Sham M. Kakade

Publication Activity (10 Years)

Years Active: 1999-2024
Publications (10 Years): 200

Top Venues

CoRR

NeurIPS

ICML

ICLR

Publications

Motoya Ohnishi, Isao Ishikawa, Kendall Lowrey, Masahiro Ikeda, Sham M. Kakade, Yoshinobu Kawahara
Koopman Spectrum Nonlinear Regulators and Efficient Online Learning. Trans. Mach. Learn. Res. 2024 (2024)
Kaiying Hou, David Brandfonbrener, Sham M. Kakade, Samy Jelassi, Eran Malach
Universal Length Generalization with Turing Programs. CoRR (2024)
Kenneth Li, Samy Jelassi, Hugh Zhang, Sham M. Kakade, Martin Wattenberg, David Brandfonbrener
Q-Probe: A Lightweight Approach to Reward Maximization for Language Models. CoRR (2024)
Jeffrey Li, Alex Fang, Georgios Smyrnis, Maor Ivgi, Matt Jordan, Samir Yitzhak Gadre, Hritik Bansal, Etash Guha, Sedrick Keh, Kushal Arora, Saurabh Garg, Rui Xin, Niklas Muennighoff, Reinhard Heckel, Jean Mercat, Mayee Chen, Suchin Gururangan, Mitchell Wortsman, Alon Albalak, Yonatan Bitton, Marianna Nezhurina, Amro Abbas, Cheng-Yu Hsieh, Dhruba Ghosh, Josh Gardner, Maciej Kilian, Hanlin Zhang, Rulin Shao, Sarah M. Pratt, Sunny Sanyal, Gabriel Ilharco, Giannis Daras, Kalyani Marathe, Aaron Gokaslan, Jieyu Zhang, Khyathi Raghavi Chandu, Thao Nguyen, Igor Vasiljevic, Sham M. Kakade, Shuran Song, Sujay Sanghavi, Fartash Faghri, Sewoong Oh, Luke Zettlemoyer, Kyle Lo, Alaaeldin El-Nouby, Hadi Pouransari, Alexander Toshev, Stephanie Wang, Dirk Groeneveld, Luca Soldaini, Pang Wei Koh, Jenia Jitsev, Thomas Kollar, Alexandros G. Dimakis, Yair Carmon, Achal Dave, Ludwig Schmidt, Vaishaal Shankar
DataComp-LM: In search of the next generation of training sets for language models. CoRR (2024)
Ethan Shen, Alan Fan, Sarah M. Pratt, Jae Sung Park, Matthew Wallingford, Sham M. Kakade, Ari Holtzman, Ranjay Krishna, Ali Farhadi, Aditya Kusupati
Superposed Decoding: Multiple Generations from a Single Autoregressive Inference Pass. CoRR (2024)
Zhenting Qi, Hanlin Zhang, Eric P. Xing, Sham M. Kakade, Himabindu Lakkaraju
Follow My Instruction and Spill the Beans: Scalable Data Extraction from Retrieval-Augmented Generation Systems. CoRR (2024)
David Brandfonbrener, Hanlin Zhang, Andreas Kirsch, Jonathan Richard Schwarz, Sham M. Kakade
CoLoR-Filter: Conditional Loss Reduction Filtering for Targeted Language Model Pre-training. CoRR (2024)
Yiwen Kou, Zixiang Chen, Quanquan Gu, Sham M. Kakade
Matching the Statistical Query Lower Bound for k-sparse Parity Problems with Stochastic Gradient Descent. CoRR (2024)
Depen Morwani, Itai Shapira, Nikhil Vyas, Eran Malach, Sham M. Kakade, Lucas Janson
A New Perspective on Shampoo's Preconditioner. CoRR (2024)
Edwin Zhang, Vincent Zhu, Naomi Saphra, Anat Kleiman, Benjamin L. Edelman, Milind Tambe, Sham M. Kakade, Eran Malach
Transcendence: Generative Models Can Outperform The Experts That Train Them. CoRR (2024)
Hanlin Zhang, Yifan Zhang, Yaodong Yu, Dhruv Madeka, Dean Foster, Eric P. Xing, Himabindu Lakkaraju, Sham M. Kakade
A Study on the Calibration of In-context Learning. NAACL-HLT (2024)
Rosie Zhao, Depen Morwani, David Brandfonbrener, Nikhil Vyas, Sham M. Kakade
Deconstructing What Makes a Good Optimizer for Language Models. CoRR (2024)
Licong Lin, Jingfeng Wu, Sham M. Kakade, Peter L. Bartlett, Jason D. Lee
Scaling Laws in Linear Regression: Compute, Parameters, and Data. CoRR (2024)
Samy Jelassi, David Brandfonbrener, Sham M. Kakade, Eran Malach
Repeat After Me: Transformers are Better than State Space Models at Copying. CoRR (2024)
Depen Morwani, Benjamin L. Edelman, Costin-Andrei Oncescu, Rosie Zhao, Sham M. Kakade
Feature emergence via margin maximization: case studies in algebraic tasks. ICLR (2024)
Ziqi Wang, Hanlin Zhang, Xiner Li, Kuan-Hao Huang, Chi Han, Shuiwang Ji, Sham M. Kakade, Hao Peng, Heng Ji
Eliminating Position Bias of Language Models: A Mechanistic Approach. CoRR (2024)
Benjamin L. Edelman, Surbhi Goel, Sham M. Kakade, Eran Malach, Cyril Zhang
Pareto Frontiers in Deep Feature Learning: Data, Compute, Width, and Luck. NeurIPS (2023)
Dylan J. Foster, Noah Golowich, Sham M. Kakade
Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games. ICML (2023)
Nikhil Vyas, Sham M. Kakade, Boaz Barak
On Provable Copyright Protection for Generative Models. ICML (2023)
Dylan J. Foster, Noah Golowich, Sham M. Kakade
Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games. CoRR (2023)
Nikhil Vyas, Depen Morwani, Rosie Zhao, Gal Kaplun, Sham M. Kakade, Boaz Barak
Beyond Implicit Bias: The Insignificance of SGD Noise in Online Learning. CoRR (2023)
Gaurav Mahajan, Sham M. Kakade, Akshay Krishnamurthy, Cyril Zhang
Learning Hidden Markov Models Using Conditional Samples. COLT (2023)
Aniket Rege, Aditya Kusupati, Sharan Ranjit S, Alan Fan, Qingqing Cao, Sham M. Kakade, Prateek Jain, Ali Farhadi
AdANNS: A Framework for Adaptive Semantic Search. NeurIPS (2023)
Sham M. Kakade, Akshay Krishnamurthy, Gaurav Mahajan, Cyril Zhang
Learning Hidden Markov Models Using Conditional Samples. CoRR (2023)
Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
Learning High-Dimensional Single-Neuron ReLU Networks with Finite Samples. CoRR (2023)
Depen Morwani, Benjamin L. Edelman, Costin-Andrei Oncescu, Rosie Zhao, Sham M. Kakade
Feature emergence via margin maximization: case studies in algebraic tasks. CoRR (2023)
Krishna Pillutla, Vincent Roulet, Sham M. Kakade, Zaïd Harchaoui
Modified Gauss-Newton Algorithms under Noise. CoRR (2023)
Benjamin L. Edelman, Surbhi Goel, Sham M. Kakade, Eran Malach, Cyril Zhang
Pareto Frontiers in Neural Feature Learning: Data, Compute, Width, and Luck. CoRR (2023)
Krishna Pillutla, Vincent Roulet, Sham M. Kakade, Zaïd Harchaoui
Modified Gauss-Newton Algorithms under Noise. SSP (2023)
Sohrab Andaz, Carson Eisenach, Dhruv Madeka, Kari Torkkola, Randy Jia, Dean P. Foster, Sham M. Kakade
Learning an Inventory Control Policy with General Inventory Arrival Dynamics. CoRR (2023)
Hanlin Zhang, Yi-Fan Zhang, Yaodong Yu, Dhruv Madeka, Dean Foster, Eric P. Xing, Hima Lakkaraju, Sham M. Kakade
A Study on the Calibration of In-context Learning. CoRR (2023)
Jens Tuyls, Dhruv Madeka, Kari Torkkola, Dean P. Foster, Karthik Narasimhan, Sham M. Kakade
Scaling Laws for Imitation Learning in NetHack. CoRR (2023)
Aniket Rege, Aditya Kusupati, Sharan Ranjit S, Alan Fan, Qingqing Cao, Sham M. Kakade, Prateek Jain, Ali Farhadi
AdANNS: A Framework for Adaptive Semantic Search. CoRR (2023)
Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron. ICML (2023)
Nikhil Vyas, Sham M. Kakade, Boaz Barak
Provable Copyright Protection for Generative Models. CoRR (2023)
Tengyang Xie, Dylan J. Foster, Yu Bai, Nan Jiang, Sham M. Kakade
The Role of Coverage in Online Reinforcement Learning. ICLR (2023)
Kaiqing Zhang, Sham M. Kakade, Tamer Basar, Lin F. Yang
Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity. J. Mach. Learn. Res. 24 (2023)
Devvrit, Sneha Kudugunta, Aditya Kusupati, Tim Dettmers, Kaifeng Chen, Inderjit S. Dhillon, Yulia Tsvetkov, Hannaneh Hajishirzi, Sham M. Kakade, Ali Farhadi, Prateek Jain
MatFormer: Nested Transformer for Elastic Inference. CoRR (2023)
Nikunj Saunshi, Jordan T. Ash, Surbhi Goel, Dipendra Misra, Cyril Zhang, Sanjeev Arora, Sham M. Kakade, Akshay Krishnamurthy
Understanding Contrastive Learning Requires Incorporating Inductive Biases. CoRR (2022)
Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime. NeurIPS (2022)
Jens Tuyls, Shunyu Yao, Sham M. Kakade, Karthik Narasimhan
Multi-Stage Episodic Control for Strategic Exploration in Text Games. CoRR (2022)
Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime. CoRR (2022)
Tengyang Xie, Dylan J. Foster, Yu Bai, Nan Jiang, Sham M. Kakade
The Role of Coverage in Online Reinforcement Learning. CoRR (2022)
Benjamin L. Edelman, Surbhi Goel, Sham M. Kakade, Cyril Zhang
Inductive Biases and Variable Creation in Self-Attention Mechanisms. ICML (2022)
Aditya Kusupati, Gantavya Bhatt, Aniket Rege, Matthew Wallingford, Aditya Sinha, Vivek Ramanujan, William Howard-Snyder, Kaifeng Chen, Sham M. Kakade, Prateek Jain, Ali Farhadi
Matryoshka Representations for Adaptive Deployment. CoRR (2022)
Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression. ICML (2022)
Abhishek Gupta, Aldo Pacchiano, Yuexiang Zhai, Sham M. Kakade, Sergey Levine
Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity. NeurIPS (2022)
Jens Tuyls, Shunyu Yao, Sham M. Kakade, Karthik Narasimhan
Multi-Stage Episodic Control for Strategic Exploration in Text Games. ICLR (2022)
Nikunj Saunshi, Jordan T. Ash, Surbhi Goel, Dipendra Misra, Cyril Zhang, Sanjeev Arora, Sham M. Kakade, Akshay Krishnamurthy
Understanding Contrastive Learning Requires Incorporating Inductive Biases. ICML (2022)
Juan C. Perdomo, Akshay Krishnamurthy, Peter L. Bartlett, Sham M. Kakade
A Sharp Characterization of Linear Estimators for Offline Policy Evaluation. CoRR (2022)
Boaz Barak, Benjamin L. Edelman, Surbhi Goel, Sham M. Kakade, Eran Malach, Cyril Zhang
Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit. CoRR (2022)
Boaz Barak, Benjamin L. Edelman, Surbhi Goel, Sham M. Kakade, Eran Malach, Cyril Zhang
Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit. NeurIPS (2022)
Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift. CoRR (2022)
Surbhi Goel, Sham M. Kakade, Adam Kalai, Cyril Zhang
Recurrent Convolutional Neural Networks Learn Succinct Learning Algorithms. NeurIPS (2022)
Aditya Kusupati, Gantavya Bhatt, Aniket Rege, Matthew Wallingford, Aditya Sinha, Vivek Ramanujan, William Howard-Snyder, Kaifeng Chen, Sham M. Kakade, Prateek Jain, Ali Farhadi
Matryoshka Representation Learning. NeurIPS (2022)
Surbhi Goel, Sham M. Kakade, Adam Tauman Kalai, Cyril Zhang
Recurrent Convolutional Neural Networks Learn Succinct Learning Algorithms. CoRR (2022)
Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift. NeurIPS (2022)
Yonathan Efroni, Sham M. Kakade, Akshay Krishnamurthy, Cyril Zhang
Sparsity in Partially Controllable Linear Systems. ICML (2022)
Abhishek Gupta, Aldo Pacchiano, Yuexiang Zhai, Sham M. Kakade, Sergey Levine
Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity. CoRR (2022)
Krishna Pillutla, Sham M. Kakade, Zaïd Harchaoui
Robust Aggregation for Federated Learning. IEEE Trans. Signal Process. 70 (2022)
Jordan T. Ash, Cyril Zhang, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade
Anti-Concentrated Confidence Bonuses For Scalable Exploration. ICLR (2022)
Yu Bai, Minshuo Chen, Pan Zhou, Tuo Zhao, Jason D. Lee, Sham M. Kakade, Huan Wang, Caiming Xiong
How Important is the Train-Validation Split in Meta-Learning? ICML (2021)
Yuanhao Wang, Ruosong Wang, Sham M. Kakade
An Exponential Lower Bound for Linearly-Realizable MDPs with Constant Suboptimality Gap. CoRR (2021)
Ruosong Wang, Yifan Wu, Ruslan Salakhutdinov, Sham M. Kakade
Instabilities of Offline RL with Pre-Trained Neural Representation. ICML (2021)
Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade
Gone Fishing: Neural Active Learning with Fisher Embeddings. CoRR (2021)
Baihe Huang, Kaixuan Huang, Sham M. Kakade, Jason D. Lee, Qi Lei, Runzhe Wang, Jiaqi Yang
Going Beyond Linear RL: Sample Efficient Neural Function Approximation. NeurIPS (2021)
Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression. CoRR (2021)
Baihe Huang, Kaixuan Huang, Sham M. Kakade, Jason D. Lee, Qi Lei, Runzhe Wang, Jiaqi Yang
Optimal Gradient-based Algorithms for Non-concave Bandit Optimization. CoRR (2021)
Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
Benign Overfitting of Constant-Stepsize SGD for Linear Regression. CoRR (2021)
Jordan T. Ash, Cyril Zhang, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade
Anti-Concentrated Confidence Bonuses for Scalable Exploration. CoRR (2021)
Alekh Agarwal, Sham M. Kakade, Jason D. Lee, Gaurav Mahajan
On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift. J. Mach. Learn. Res. 22 (2021)
Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Dean P. Foster, Sham M. Kakade
The Benefits of Implicit Regularization from SGD in Least Squares Problems. CoRR (2021)
Simon S. Du, Sham M. Kakade, Jason D. Lee, Shachar Lovett, Gaurav Mahajan, Wen Sun, Ruosong Wang
Bilinear Classes: A Structural Framework for Provable Generalization in RL. ICML (2021)
Simon Shaolei Du, Wei Hu, Sham M. Kakade, Jason D. Lee, Qi Lei
Few-Shot Learning via Learning the Representation, Provably. ICLR (2021)
Dylan J. Foster, Sham M. Kakade, Jian Qian, Alexander Rakhlin
The Statistical Complexity of Interactive Decision Making. CoRR (2021)
Simon S. Du, Sham M. Kakade, Jason D. Lee, Shachar Lovett, Gaurav Mahajan, Wen Sun, Ruosong Wang
Bilinear Classes: A Structural Framework for Provable Generalization in RL. CoRR (2021)
Baihe Huang, Kaixuan Huang, Sham M. Kakade, Jason D. Lee, Qi Lei, Runzhe Wang, Jiaqi Yang
Optimal Gradient-based Algorithms for Non-concave Bandit Optimization. NeurIPS (2021)
Xiyang Liu, Weihao Kong, Sham M. Kakade, Sewoong Oh
Robust and differentially private mean estimation. NeurIPS (2021)
Ruosong Wang, Yifan Wu, Ruslan Salakhutdinov, Sham M. Kakade
Instabilities of Offline RL with Pre-Trained Neural Representation. CoRR (2021)
Aditya Kusupati, Matthew Wallingford, Vivek Ramanujan, Raghav Somani, Jae Sung Park, Krishna Pillutla, Prateek Jain, Sham M. Kakade, Ali Farhadi
LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes. CoRR (2021)
Aditya Kusupati, Matthew Wallingford, Vivek Ramanujan, Raghav Somani, Jae Sung Park, Krishna Pillutla, Prateek Jain, Sham M. Kakade, Ali Farhadi
LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes. NeurIPS (2021)
Motoya Ohnishi, Isao Ishikawa, Kendall Lowrey, Masahiro Ikeda, Sham M. Kakade, Yoshinobu Kawahara
Koopman Spectrum Nonlinear Regulator and Provably Efficient Online Learning. CoRR (2021)
Yuanhao Wang, Ruosong Wang, Sham M. Kakade
An Exponential Lower Bound for Linearly Realizable MDP with Constant Suboptimality Gap. NeurIPS (2021)
Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan
On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points. J. ACM 68 (2) (2021)
Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
Benign Overfitting of Constant-Stepsize SGD for Linear Regression. COLT (2021)
Kaixuan Huang, Sham M. Kakade, Jason D. Lee, Qi Lei
A Short Note on the Relationship of Information Gain and Eluder Dimension. CoRR (2021)
Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Dean P. Foster, Sham M. Kakade
The Benefits of Implicit Regularization from SGD in Least Squares Problems. NeurIPS (2021)
Benjamin L. Edelman, Surbhi Goel, Sham M. Kakade, Cyril Zhang
Inductive Biases and Variable Creation in Self-Attention Mechanisms. CoRR (2021)
Xiyang Liu, Weihao Kong, Sham M. Kakade, Sewoong Oh
Robust and Differentially Private Mean Estimation. CoRR (2021)
Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade
Gone Fishing: Neural Active Learning with Fisher Embeddings. NeurIPS (2021)
Baihe Huang, Kaixuan Huang, Sham M. Kakade, Jason D. Lee, Qi Lei, Runzhe Wang, Jiaqi Yang
Going Beyond Linear RL: Sample Efficient Neural Function Approximation. CoRR (2021)
Yonathan Efroni, Sham M. Kakade, Akshay Krishnamurthy, Cyril Zhang
Sparsity in Partially Controllable Linear Systems. CoRR (2021)
Preetum Nakkiran, Prayaag Venkat, Sham M. Kakade, Tengyu Ma
Optimal Regularization can Mitigate Double Descent. ICLR (2021)
Ruosong Wang, Dean P. Foster, Sham M. Kakade
What are the Statistical Limits of Offline RL with Linear Function Approximation? ICLR (2021)
Sanjeev Arora, Simon S. Du, Sham M. Kakade, Yuping Luo, Nikunj Saunshi
Provable Representation Learning for Imitation Learning via Bi-level Optimization. ICML (2020)
Simon S. Du, Wei Hu, Sham M. Kakade, Jason D. Lee, Qi Lei
Few-Shot Learning via Learning the Representation, Provably. CoRR (2020)
Weihao Kong, Raghav Somani, Zhao Song, Sham M. Kakade, Sewoong Oh
Meta-learning for mixed linear regression. CoRR (2020)
Ruosong Wang, Dean P. Foster, Sham M. Kakade
What are the Statistical Limits of Offline RL with Linear Function Approximation? CoRR (2020)
Yu Bai, Minshuo Chen, Pan Zhou, Tuo Zhao, Jason D. Lee, Sham M. Kakade, Huan Wang, Caiming Xiong
How Important is the Train-Validation Split in Meta-Learning? CoRR (2020)
Damek Davis, Dmitriy Drusvyatskiy, Sham M. Kakade, Jason D. Lee
Stochastic Subgradient Method Converges on Tame Functions. Found. Comput. Math. 20 (1) (2020)

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Publication Activity (10 Years)

Top Topics

Top Venues

Sham M. Kakade