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Author Information
Xiang Cheng (Massachusetts Institute of Technology)
Jingzhao Zhang (Tsinghua University, Tsinghua University)
Suvrit Sra (MIT)
Suvrit Sra is a Research Faculty at the Laboratory for Information and Decision Systems (LIDS) at Massachusetts Institute of Technology (MIT). He obtained his PhD in Computer Science from the University of Texas at Austin in 2007. Before moving to MIT, he was a Senior Research Scientist at the Max Planck Institute for Intelligent Systems, in Tübingen, Germany. He has also held visiting faculty positions at UC Berkeley (EECS) and Carnegie Mellon University (Machine Learning Department) during 2013-2014. His research is dedicated to bridging a number of mathematical areas such as metric geometry, matrix analysis, convex analysis, probability theory, and optimization with machine learning; more broadly, his work involves algorithmically grounded topics within engineering and science. He has been a co-chair for OPT2008-2015, NIPS workshops on "Optimization for Machine Learning," and has also edited a volume of the same name (MIT Press, 2011).
More from the Same Authors
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2022 : Online Policy Optimization for Robust MDP »
Jing Dong · Jingwei Li · Baoxiang Wang · Jingzhao Zhang -
2023 Poster: The Curious Role of Normalization in Sharpness-Aware Minimization »
Yan Dai · Kwangjun Ahn · Suvrit Sra -
2023 Poster: Fast Conditional Mixing of MCMC Algorithms for Non-log-concave Distributions »
Xiang Cheng · Bohan Wang · Jingzhao Zhang · Yusong Zhu -
2023 Poster: Restart Sampling for Improving Generative Processes »
Yilun Xu · Mingyang Deng · Xiang Cheng · Yonglong Tian · Ziming Liu · Tommi Jaakkola -
2023 Poster: On the Overlooked Pitfalls of Weight Decay and How to Mitigate Them: A Gradient-Norm Perspective »
Zeke Xie · Zhiqiang Xu · Jingzhao Zhang · Issei Sato · Masashi Sugiyama -
2023 Poster: Iteratively Learn Diverse Strategies with State Distance Information »
Wei Fu · Weihua Du · Jingwei Li · Sunli Chen · Jingzhao Zhang · YI WU -
2023 Poster: Transformers learn to implement preconditioned gradient descent for in-context learning »
Kwangjun Ahn · Xiang Cheng · Hadi Daneshmand · Suvrit Sra -
2022 Panel: Panel 2C-1: High-dimensional limit theorems… & Efficient Sampling on… »
Xiang Cheng · Reza Gheissari -
2022 : Online Policy Optimization for Robust MDP »
Jing Dong · Jingwei Li · Baoxiang Wang · Jingzhao Zhang -
2022 Poster: CCCP is Frank-Wolfe in disguise »
Alp Yurtsever · 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 -
2017 Poster: Elementary Symmetric Polynomials for Optimal Experimental Design »
Zelda Mariet · Suvrit Sra -
2017 Poster: Polynomial time algorithms for dual volume sampling »
Chengtao Li · Stefanie Jegelka · Suvrit Sra -
2016 : Taming non-convexity via geometry »
Suvrit Sra -
2016 Tutorial: Large-Scale Optimization: Beyond Stochastic Gradient Descent and Convexity »
Suvrit Sra · Francis Bach -
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