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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 Gordon McKay Professor of Computer Science and Statistics at Harvard University and a co-director of the recently announced Kempner Institute. He works on the mathematical foundations of machine learning and AI. Sham's thesis helped in laying the statistical foundations of 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; the first sharp analysis of the perturbed gradient descent algorithm, along with the design and analysis of numerous other convex and non-convex algorithms. He is the recipient of the ICML Test of Time Award (2020), the IBM Pat Goldberg best paper award (in 2007), INFORMS Revenue Management and Pricing 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)
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2019 Poster: Maximum Likelihood Estimation for Learning Populations of Parameters »
Wed. Jun 12th 01:30 -- 04:00 AM Room Pacific Ballroom #189
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2021 : A Short Note on the Relationship of Information Gain and Eluder Dimension »
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2023 : Lexinvariant Language Models »
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2023 Poster: One-sided Matrix Completion from Two Observations Per Row »
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2021 : Sparsity in the Partially Controllable LQR »
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2021 Poster: Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed Self-Training »
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2021 Spotlight: Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed Self-Training »
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2021 Poster: How Important is the Train-Validation Split in Meta-Learning? »
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2021 Spotlight: How Important is the Train-Validation Split in Meta-Learning? »
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2021 Poster: Bilinear Classes: A Structural Framework for Provable Generalization in RL »
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2021 Poster: Instabilities of Offline RL with Pre-Trained Neural Representation »
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2021 Spotlight: Instabilities of Offline RL with Pre-Trained Neural Representation »
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2021 Oral: Bilinear Classes: A Structural Framework for Provable Generalization in RL »
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2020 : QA for invited talk 8 Kakade »
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2020 : Invited talk 8 Kakade »
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2020 : Speaker Panel »
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2020 : Exploration, Policy Gradient Methods, and the Deadly Triad - Sham Kakade »
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2020 Poster: Soft Threshold Weight Reparameterization for Learnable Sparsity »
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2020 Poster: Calibration, Entropy Rates, and Memory in Language Models »
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2020 Poster: The Implicit and Explicit Regularization Effects of Dropout »
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2020 Poster: Provable Representation Learning for Imitation Learning via Bi-level Optimization »
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2020 Poster: Sample Amplification: Increasing Dataset Size even when Learning is Impossible »
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2020 Poster: Meta-learning for Mixed Linear Regression »
Weihao Kong · Raghav Somani · Zhao Song · Sham Kakade · Sewoong Oh -
2020 Test Of Time: Test of Time: Gaussian Process Optimization in the Bandit Settings: No Regret and Experimental Design »
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2019 : Keynote by Sham Kakade: Prediction, Learning, and Memory »
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2019 Poster: Online Control with Adversarial Disturbances »
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2019 Oral: Online Control with Adversarial Disturbances »
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2019 Poster: Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data »
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2019 Poster: Equivariant Transformer Networks »
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2019 Poster: Provably Efficient Maximum Entropy Exploration »
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2019 Oral: Provably Efficient Maximum Entropy Exploration »
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2019 Oral: Equivariant Transformer Networks »
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2019 Oral: Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data »
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2019 Poster: Online Meta-Learning »
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2019 Oral: Online Meta-Learning »
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2018 Poster: Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator »
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2018 Oral: Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator »
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2017 Workshop: Principled Approaches to Deep Learning »
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2017 Poster: Estimating the unseen from multiple populations »
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2017 Poster: How to Escape Saddle Points Efficiently »
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