecker Determinantal Point Processes »
Zelda Mariet · Suvrit Sra -
2016 Poster: f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization »
Sebastian Nowozin · Botond Cseke · Ryota Tomioka -
2016 Poster: Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization »
Sashank J. Reddi · Suvrit Sra · Barnabas Poczos · Alexander Smola -
2016 Poster: Riemannian SVRG: Fast Stochastic Optimization on Riemannian Manifolds »
Hongyi Zhang · Sashank J. Reddi · Suvrit Sra -
2016 Poster: DISCO Nets : DISsimilarity COefficients Networks »
Diane Bouchacourt · Pawan K Mudigonda · Sebastian Nowozin -
2016 Tutorial: Large-Scale Optimization: Beyond Stochastic Gradient Descent and Convexity »
Suvrit Sra · Francis Bach -
2015 Workshop: Optimization for Machine Learning (OPT2015) »
Suvrit Sra · Alekh Agarwal · Leon Bottou · Sashank J. Reddi -
2015 Poster: Matrix Manifold Optimization for Gaussian Mixtures »
Reshad Hosseini · Suvrit Sra -
2015 Poster: On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants »
Sashank J. Reddi · Ahmed Hefny · Suvrit Sra · Barnabas Poczos · Alexander Smola -
2014 Workshop: Discrete Optimization in Machine Learning »
Jeffrey A Bilmes · Andreas Krause · Stefanie Jegelka · S Thomas McCormick · Sebastian Nowozin · Yaron Singer · Dhruv Batra · Volkan Cevher -
2014 Workshop: OPT2014: Optimization for Machine Learning »
Zaid Harchaoui · Suvrit Sra · Alekh Agarwal · Martin Jaggi · Miro Dudik · Aaditya Ramdas · Jean Lasserre · Yoshua Bengio · Amir Beck -
2014 Poster: Efficient Structured Matrix Rank Minimization »
Adams Wei Yu · Wanli Ma · Yaoliang Yu · Jaime Carbonell · Suvrit Sra -
2013 Workshop: OPT2013: Optimization for Machine Learning »
Suvrit Sra · Alekh Agarwal -
2013 Poster: Decision Jungles: Compact and Rich Models for Classification »
Jamie Shotton · Toby Sharp · Pushmeet Kohli · Sebastian Nowozin · John Winn · Antonio Criminisi -
2013 Poster: Geometric optimisation on positive definite matrices for elliptically contoured distributions »
Suvrit Sra · Reshad Hosseini -
2013 Poster: Reflection methods for user-friendly submodular optimization »
Stefanie Jegelka · Francis Bach · Suvrit Sra -
2012 Workshop: Optimization for Machine Learning »
Suvrit Sra · Alekh Agarwal -
2012 Poster: A new metric on the manifold of kernel matrices with application to matrix geometric means »
Suvrit Sra -
2012 Poster: Scalable nonconvex inexact proximal splitting »
Suvrit Sra -
2011 Workshop: Optimization for Machine Learning »
Suvrit Sra · Stephen Wright · Sebastian Nowozin -
2011 Poster: Higher-Order Correlation Clustering for Image Segmentation »
Sungwoong Kim · Sebastian Nowozin · Pushmeet Kohli · Chang D. D Yoo -
2010 Workshop: Numerical Mathematics Challenges in Machine Learning »
Matthias Seeger · Suvrit Sra -
2010 Workshop: Optimization for Machine Learning »
Suvrit Sra · Sebastian Nowozin · Stephen Wright -
2009 Workshop: Optimization for Machine Learning »
Sebastian Nowozin · Suvrit Sra · S.V.N Vishwanthan · Stephen Wright -
2008 Workshop: Structured Input - Structured Output »
Karsten Borgwardt · Koji Tsuda · Vishwanathan S V N · Xifeng Yan -
2007 Spotlight: Bundle Methods for Machine Learning »
Alexander Smola · Vishwanathan S V N · Quoc V Le -
2007 Poster: Bundle Methods for Machine Learning »
Alexander Smola · Vishwanathan S V N · Quoc V Le -
2007 Demonstration: Elefant »
Kishor Gawande · Alexander Smola · Vishwanathan S V N · Li Cheng · Simon A Guenter -
2006 Poster: Fast Computation of Graph Kernels »
Vishwanathan S V N · Karsten Borgwardt · Nic Schraudolph -
2006 Poster: Fast Iterative Kernel PCA »
Nic Schraudolph · Simon Günter · Vishwanathan S V N -
2006 Poster: implicit Online Learning with Kernels »
Li Cheng · Vishwanathan S V N · Dale Schuurmans · Shaojun Wang · Terry Caelli