Sham M. Kakade
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
98
Venues
15
Active years
2001–2026
Best venue rank
A*
Where they publish
Papers
98 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2026 | COLT | Risk Comparisons in Linear Regression: Implicit Regularization Dominates Explicit Regularization (Extended Abstract). | Jingfeng Wu, Peter L. Bartlett, Sham M. Kakade, Jason D. Lee, Bin Yu |
| 2025 | COLING | LoRA Soups: Merging LoRAs for Practical Skill Composition Tasks. | Akshara Prabhakar, Yuanzhi Li, Karthik Narasimhan, Sham M. Kakade, Eran Malach, Samy Jelassi |
| 2025 | ICLR | SOAP: Improving and Stabilizing Shampoo using Adam for Language Modeling. | Nikhil Vyas, Depen Morwani, Rosie Zhao, Itai Shapira, David Brandfonbrener, Lucas Janson, Sham M. Kakade |
| 2025 | ICLR | Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models. | Yuda Song, Hanlin Zhang, Carson Eisenach, Sham M. Kakade, Dean P. Foster, Udaya Ghai |
| 2025 | ICLR | Mixture of Parrots: Experts improve memorization more than reasoning. | Samy Jelassi, Clara Mohri, David Brandfonbrener, Alex Gu, Nikhil Vyas, Nikhil Anand, David Alvarez-Melis, Yuanzhi Li, Sham M. Kakade, Eran Malach |
| 2025 | ICLR | A New Perspective on Shampoo's Preconditioner. | Depen Morwani, Itai Shapira, Nikhil Vyas, Eran Malach, Sham M. Kakade, Lucas Janson |
| 2025 | ICLR | Flash Inference: Near Linear Time Inference for Long Convolution Sequence Models and Beyond. | Costin-Andrei Oncescu, Sanket Purandare, Stratos Idreos, Sham M. Kakade |
| 2025 | ICLR | Follow My Instruction and Spill the Beans: Scalable Data Extraction from Retrieval-Augmented Generation Systems. | Zhenting Qi, Hanlin Zhang, Eric P. Xing, Sham M. Kakade, Himabindu Lakkaraju |
| 2025 | ICLR | Eliminating Position Bias of Language Models: A Mechanistic Approach. | Ziqi Wang, Hanlin Zhang, Xiner Li, Kuan-Hao Huang, Chi Han, Shuiwang Ji, Sham M. Kakade, Hao Peng, Heng Ji |
| 2025 | ICLR | How Does Critical Batch Size Scale in Pre-training? | Hanlin Zhang, Depen Morwani, Nikhil Vyas, Jingfeng Wu, Difan Zou, Udaya Ghai, Dean P. Foster, Sham M. Kakade |
| 2025 | ICLR | Deconstructing What Makes a Good Optimizer for Autoregressive Language Models. | Rosie Zhao, Depen Morwani, David Brandfonbrener, Nikhil Vyas, Sham M. Kakade |
| 2025 | ICML | The Role of Sparsity for Length Generalization in LLMs. | Noah Golowich, Samy Jelassi, David Brandfonbrener, Sham M. Kakade, Eran Malach |
| 2025 | ICML | Universal Length Generalization with Turing Programs. | Kaiying Hou, David Brandfonbrener, Sham M. Kakade, Samy Jelassi, Eran Malach |
| 2025 | ICML | Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions. | Jaeyeon Kim, Kulin Shah, Vasilis Kontonis, Sham M. Kakade, Sitan Chen |
| 2024 | ICLR | Feature emergence via margin maximization: case studies in algebraic tasks. | Depen Morwani, Benjamin L. Edelman, Costin-Andrei Oncescu, Rosie Zhao, Sham M. Kakade |
| 2024 | ICML | Beyond Implicit Bias: The Insignificance of SGD Noise in Online Learning. | Nikhil Vyas, Depen Morwani, Rosie Zhao, Gal Kaplun, Sham M. Kakade, Boaz Barak |
| 2024 | ICML | Q-Probe: A Lightweight Approach to Reward Maximization for Language Models. | Kenneth Li, Samy Jelassi, Hugh Zhang, Sham M. Kakade, Martin Wattenberg, David Brandfonbrener |
| 2024 | ICML | Repeat After Me: Transformers are Better than State Space Models at Copying. | Samy Jelassi, David Brandfonbrener, Sham M. Kakade, Eran Malach |
| 2024 | NAACL | A Study on the Calibration of In-context Learning. | Hanlin Zhang, Yifan Zhang, Yaodong Yu, Dhruv Madeka, Dean P. Foster, Eric P. Xing, Himabindu Lakkaraju, Sham M. Kakade |
| 2023 | COLT | Learning Hidden Markov Models Using Conditional Samples. | Gaurav Mahajan, Sham M. Kakade, Akshay Krishnamurthy, Cyril Zhang |
| 2023 | ICLR | The Role of Coverage in Online Reinforcement Learning. | Tengyang Xie, Dylan J. Foster, Yu Bai, Nan Jiang, Sham M. Kakade |
| 2023 | ICML | Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games. | Dylan J. Foster, Noah Golowich, Sham M. Kakade |
| 2023 | ICML | On Provable Copyright Protection for Generative Models. | Nikhil Vyas, Sham M. Kakade, Boaz Barak |
| 2023 | ICML | Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron. | Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Sham M. Kakade |
| 2022 | ICLR | Anti-Concentrated Confidence Bonuses For Scalable Exploration. | Jordan T. Ash, Cyril Zhang, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade |
| 2022 | ICLR | Multi-Stage Episodic Control for Strategic Exploration in Text Games. | Jens Tuyls, Shunyu Yao, Sham M. Kakade, Karthik Narasimhan |
| 2022 | ICML | Inductive Biases and Variable Creation in Self-Attention Mechanisms. | Benjamin L. Edelman, Surbhi Goel, Sham M. Kakade, Cyril Zhang |
| 2022 | ICML | Sparsity in Partially Controllable Linear Systems. | Yonathan Efroni, Sham M. Kakade, Akshay Krishnamurthy, Cyril Zhang |
| 2022 | ICML | Understanding Contrastive Learning Requires Incorporating Inductive Biases. | Nikunj Saunshi, Jordan T. Ash, Surbhi Goel, Dipendra Misra, Cyril Zhang, Sanjeev Arora, Sham M. Kakade, Akshay Krishnamurthy |
| 2022 | ICML | Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression. | Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade |
| 2021 | COLT | Benign Overfitting of Constant-Stepsize SGD for Linear Regression. | Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade |
| 2021 | ICLR | Few-Shot Learning via Learning the Representation, Provably. | Simon Shaolei Du, Wei Hu, Sham M. Kakade, Jason D. Lee, Qi Lei |
| 2021 | ICLR | Optimal Regularization can Mitigate Double Descent. | Preetum Nakkiran, Prayaag Venkat, Sham M. Kakade, Tengyu Ma |
| 2021 | ICLR | What are the Statistical Limits of Offline RL with Linear Function Approximation? | Ruosong Wang, Dean P. Foster, Sham M. Kakade |
| 2021 | ICML | How Important is the Train-Validation Split in Meta-Learning? | Yu Bai, Minshuo Chen, Pan Zhou, Tuo Zhao, Jason D. Lee, Sham M. Kakade, Huan Wang, Caiming Xiong |
| 2021 | ICML | Bilinear Classes: A Structural Framework for Provable Generalization in RL. | Simon S. Du, Sham M. Kakade, Jason D. Lee, Shachar Lovett, Gaurav Mahajan, Wen Sun, Ruosong Wang |
| 2021 | ICML | Instabilities of Offline RL with Pre-Trained Neural Representation. | Ruosong Wang, Yifan Wu, Ruslan Salakhutdinov, Sham M. Kakade |
| 2020 | ALT | Leverage Score Sampling for Faster Accelerated Regression and ERM. | Naman Agarwal, Sham M. Kakade, Rahul Kidambi, Yin Tat Lee, Praneeth Netrapalli, Aaron Sidford |
| 2020 | ALT | The Nonstochastic Control Problem. | Elad Hazan, Sham M. Kakade, Karan Singh |
| 2020 | COLT | Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes. | Alekh Agarwal, Sham M. Kakade, Jason D. Lee, Gaurav Mahajan |
| 2020 | COLT | Model-Based Reinforcement Learning with a Generative Model is Minimax Optimal. | Alekh Agarwal, Sham M. Kakade, Lin F. Yang |
| 2020 | ICLR | Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning? | Simon S. Du, Sham M. Kakade, Ruosong Wang, Lin F. Yang |
| 2020 | ICML | Provable Representation Learning for Imitation Learning via Bi-level Optimization. | Sanjeev Arora, Simon S. Du, Sham M. Kakade, Yuping Luo, Nikunj Saunshi |
| 2020 | ICML | Calibration, Entropy Rates, and Memory in Language Models. | Mark Braverman, Xinyi Chen, Sham M. Kakade, Karthik Narasimhan, Cyril Zhang, Yi Zhang |
| 2020 | ICML | Meta-learning for Mixed Linear Regression. | Weihao Kong, Raghav Somani, Zhao Song, Sham M. Kakade, Sewoong Oh |
| 2020 | ICML | Soft Threshold Weight Reparameterization for Learnable Sparsity. | Aditya Kusupati, Vivek Ramanujan, Raghav Somani, Mitchell Wortsman, Prateek Jain, Sham M. Kakade, Ali Farhadi |
| 2020 | ICML | The Implicit and Explicit Regularization Effects of Dropout. | Colin Wei, Sham M. Kakade, Tengyu Ma |
| 2019 | COLT | Open Problem: Do Good Algorithms Necessarily Query Bad Points? | Rong Ge, Prateek Jain, Sham M. Kakade, Rahul Kidambi, Dheeraj M. Nagaraj, Praneeth Netrapalli |
| 2019 | ICLR | Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control. | Kendall Lowrey, Aravind Rajeswaran, Sham M. Kakade, Emanuel Todorov, Igor Mordatch |
| 2019 | ICML | Online Control with Adversarial Disturbances. | Naman Agarwal, Brian Bullins, Elad Hazan, Sham M. Kakade, Karan Singh |
| 2019 | ICML | Online Meta-Learning. | Chelsea Finn, Aravind Rajeswaran, Sham M. Kakade, Sergey Levine |
| 2019 | ICML | Provably Efficient Maximum Entropy Exploration. | Elad Hazan, Sham M. Kakade, Karan Singh, Abby Van Soest |
| 2019 | ICML | Maximum Likelihood Estimation for Learning Populations of Parameters. | Ramya Korlakai Vinayak, Weihao Kong, Gregory Valiant, Sham M. Kakade |
| 2019 | WWW | The Illusion of Change: Correcting for Biases in Change Inference for Sparse, Societal-Scale Data. | Gabriel Cadamuro, Ramya Korlakai Vinayak, Joshua Blumenstock, Sham M. Kakade, Jacob N. Shapiro |
| 2018 | COLT | Accelerating Stochastic Gradient Descent for Least Squares Regression. | Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford |
| 2018 | ICASSP | Invariances and Data Augmentation for Supervised Music Transcription. | John Thickstun, Zad Harchaoui, Dean P. Foster, Sham M. Kakade |
| 2018 | ICLR | On the insufficiency of existing momentum schemes for Stochastic Optimization. | Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, Sham M. Kakade |
| 2018 | ICLR | Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines. | Cathy Wu, Aravind Rajeswaran, Yan Duan, Vikash Kumar, Alexandre M. Bayen, Sham M. Kakade, Igor Mordatch, Pieter Abbeel |
| 2018 | ICML | Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator. | Maryam Fazel, Rong Ge, Sham M. Kakade, Mehran Mesbahi |
| 2018 | ITA | On the Insufficiency of Existing Momentum Schemes for Stochastic Optimization. | Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, Sham M. Kakade |
| 2018 | STOC | Prediction with a short memory. | Vatsal Sharan, Sham M. Kakade, Percy Liang, Gregory Valiant |
| 2017 | AISTATS | Global Convergence of Non-Convex Gradient Descent for Computing Matrix Squareroot. | Prateek Jain, Chi Jin, Sham M. Kakade, Praneeth Netrapalli |
| 2017 | ICLR | Learning Features of Music From Scratch. | John Thickstun, Zad Harchaoui, Sham M. Kakade |
| 2017 | ICML | How to Escape Saddle Points Efficiently. | Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M. Kakade, Michael I. Jordan |
| 2016 | COLT | Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja's Algorithm. | Prateek Jain, Chi Jin, Sham M. Kakade, Praneeth Netrapalli, Aaron Sidford |
| 2016 | ICML | Faster Eigenvector Computation via Shift-and-Invert Preconditioning. | Dan Garber, Elad Hazan, Chi Jin, Sham M. Kakade, Cameron Musco, Praneeth Netrapalli, Aaron Sidford |
| 2016 | ICML | Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis. | Rong Ge, Chi Jin, Sham M. Kakade, Praneeth Netrapalli, Aaron Sidford |
| 2015 | ALT | Tensor Decompositions for Learning Latent Variable Models (A Survey for ALT). | Anima Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade, Matus Telgarsky |
| 2015 | COLT | Competing with the Empirical Risk Minimizer in a Single Pass. | Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford |
| 2015 | ICML | A Linear Dynamical System Model for Text. | David Belanger, Sham M. Kakade |
| 2015 | ICML | Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization. | Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford |
| 2015 | STOC | Learning Mixtures of Gaussians in High Dimensions. | Rong Ge, Qingqing Huang, Sham M. Kakade |
| 2014 | ICML | Least Squares Revisited: Scalable Approaches for Multi-class Prediction. | Alekh Agarwal, Sham M. Kakade, Nikos Karampatziakis, Le Song, Gregory Valiant |
| 2013 | COLT | A Tensor Spectral Approach to Learning Mixed Membership Community Models. | Animashree Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade |
| 2013 | ICML | Learning Linear Bayesian Networks with Latent Variables. | Animashree Anandkumar, Daniel J. Hsu, Adel Javanmard, Sham M. Kakade |
| 2010 | ICML | Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design. | Niranjan Srinivas, Andreas Krause, Sham M. Kakade, Matthias W. Seeger |
| 2009 | COLT | A Spectral Algorithm for Learning Hidden Markov Models. | Daniel J. Hsu, Sham M. Kakade, Tong Zhang |
| 2009 | ICML | Multi-view clustering via canonical correlation analysis. | Kamalika Chaudhuri, Sham M. Kakade, Karen Livescu, Karthik Sridharan |
| 2008 | COLT | High-Probability Regret Bounds for Bandit Online Linear Optimization. | Peter L. Bartlett, Varsha Dani, Thomas P. Hayes, Sham M. Kakade, Alexander Rakhlin, Ambuj Tewari |
| 2008 | COLT | Stochastic Linear Optimization under Bandit Feedback. | Varsha Dani, Thomas P. Hayes, Sham M. Kakade |
| 2008 | COLT | An Information Theoretic Framework for Multi-view Learning. | Karthik Sridharan, Sham M. Kakade |
| 2008 | ICML | Efficient bandit algorithms for online multiclass prediction. | Sham M. Kakade, Shai Shalev-Shwartz, Ambuj Tewari |
| 2007 | COLT | Multi-view Regression Via Canonical Correlation Analysis. | Sham M. Kakade, Dean P. Foster |
| 2007 | ICCV | Leveragingarchivalvideo for building face datasets. | Deva Ramanan, Simon Baker, Sham M. Kakade |
| 2007 | IJCAI | The Value of Observation for Monitoring Dynamic Systems. | Eyal Even-Dar, Sham M. Kakade, Yishay Mansour |
| 2007 | STOC | Playing games with approximation algorithms. | Sham M. Kakade, Adam Tauman Kalai, Katrina Ligett |
| 2006 | ICML | Cover trees for nearest neighbor. | Alina Beygelzimer, Sham M. Kakade, John Langford |
| 2006 | ITW | Calibration via Regression. | Dean P. Foster, Sham M. Kakade |
| 2005 | COLT | Trading in Markovian Price Models. | Sham M. Kakade, Michael J. Kearns |
| 2005 | IJCAI | Reinforcement Learning in POMDPs Without Resets. | Eyal Even-Dar, Sham M. Kakade, Yishay Mansour |
| 2005 | UAI | Planning in POMDPs Using Multiplicity Automata. | Eyal Even-Dar, Sham M. Kakade, Yishay Mansour |
| 2004 | COLT | Deterministic Calibration and Nash Equilibrium. | Sham M. Kakade, Dean P. Foster |
| 2004 | COLT | Graphical Economics. | Sham M. Kakade, Michael J. Kearns, Luis E. Ortiz |
| 2003 | ICML | Exploration in Metric State Spaces. | Sham M. Kakade, Michael J. Kearns, John Langford |
| 2002 | ICML | Approximately Optimal Approximate Reinforcement Learning. | Sham M. Kakade, John Langford |
| 2002 | ICML | An Alternate Objective Function for Markovian Fields. | Sham M. Kakade, Yee Whye Teh, Sam T. Roweis |
| 2002 | ICML | Competitive Analysis of the Explore/Exploit Tradeoff. | John Langford, Martin Zinkevich, Sham M. Kakade |
| 2001 | COLT | Optimizing Average Reward Using Discounted Rewards. | Sham M. Kakade |