Timezone: »
Author Information
Ramya Korlakai Vinayak (University of Washington)
Weihao Kong (Stanford University)
Gregory Valiant (Stanford University)
Sham Kakade (University of Washington)
Sham Kakade is a Washington Research Foundation Data Science Chair, with a joint appointment in the Department of Computer Science and the Department of Statistics at the University of Washington, and is a codirector for the Algorithmic Foundations of Data Science Institute. He works on the mathematical foundations of machine learning and AI. Sham's thesis helped in laying the foundations of the PACMDP framework for reinforcement learning. With his collaborators, his additional contributions include: one of the first provably efficient policy search methods, Conservative Policy Iteration, for reinforcement learning; developing the mathematical foundations for the widely used linear bandit models and the Gaussian process bandit models; the tensor and spectral methodologies for provable estimation of latent variable models (applicable to mixture of Gaussians, HMMs, and LDA); the first sharp analysis of the perturbed gradient descent algorithm, along with the design and analysis of numerous other convex and nonconvex algorithms. He is the recipient of the IBM Goldberg best paper award (in 2007) for contributions to fast nearest neighbor search and the best paper, INFORMS Revenue Management and Pricing Section Prize (2014). He has been program chair for COLT 2011. Sham was an undergraduate at Caltech, where he studied physics and worked under the guidance of John Preskill in quantum computing. He then completed his Ph.D. in computational neuroscience at the Gatsby Unit at University College London, under the supervision of Peter Dayan. He was a postdoc at the Dept. of Computer Science, University of Pennsylvania , where he broadened his studies to include computational game theory and economics from the guidance of Michael Kearns. Sham has been a Principal Research Scientist at Microsoft Research, New England, an associate professor at the Department of Statistics, Wharton, UPenn, and an assistant professor at the Toyota Technological Institute at Chicago.
Related Events (a corresponding poster, oral, or spotlight)

2019 Oral: Maximum Likelihood Estimation for Learning Populations of Parameters »
Tue Jun 11th 07:15  07:20 PM Room Room 103
More from the Same Authors

2020 Poster: The Implicit and Explicit Regularization Effects of Dropout »
Colin Wei · Sham Kakade · Tengyu Ma 
2020 Poster: Estimating the Number and Effect Sizes of Nonnull Hypotheses »
Jennifer Brennan · Ramya Korlakai Vinayak · Kevin Jamieson 
2020 Poster: Sample Amplification: Increasing Dataset Size even when Learning is Impossible »
Brian Axelrod · Shivam Garg · Vatsal Sharan · Gregory Valiant 
2020 Poster: Calibration, Entropy Rates, and Memory in Language Models »
Mark Braverman · Xinyi Chen · Sham Kakade · Karthik Narasimhan · Cyril Zhang · Yi Zhang 
2020 Poster: Metalearning for Mixed Linear Regression »
Weihao Kong · Raghav Somani · Zhao Song · Sham Kakade · Sewoong Oh 
2020 Poster: Soft Threshold Weight Reparameterization for Learnable Sparsity »
Aditya Kusupati · Vivek Ramanujan · Raghav Somani · Mitchell Wortsman · Prateek Jain · Sham Kakade · Ali Farhadi 
2020 Poster: Provable Representation Learning for Imitation Learning via Bilevel Optimization »
Sanjeev Arora · Simon Du · Sham Kakade · Yuping Luo · Nikunj Umesh Saunshi 
2020 Test Of Time: Test of Time: Gaussian Process Optimization in the Bandit Settings: No Regret and Experimental Design »
Niranjan Srinivas · Andreas Krause · Sham Kakade · Matthias W Seeger 
2019 Poster: Online Control with Adversarial Disturbances »
Naman Agarwal · Brian Bullins · Elad Hazan · Sham Kakade · Karan Singh 
2019 Oral: Online Control with Adversarial Disturbances »
Naman Agarwal · Brian Bullins · Elad Hazan · Sham Kakade · Karan Singh 
2019 Poster: Compressed Factorization: Fast and Accurate LowRank Factorization of CompressivelySensed Data »
Vatsal Sharan · Kai Sheng Tai · Peter Bailis · Gregory Valiant 
2019 Poster: Equivariant Transformer Networks »
Kai Sheng Tai · Peter Bailis · Gregory Valiant 
2019 Poster: Provably Efficient Maximum Entropy Exploration »
Elad Hazan · Sham Kakade · Karan Singh · Abby Van Soest 
2019 Oral: Provably Efficient Maximum Entropy Exploration »
Elad Hazan · Sham Kakade · Karan Singh · Abby Van Soest 
2019 Oral: Equivariant Transformer Networks »
Kai Sheng Tai · Peter Bailis · Gregory Valiant 
2019 Oral: Compressed Factorization: Fast and Accurate LowRank Factorization of CompressivelySensed Data »
Vatsal Sharan · Kai Sheng Tai · Peter Bailis · Gregory Valiant 
2019 Poster: Online MetaLearning »
Chelsea Finn · Aravind Rajeswaran · Sham Kakade · Sergey Levine 
2019 Oral: Online MetaLearning »
Chelsea Finn · Aravind Rajeswaran · Sham Kakade · Sergey Levine 
2018 Poster: Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator »
Maryam Fazel · Rong Ge · Sham Kakade · Mehran Mesbahi 
2018 Oral: Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator »
Maryam Fazel · Rong Ge · Sham Kakade · Mehran Mesbahi 
2017 Workshop: Principled Approaches to Deep Learning »
Andrzej Pronobis · Robert Gens · Sham Kakade · Pedro Domingos 
2017 Poster: Estimating the unseen from multiple populations »
Aditi Raghunathan · Greg Valiant · James Zou 
2017 Poster: Orthogonalized ALS: A Theoretically Principled Tensor Decomposition Algorithm for Practical Use »
Vatsal Sharan · Gregory Valiant 
2017 Poster: How to Escape Saddle Points Efficiently »
Chi Jin · Rong Ge · Praneeth Netrapalli · Sham Kakade · Michael Jordan 
2017 Talk: How to Escape Saddle Points Efficiently »
Chi Jin · Rong Ge · Praneeth Netrapalli · Sham Kakade · Michael Jordan 
2017 Talk: Estimating the unseen from multiple populations »
Aditi Raghunathan · Greg Valiant · James Zou 
2017 Talk: Orthogonalized ALS: A Theoretically Principled Tensor Decomposition Algorithm for Practical Use »
Vatsal Sharan · Gregory Valiant