Acquisition in Autoshaping. - Sham M. Kakade; Peter Dayan (NIPS, 1999)

Explaining Away in Weight Space. - Peter Dayan; Sham M. Kakade (NIPS, 2000)

Dopamine Bonuses. - Sham M. Kakade; Peter Dayan (NIPS, 2000)

Optimizing Average Reward Using Discounted Rewards. - Sham M. Kakade (COLT/EuroCOLT, 2001)

A Natural Policy Gradient. - Sham M. Kakade (NIPS, 2001)

Approximately Optimal Approximate Reinforcement Learning. - Sham M. Kakade; John Langford 001 (ICML, 2002)

An Alternate Objective Function for Markovian Fields. - Sham M. Kakade; Yee Whye Teh; Sam T. Roweis (ICML, 2002)

Competitive Analysis of the Explore/Exploit Tradeoff. - John Langford 001; Martin Zinkevich; Sham M. Kakade (ICML, 2002)

Opponent interactions between serotonin and dopamine. - Nathaniel D. Daw; Sham M. Kakade; Peter Dayan (Neural Networks, 2002)

Dopamine: generalization and bonuses. - Sham M. Kakade; Peter Dayan (Neural Networks, 2002)

Exploration in Metric State Spaces. - Sham M. Kakade; Michael J. Kearns; John Langford 001 (ICML, 2003)

Policy Search by Dynamic Programming. - J. Andrew Bagnell; Sham M. Kakade; Andrew Y. Ng; Jeff G. Schneider (NIPS, 2003)

Correlated equilibria in graphical games. - Sham M. Kakade; Michael J. Kearns; John Langford 001; Luis E. Ortiz (EC, 2003)

Graphical Economics. - Sham M. Kakade; Michael J. Kearns; Luis E. Ortiz (COLT, 2004)

Experts in a Markov Decision Process. - Eyal Even-Dar; Sham M. Kakade; Yishay Mansour (NIPS, 2004)

Economic Properties of Social Networks. - Sham M. Kakade; Michael J. Kearns; Luis E. Ortiz; Robin Pemantle; Siddharth Suri (NIPS, 2004)

Online Bounds for Bayesian Algorithms. - Sham M. Kakade; Andrew Y. Ng (NIPS, 2004)

Competitive algorithms for VWAP and limit order trading. - Sham M. Kakade; Michael J. Kearns; Yishay Mansour; Luis E. Ortiz (EC, 2004)

Trading in Markovian Price Models. - Sham M. Kakade; Michael J. Kearns (COLT, 2005)

Reinforcement Learning in POMDPs Without Resets. - Eyal Even-Dar; Sham M. Kakade; Yishay Mansour (IJCAI, 2005)

From Batch to Transductive Online Learning. - Sham M. Kakade; Adam Kalai (NIPS, 2005)

Worst-Case Bounds for Gaussian Process Models. - Sham M. Kakade; Matthias W. Seeger; Dean P. Foster (NIPS, 2005)

Planning in POMDPs Using Multiplicity Automata. - Eyal Even-Dar; Sham M. Kakade; Yishay Mansour (UAI, 2005)

Cover trees for nearest neighbor. - Alina Beygelzimer; Sham M. Kakade; John Langford 001 (ICML, 2006)

Calibration via Regression. - Dean P. Foster; Sham M. Kakade (ITW, 2006)

(In)Stability properties of limit order dynamics. - Eyal Even-Dar; Sham M. Kakade; Michael J. Kearns; Yishay Mansour (EC, 2006)

Multi-view Regression Via Canonical Correlation Analysis. - Sham M. Kakade; Dean P. Foster (COLT, 2007)

Leveragingarchivalvideo for building face datasets. - Deva Ramanan; Simon Baker; Sham M. Kakade (ICCV, 2007)

The Value of Observation for Monitoring Dynamic Systems. - Eyal Even-Dar; Sham M. Kakade; Yishay Mansour (IJCAI, 2007)

The Price of Bandit Information for Online Optimization. - Varsha Dani; Thomas P. Hayes; Sham M. Kakade (NIPS, 2007)

Maximum Entropy Correlated Equilibria. - Luis E. Ortiz; Robert E. Schapire; Sham M. Kakade (AISTATS, 2007)

High-Probability Regret Bounds for Bandit Online Linear Optimization. - Peter L. Bartlett; Varsha Dani; Thomas P. Hayes; Sham M. Kakade; Alexander Rakhlin; Ambuj Tewari (COLT, 2008)

Stochastic Linear Optimization under Bandit Feedback. - Varsha Dani; Thomas P. Hayes; Sham M. Kakade (COLT, 2008)

An Information Theoretic Framework for Multi-view Learning. - Karthik Sridharan; Sham M. Kakade (COLT, 2008)

Efficient bandit algorithms for online multiclass prediction. - Sham M. Kakade; Shai Shalev-Shwartz; Ambuj Tewari (ICML, 2008)

On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization. - Sham M. Kakade; Karthik Sridharan; Ambuj Tewari (NIPS, 2008)

On the Generalization Ability of Online Strongly Convex Programming Algorithms. - Sham M. Kakade; Ambuj Tewari (NIPS, 2008)

Mind the Duality Gap: Logarithmic regret algorithms for online optimization. - Shai Shalev-Shwartz; Sham M. Kakade (NIPS, 2008)

Deterministic calibration and Nash equilibrium. - Sham M. Kakade; Dean P. Foster (J. Comput. Syst. Sci., 2008)

Information Consistency of Nonparametric Gaussian Process Methods. - Matthias W. Seeger; Sham M. Kakade; Dean P. Foster (IEEE Trans. Inf. Theory, 2008)

Multi-view clustering via canonical correlation analysis. - Kamalika Chaudhuri; Sham M. Kakade; Karen Livescu; Karthik Sridharan (ICML, 2009)

Multi-Label Prediction via Compressed Sensing. - Daniel J. Hsu; Sham M. Kakade; John Langford 001; Tong Zhang 001 (NIPS, 2009)

The price of truthfulness for pay-per-click auctions. - Nikhil R. Devanur; Sham M. Kakade (EC, 2009)

Applications of strong convexity–strong smoothness duality to learning with matrices - Sham M. Kakade; Shai Shalev-Shwartz; Ambuj Tewari (CoRR, 2009)

Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity - Sham M. Kakade; Ohad Shamir; Karthik Sridharan; Ambuj Tewari (CoRR, 2009)

Gaussian Process Bandits without Regret: An Experimental Design Approach - Niranjan Srinivas; Andreas Krause 001; Sham M. Kakade; Matthias W. Seeger (CoRR, 2009)

Online Markov Decision Processes. - Eyal Even-Dar; Sham M. Kakade; Yishay Mansour (Math. Oper. Res., 2009)

Playing Games with Approximation Algorithms. - Sham M. Kakade; Adam Tauman Kalai; Katrina Ligett (SIAM J. Comput., 2009)

Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design. - Niranjan Srinivas; Andreas Krause 001; Sham M. Kakade; Matthias W. Seeger (ICML, 2010)

Learning from Logged Implicit Exploration Data. - Alexander L. Strehl; John Langford 001; Lihong Li 001; Sham M. Kakade (NIPS, 2010)

An Optimal Dynamic Mechanism for Multi-Armed Bandit Processes - Sham M. Kakade; Ilan Lobel; Hamid Nazerzadeh (CoRR, 2010)

Learning from Logged Implicit Exploration Data - Alexander L. Strehl; John Langford 001; Sham M. Kakade (CoRR, 2010)

Robust Matrix Decomposition with Outliers - Daniel J. Hsu; Sham M. Kakade; Tong Zhang 001 (CoRR, 2010)

Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity. - Sham M. Kakade; Ohad Shamir; Karthik Sindharan; Ambuj Tewari (AISTATS, 2010)

Guest editorial: special issue on learning theory. - Sham M. Kakade; Ping Li 001 (Mach. Learn., 2010)

Spectral Methods for Learning Multivariate Latent Tree Structure. - Animashree Anandkumar; Kamalika Chaudhuri; Daniel J. Hsu; Sham M. Kakade; Le Song; Tong Zhang 001 (NIPS, 2011)

Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression. - Sham M. Kakade; Adam Kalai; Varun Kanade; Ohad Shamir (NIPS, 2011)

Dimension-free tail inequalities for sums of random matrices. - Daniel J. Hsu; Sham M. Kakade; Tong Zhang 001 (CoRR, 2011)

Domain Adaptation: Overfitting and Small Sample Statistics - Dean P. Foster; Sham M. Kakade; Ruslan Salakhutdinov (CoRR, 2011)

An Analysis of Random Design Linear Regression - Daniel J. Hsu; Sham M. Kakade; Tong Zhang 001 (CoRR, 2011)

A tail inequality for quadratic forms of subgaussian random vectors - Daniel J. Hsu; Sham M. Kakade; Tong Zhang 001 (CoRR, 2011)

Domain Adaptation with Coupled Subspaces. - John Blitzer; Sham M. Kakade; Dean P. Foster (AISTATS, 2011)

Preface. - Sham M. Kakade; Ulrike von Luxburg (COLT, 2011)

Optimal dynamic mechanism design via a virtual VCG mechanism. - Sham M. Kakade; Ilan Lobel; Hamid Nazerzadeh (SIGecom Exch., 2011)

Robust Matrix Decomposition With Sparse Corruptions. - Daniel J. Hsu; Sham M. Kakade; Tong Zhang 001 (IEEE Trans. Inf. Theory, 2011)

Learning Mixtures of Tree Graphical Models. - Animashree Anandkumar; Daniel J. Hsu; Furong Huang; Sham M. Kakade (NIPS, 2012)

Identifiability and Unmixing of Latent Parse Trees. - Daniel J. Hsu; Sham M. Kakade; Percy Liang (NIPS, 2012)

Learning High-Dimensional Mixtures of Graphical Models - Animashree Anandkumar; Daniel J. Hsu; Sham M. Kakade (CoRR, 2012)

Two SVDs Suffice: Spectral decompositions for probabilistic topic modeling and latent Dirichlet allocation - Animashree Anandkumar; Dean P. Foster; Daniel J. Hsu; Sham M. Kakade; Yi-Kai Liu 001 (CoRR, 2012)

Learning Gaussian Mixture Models: Moment Methods and Spectral Decompositions - Daniel J. Hsu; Sham M. Kakade (CoRR, 2012)

Analysis of a randomized approximation scheme for matrix multiplication - Daniel J. Hsu; Sham M. Kakade; Tong Zhang 001 (CoRR, 2012)

A spectral algorithm for learning Hidden Markov Models. - Daniel J. Hsu; Sham M. Kakade; Tong Zhang 001 (J. Comput. Syst. Sci., 2012)

A Method of Moments for Mixture Models and Hidden Markov Models. - Animashree Anandkumar; Daniel J. Hsu; Sham M. Kakade (COLT, 2012)

Towards Minimax Policies for Online Linear Optimization with Bandit Feedback. - Sébastien Bubeck; Nicolò Cesa-Bianchi; Sham M. Kakade (COLT, 2012)

(weak) Calibration is Computationally Hard. - Elad Hazan; Sham M. Kakade (COLT, 2012)

Regularization Techniques for Learning with Matrices. - Sham M. Kakade; Shai Shalev-Shwartz; Ambuj Tewari (J. Mach. Learn. Res., 2012)

Domain Adaptation: A Small Sample Statistical Approach. - Ruslan Salakhutdinov; Sham M. Kakade; Dean P. Foster (AISTATS, 2012)

Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting. - Niranjan Srinivas; Andreas Krause 001; Sham M. Kakade; Matthias W. Seeger (IEEE Trans. Inf. Theory, 2012)

A Tensor Spectral Approach to Learning Mixed Membership Community Models. - Animashree Anandkumar; Rong Ge 001; Daniel J. Hsu; Sham M. Kakade (COLT, 2013)

Learning Linear Bayesian Networks with Latent Variables. - Animashree Anandkumar; Daniel J. Hsu; Adel Javanmard; Sham M. Kakade (ICML (1), 2013)

Learning mixtures of spherical gaussians: moment methods and spectral decompositions. - Daniel J. Hsu; Sham M. Kakade (ITCS, 2013)

Optimal Dynamic Mechanism Design and the Virtual-Pivot Mechanism. - Sham M. Kakade; Ilan Lobel; Hamid Nazerzadeh (Oper. Res., 2013)

A risk comparison of ordinary least squares vs ridge regression. - Paramveer S. Dhillon; Dean P. Foster; Sham M. Kakade; Lyle H. Ungar (J. Mach. Learn. Res., 2013)

Stochastic Convex Optimization with Bandit Feedback. - Alekh Agarwal; Dean P. Foster; Daniel J. Hsu; Sham M. Kakade; Alexander Rakhlin (SIAM J. Optim., 2013)

Minimal realization problem for Hidden Markov Models. - Qingqing Huang 001; Rong Ge 001; Sham M. Kakade; Munther A. Dahleh (Allerton, 2014)

Least Squares Revisited: Scalable Approaches for Multi-class Prediction. - Alekh Agarwal; Sham M. Kakade; Nikos Karampatziakis; Le Song; Gregory Valiant (ICML, 2014)

Random Design Analysis of Ridge Regression. - Daniel J. Hsu; Sham M. Kakade; Tong Zhang 001 (Found. Comput. Math., 2014)

A tensor approach to learning mixed membership community models. - Animashree Anandkumar; Rong Ge 001; Daniel J. Hsu; Sham M. Kakade (J. Mach. Learn. Res., 2014)

Tensor decompositions for learning latent variable models. - Animashree Anandkumar; Rong Ge 001; Daniel J. Hsu; Sham M. Kakade; Matus Telgarsky (J. Mach. Learn. Res., 2014)

Tensor Decompositions for Learning Latent Variable Models (A Survey for ALT). - Anima Anandkumar; Rong Ge 001; Daniel J. Hsu; Sham M. Kakade; Matus Telgarsky (ALT, 2015)

Competing with the Empirical Risk Minimizer in a Single Pass. - Roy Frostig; Rong Ge 001; Sham M. Kakade; Aaron Sidford (COLT, 2015)

A Linear Dynamical System Model for Text. - David Belanger 002; Sham M. Kakade (ICML, 2015)

Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization. - Roy Frostig; Rong Ge 001; Sham M. Kakade; Aaron Sidford (ICML, 2015)

Convergence Rates of Active Learning for Maximum Likelihood Estimation. - Kamalika Chaudhuri; Sham M. Kakade; Praneeth Netrapalli; Sujay Sanghavi (NIPS, 2015)

Super-Resolution Off the Grid. - Qingqing Huang 001; Sham M. Kakade (NIPS, 2015)

Learning Mixtures of Gaussians in High Dimensions. - Rong Ge 001; Qingqing Huang 001; Sham M. Kakade (STOC, 2015)

A Spectral Algorithm for Latent Dirichlet Allocation. - Anima Anandkumar; Dean P. Foster; Daniel J. Hsu; Sham M. Kakade; Yi-Kai Liu 001 (Algorithmica, 2015)

Computing Matrix Squareroot via Non Convex Local Search. - Prateek Jain 002; Chi Jin 001; Sham M. Kakade; Praneeth Netrapalli (CoRR, 2015)

Robust Shift-and-Invert Preconditioning: Faster and More Sample Efficient Algorithms for Eigenvector Computation. - Chi Jin 001; Sham M. Kakade; Cameron Musco; Praneeth Netrapalli; Aaron Sidford (CoRR, 2015)

When are overcomplete topic models identifiable? uniqueness of tensor tucker decompositions with structured sparsity. - Animashree Anandkumar; Daniel J. Hsu; Majid Janzamin; Sham M. Kakade (J. Mach. Learn. Res., 2015)

Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja's Algorithm. - Prateek Jain 002; Chi Jin 001; Sham M. Kakade; Praneeth Netrapalli; Aaron Sidford (COLT, 2016)

Faster Eigenvector Computation via Shift-and-Invert Preconditioning. - Dan Garber; Elad Hazan; Chi Jin 001; Sham M. Kakade; Cameron Musco; Praneeth Netrapalli; Aaron Sidford (ICML, 2016)

Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis. - Rong Ge 001; Chi Jin 001; Sham M. Kakade; Praneeth Netrapalli; Aaron Sidford (ICML, 2016)

Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent. - Chi Jin 001; Sham M. Kakade; Praneeth Netrapalli (NIPS, 2016)

Matching Matrix Bernstein with Little Memory: Near-Optimal Finite Sample Guarantees for Oja's Algorithm. - Prateek Jain 002; Chi Jin 001; Sham M. Kakade; Praneeth Netrapalli; Aaron Sidford (CoRR, 2016)

Parallelizing Stochastic Approximation Through Mini-Batching and Tail-Averaging. - Prateek Jain 002; Sham M. Kakade; Rahul Kidambi; Praneeth Netrapalli; Aaron Sidford (CoRR, 2016)

Canonical Correlation Analysis for Analyzing Sequences of Medical Billing Codes. - Corinne L. Jones; Sham M. Kakade; Lucas W. Thornblade; David R. Flum; Abraham D. Flaxman (CoRR, 2016)

Minimal Realization Problems for Hidden Markov Models. - Qingqing Huang 001; Rong Ge 001; Sham M. Kakade; Munther A. Dahleh (IEEE Trans. Signal Process., 2016)

Global Convergence of Non-Convex Gradient Descent for Computing Matrix Squareroot. - Prateek Jain 002; Chi Jin 001; Sham M. Kakade; Praneeth Netrapalli (AISTATS, 2017)

A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares). - Prateek Jain 002; Sham M. Kakade; Rahul Kidambi; Praneeth Netrapalli; Venkata Krishna Pillutla; Aaron Sidford (FSTTCS, 2017)

Learning Features of Music From Scratch. - John Thickstun; Zaïd Harchaoui; Sham M. Kakade (ICLR (Poster), 2017)

How to Escape Saddle Points Efficiently. - Chi Jin 001; Rong Ge 001; Praneeth Netrapalli; Sham M. Kakade; Michael I. Jordan (ICML, 2017)

Towards Generalization and Simplicity in Continuous Control. - Aravind Rajeswaran; Kendall Lowrey; Emanuel Todorov; Sham M. Kakade (NIPS, 2017)

Learning Overcomplete HMMs. - Vatsal Sharan; Sham M. Kakade; Percy Liang; Gregory Valiant (NIPS, 2017)

Accelerating Stochastic Gradient Descent. - Prateek Jain 002; Sham M. Kakade; Rahul Kidambi; Praneeth Netrapalli; Aaron Sidford (CoRR, 2017)

Parallelizing Stochastic Gradient Descent for Least Squares Regression: Mini-batching, Averaging, and Model Misspecification. - Prateek Jain 002; Sham M. Kakade; Rahul Kidambi; Praneeth Netrapalli; Aaron Sidford (J. Mach. Learn. Res., 2017)

Accelerating Stochastic Gradient Descent for Least Squares Regression. - Prateek Jain 002; Sham M. Kakade; Rahul Kidambi; Praneeth Netrapalli; Aaron Sidford (COLT, 2018)

Invariances and Data Augmentation for Supervised Music Transcription. - John Thickstun; Zaïd Harchaoui; Dean P. Foster; Sham M. Kakade (ICASSP, 2018)

On the insufficiency of existing momentum schemes for Stochastic Optimization. - Rahul Kidambi; Praneeth Netrapalli; Prateek Jain 002; Sham M. Kakade (ICLR, 2018)

Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines. - Cathy Wu 002; Aravind Rajeswaran; Yan Duan; Vikash Kumar; Alexandre M. Bayen; Sham M. Kakade; Igor Mordatch; Pieter Abbeel (ICLR, 2018)

Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator. - Maryam Fazel; Rong Ge 001; Sham M. Kakade; Mehran Mesbahi (ICML, 2018)

Recovering Structured Probability Matrices. - Qingqing Huang 001; Sham M. Kakade; Weihao Kong; Gregory Valiant (ITCS, 2018)

Provably Correct Automatic Sub-Differentiation for Qualified Programs. - Sham M. Kakade; Jason D. Lee (NeurIPS, 2018)

A Smoother Way to Train Structured Prediction Models. - Venkata Krishna Pillutla; Vincent Roulet; Sham M. Kakade; Zaïd Harchaoui (NeurIPS, 2018)

Prediction with a short memory. - Vatsal Sharan; Sham M. Kakade; Percy Liang; Gregory Valiant (STOC, 2018)

Global Convergence of Policy Gradient Methods for Linearized Control Problems. - Maryam Fazel; Rong Ge 001; Sham M. Kakade; Mehran Mesbahi (CoRR, 2018)

Provably Correct Automatic Subdifferentiation for Qualified Programs. - Sham M. Kakade; Jason D. Lee (CoRR, 2018)

Open Problem: Do Good Algorithms Necessarily Query Bad Points? - Rong Ge 001; Prateek Jain 002; Sham M. Kakade; Rahul Kidambi; Dheeraj M. Nagaraj; Praneeth Netrapalli (COLT, 2019)

Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control. - Kendall Lowrey; Aravind Rajeswaran; Sham M. Kakade; Emanuel Todorov; Igor Mordatch (ICLR (Poster), 2019)

Online Control with Adversarial Disturbances. - Naman Agarwal; Brian Bullins; Elad Hazan; Sham M. Kakade; Karan Singh (ICML, 2019)

Online Meta-Learning. - Chelsea Finn; Aravind Rajeswaran; Sham M. Kakade; Sergey Levine (ICML, 2019)

Provably Efficient Maximum Entropy Exploration. - Elad Hazan; Sham M. Kakade; Karan Singh; Abby Van Soest (ICML, 2019)

Maximum Likelihood Estimation for Learning Populations of Parameters. - Ramya Korlakai Vinayak; Weihao Kong; Gregory Valiant; Sham M. Kakade (ICML, 2019)

Coupled Recurrent Models for Polyphonic Music Composition. - John Thickstun; Zaïd Harchaoui; Dean P. Foster; Sham M. Kakade (ISMIR, 2019)

The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares. - Rong Ge 001; Sham M. Kakade; Rahul Kidambi; Praneeth Netrapalli (NeurIPS, 2019)

Meta-Learning with Implicit Gradients. - Aravind Rajeswaran; Chelsea Finn; Sham M. Kakade; Sergey Levine (NeurIPS, 2019)

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 (WWW, 2019)

A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Norm. - Chi Jin 001; Praneeth Netrapalli; Rong Ge 001; Sham M. Kakade; Michael I. Jordan (CoRR, 2019)

Stochastic Gradient Descent Escapes Saddle Points Efficiently. - Chi Jin 001; Praneeth Netrapalli; Rong Ge 001; Sham M. Kakade; Michael I. Jordan (CoRR, 2019)

The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure. - Rong Ge 001; Sham M. Kakade; Rahul Kidambi; Praneeth Netrapalli (CoRR, 2019)

On the Optimality of Sparse Model-Based Planning for Markov Decision Processes. - Alekh Agarwal; Sham M. Kakade; Lin F. Yang (CoRR, 2019)

Optimal Estimation of Change in a Population of Parameters. - Ramya Korlakai Vinayak; Weihao Kong; Sham M. Kakade (CoRR, 2019)

Robust Aggregation for Federated Learning. - Venkata Krishna Pillutla; Sham M. Kakade; Zaïd Harchaoui (CoRR, 2019)

Leverage Score Sampling for Faster Accelerated Regression and ERM. - Naman Agarwal; Sham M. Kakade; Rahul Kidambi; Yin Tat Lee; Praneeth Netrapalli; Aaron Sidford (ALT, 2020)

The Nonstochastic Control Problem. - Elad Hazan; Sham M. Kakade; Karan Singh (ALT, 2020)

Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes. - Alekh Agarwal; Sham M. Kakade; Jason D. Lee; Gaurav Mahajan (COLT, 2020)

Model-Based Reinforcement Learning with a Generative Model is Minimax Optimal. - Alekh Agarwal; Sham M. Kakade; Lin F. Yang (COLT, 2020)

Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning? - Simon S. Du; Sham M. Kakade; Ruosong Wang; Lin F. Yang (ICLR, 2020)

Provable Representation Learning for Imitation Learning via Bi-level Optimization. - Sanjeev Arora; Simon S. Du; Sham M. Kakade; Yuping Luo; Nikunj Saunshi (ICML, 2020)

Calibration, Entropy Rates, and Memory in Language Models. - Mark Braverman; Xinyi Chen; Sham M. Kakade; Karthik Narasimhan; Cyril Zhang; Yi Zhang (ICML, 2020)

Meta-learning for Mixed Linear Regression. - Weihao Kong; Raghav Somani; Zhao Song 002; Sham M. Kakade; Sewoong Oh (ICML, 2020)

Soft Threshold Weight Reparameterization for Learnable Sparsity. - Aditya Kusupati; Vivek Ramanujan; Raghav Somani; Mitchell Wortsman; Prateek Jain 002; Sham M. Kakade; Ali Farhadi (ICML, 2020)

The Implicit and Explicit Regularization Effects of Dropout. - Colin Wei; Sham M. Kakade; Tengyu Ma 001 (ICML, 2020)

PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning. - Alekh Agarwal; Mikael Henaff; Sham M. Kakade; Wen Sun 002 (NeurIPS, 2020)

FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs. - Alekh Agarwal; Sham M. Kakade; Akshay Krishnamurthy; Wen Sun 002 (NeurIPS, 2020)

Sample-Efficient Reinforcement Learning of Undercomplete POMDPs. - Chi Jin 001; Sham M. Kakade; Akshay Krishnamurthy; Qinghua Liu (NeurIPS, 2020)

Information Theoretic Regret Bounds for Online Nonlinear Control. - Sham M. Kakade; Akshay Krishnamurthy; Kendall Lowrey; Motoya Ohnishi; Wen Sun 002 (NeurIPS, 2020)

Robust Meta-learning for Mixed Linear Regression with Small Batches. - Weihao Kong; Raghav Somani; Sham M. Kakade; Sewoong Oh (NeurIPS, 2020)

Is Long Horizon RL More Difficult Than Short Horizon RL? - Ruosong Wang; Simon S. Du; Lin F. Yang; Sham M. Kakade (NeurIPS, 2020)

PACT: Privacy Sensitive Protocols and Mechanisms for Mobile Contact Tracing. - Justin Chan; Dean P. Foster; Shyam Gollakota; Eric Horvitz; Joseph Jaeger; Sham M. Kakade; Tadayoshi Kohno; John Langford 001; Jonathan Larson; Sudheesh Singanamalla; Jacob E. Sunshine; Stefano Tessaro (CoRR, 2020)

Is Long Horizon Reinforcement Learning More Difficult Than Short Horizon Reinforcement Learning? - Ruosong Wang; Simon S. Du; Lin F. Yang; Sham M. Kakade (CoRR, 2020)

PACT: Privacy-Sensitive Protocols And Mechanisms for Mobile Contact Tracing. - Justin Chan; Landon P. Cox; Dean P. Foster; Shyam Gollakota; Eric Horvitz; Joseph Jaeger; Sham M. Kakade; Tadayoshi Kohno; John Langford 001; Jonathan Larson; Puneet Sharma; Sudheesh Singanamalla; et al. (IEEE Data Eng. Bull., 2020)

Stochastic Subgradient Method Converges on Tame Functions. - Damek Davis; Dmitriy Drusvyatskiy; Sham M. Kakade; Jason D. Lee (Found. Comput. Math., 2020)

Few-Shot Learning via Learning the Representation, Provably. - Simon Shaolei Du; Wei Hu 014; Sham M. Kakade; Jason D. Lee; Qi Lei (ICLR, 2021)

Optimal Regularization can Mitigate Double Descent. - Preetum Nakkiran; Prayaag Venkat; Sham M. Kakade; Tengyu Ma 001 (ICLR, 2021)

What are the Statistical Limits of Offline RL with Linear Function Approximation? - Ruosong Wang; Dean P. Foster; Sham M. Kakade (ICLR, 2021)

How Important is the Train-Validation Split in Meta-Learning? - Yu Bai 017; Minshuo Chen; Pan Zhou 002; Tuo Zhao; Jason D. Lee; Sham M. Kakade; Huan Wang 014; Caiming Xiong (ICML, 2021)

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 002; Ruosong Wang (ICML, 2021)

Instabilities of Offline RL with Pre-Trained Neural Representation. - Ruosong Wang; Yifan Wu; Ruslan Salakhutdinov; Sham M. Kakade (ICML, 2021)

Gone Fishing: Neural Active Learning with Fisher Embeddings. - Jordan T. Ash; Surbhi Goel; Akshay Krishnamurthy; Sham M. Kakade (NeurIPS, 2021)

Going Beyond Linear RL: Sample Efficient Neural Function Approximation. - Baihe Huang; Kaixuan Huang; Sham M. Kakade; Jason D. Lee; Qi Lei; Runzhe Wang; Jiaqi Yang 001 (NeurIPS, 2021)

Optimal Gradient-based Algorithms for Non-concave Bandit Optimization. - Baihe Huang; Kaixuan Huang; Sham M. Kakade; Jason D. Lee; Qi Lei; Runzhe Wang; Jiaqi Yang 001 (NeurIPS, 2021)

LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes. - Aditya Kusupati; Matthew Wallingford; Vivek Ramanujan; Raghav Somani; Jae Sung Park; Krishna Pillutla; Prateek Jain 002; Sham M. Kakade; Ali Farhadi (NeurIPS, 2021)

Robust and differentially private mean estimation. - Xiyang Liu; Weihao Kong; Sham M. Kakade; Sewoong Oh (NeurIPS, 2021)

An Exponential Lower Bound for Linearly Realizable MDP with Constant Suboptimality Gap. - Yuanhao Wang 001; Ruosong Wang; Sham M. Kakade (NeurIPS, 2021)

The Benefits of Implicit Regularization from SGD in Least Squares Problems. - Difan Zou; Jingfeng Wu; Vladimir Braverman; Quanquan Gu; Dean P. Foster; Sham M. Kakade (NeurIPS, 2021)

An Exponential Lower Bound for Linearly-Realizable MDPs with Constant Suboptimality Gap. - Yuanhao Wang 001; Ruosong Wang; Sham M. Kakade (CoRR, 2021)

Koopman Spectrum Nonlinear Regulator and Provably Efficient Online Learning. - Motoya Ohnishi; Isao Ishikawa; Kendall Lowrey; Masahiro Ikeda; Sham M. Kakade; Yoshinobu Kawahara (CoRR, 2021)

A Short Note on the Relationship of Information Gain and Eluder Dimension. - Kaixuan Huang; Sham M. Kakade; Jason D. Lee; Qi Lei (CoRR, 2021)

The Statistical Complexity of Interactive Decision Making. - Dylan J. Foster; Sham M. Kakade; Jian Qian; Alexander Rakhlin (CoRR, 2021)

On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points. - Chi Jin 001; Praneeth Netrapalli; Rong Ge 001; Sham M. Kakade; Michael I. Jordan (J. ACM, 2021)

On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift. - Alekh Agarwal; Sham M. Kakade; Jason D. Lee; Gaurav Mahajan (J. Mach. Learn. Res., 2021)

Anti-Concentrated Confidence Bonuses For Scalable Exploration. - Jordan T. Ash; Cyril Zhang; Surbhi Goel; Akshay Krishnamurthy; Sham M. Kakade (ICLR, 2022)

Multi-Stage Episodic Control for Strategic Exploration in Text Games. - Jens Tuyls; Shunyu Yao; Sham M. Kakade; Karthik Narasimhan (ICLR, 2022)

Inductive Biases and Variable Creation in Self-Attention Mechanisms. - Benjamin L. Edelman; Surbhi Goel; Sham M. Kakade; Cyril Zhang (ICML, 2022)

Sparsity in Partially Controllable Linear Systems. - Yonathan Efroni; Sham M. Kakade; Akshay Krishnamurthy; Cyril Zhang (ICML, 2022)

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 (ICML, 2022)

Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression. - Jingfeng Wu; Difan Zou; Vladimir Braverman; Quanquan Gu; Sham M. Kakade (ICML, 2022)

Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity. - Abhishek Gupta 004; Aldo Pacchiano; Yuexiang Zhai; Sham M. Kakade; Sergey Levine (NeurIPS, 2022)

Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit. - Boaz Barak; Benjamin L. Edelman; Surbhi Goel; Sham M. Kakade; Eran Malach; Cyril Zhang (NeurIPS, 2022)

Recurrent Convolutional Neural Networks Learn Succinct Learning Algorithms. - Surbhi Goel; Sham M. Kakade; Adam Kalai; Cyril Zhang (NeurIPS, 2022)

Matryoshka Representation Learning. - Aditya Kusupati; Gantavya Bhatt; Aniket Rege; Matthew Wallingford; Aditya Sinha; Vivek Ramanujan; William Howard-Snyder; Kaifeng Chen; Sham M. Kakade; Prateek Jain 002; Ali Farhadi (NeurIPS, 2022)

The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift. - Jingfeng Wu; Difan Zou; Vladimir Braverman; Quanquan Gu; Sham M. Kakade (NeurIPS, 2022)

Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime. - Difan Zou; Jingfeng Wu; Vladimir Braverman; Quanquan Gu; Sham M. Kakade (NeurIPS, 2022)

A Sharp Characterization of Linear Estimators for Offline Policy Evaluation. - Juan C. Perdomo; Akshay Krishnamurthy; Peter L. Bartlett; Sham M. Kakade (CoRR, 2022)

Matryoshka Representations for Adaptive Deployment. - Aditya Kusupati; Gantavya Bhatt; Aniket Rege; Matthew Wallingford; Aditya Sinha; Vivek Ramanujan; William Howard-Snyder; Kaifeng Chen; Sham M. Kakade; Prateek Jain 002; Ali Farhadi (CoRR, 2022)

Robust Aggregation for Federated Learning. - Krishna Pillutla; Sham M. Kakade; Zaïd Harchaoui (IEEE Trans. Signal Process., 2022)

Learning Hidden Markov Models Using Conditional Samples. - Gaurav Mahajan; Sham M. Kakade; Akshay Krishnamurthy; Cyril Zhang (COLT, 2023)

The Role of Coverage in Online Reinforcement Learning. - Tengyang Xie; Dylan J. Foster; Yu Bai 017; Nan Jiang 008; Sham M. Kakade (ICLR, 2023)

Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games. - Dylan J. Foster; Noah Golowich; Sham M. Kakade (ICML, 2023)

On Provable Copyright Protection for Generative Models. - Nikhil Vyas 001; Sham M. Kakade; Boaz Barak (ICML, 2023)

Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron. - Jingfeng Wu; Difan Zou; Zixiang Chen; Vladimir Braverman; Quanquan Gu; Sham M. Kakade (ICML, 2023)

Pareto Frontiers in Deep Feature Learning: Data, Compute, Width, and Luck. - Benjamin L. Edelman; Surbhi Goel; Sham M. Kakade; Eran Malach; Cyril Zhang (NeurIPS, 2023)

AdANNS: A Framework for Adaptive Semantic Search. - Aniket Rege; Aditya Kusupati; Sharan Ranjit S; Alan Fan; Qingqing Cao; Sham M. Kakade; Prateek Jain 002; Ali Farhadi (NeurIPS, 2023)

Modified Gauss-Newton Algorithms under Noise. - Krishna Pillutla; Vincent Roulet; Sham M. Kakade; Zaïd Harchaoui (SSP, 2023)

Learning High-Dimensional Single-Neuron ReLU Networks with Finite Samples. - Jingfeng Wu; Difan Zou; Zixiang Chen; Vladimir Braverman; Quanquan Gu; Sham M. Kakade (CoRR, 2023)

Scaling Laws for Imitation Learning in NetHack. - Jens Tuyls; Dhruv Madeka; Kari Torkkola; Dean P. Foster; Karthik Narasimhan; Sham M. Kakade (CoRR, 2023)

Pareto Frontiers in Neural Feature Learning: Data, Compute, Width, and Luck. - Benjamin L. Edelman; Surbhi Goel; Sham M. Kakade; Eran Malach; Cyril Zhang (CoRR, 2023)

Learning an Inventory Control Policy with General Inventory Arrival Dynamics. - Sohrab Andaz; Carson Eisenach; Dhruv Madeka; Kari Torkkola; Randy Jia; Dean P. Foster; Sham M. Kakade (CoRR, 2023)

A Complete Characterization of Linear Estimators for Offline Policy Evaluation. - Juan C. Perdomo; Akshay Krishnamurthy; Peter L. Bartlett; Sham M. Kakade (J. Mach. Learn. Res., 2023)

Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity. - Kaiqing Zhang; Sham M. Kakade; Tamer Basar; Lin F. Yang (J. Mach. Learn. Res., 2023)

Benign Overfitting of Constant-Stepsize SGD for Linear Regression. - Difan Zou; Jingfeng Wu; Vladimir Braverman; Quanquan Gu; Sham M. Kakade (J. Mach. Learn. Res., 2023)

Feature emergence via margin maximization: case studies in algebraic tasks. - Depen Morwani; Benjamin L. Edelman; Costin-Andrei Oncescu; Rosie Zhao; Sham M. Kakade (ICLR, 2024)

Beyond Implicit Bias: The Insignificance of SGD Noise in Online Learning. - Nikhil Vyas 001; Depen Morwani; Rosie Zhao; Gal Kaplun; Sham M. Kakade; Boaz Barak (ICML, 2024)

Q-Probe: A Lightweight Approach to Reward Maximization for Language Models. - Kenneth Li 002; Samy Jelassi; Hugh Zhang; Sham M. Kakade; Martin Wattenberg; David Brandfonbrener (ICML, 2024)

Repeat After Me: Transformers are Better than State Space Models at Copying. - Samy Jelassi; David Brandfonbrener; Sham M. Kakade; Eran Malach (ICML, 2024)

A Study on the Calibration of In-context Learning. - Hanlin Zhang 002; Yifan Zhang 004; Yaodong Yu; Dhruv Madeka; Dean P. Foster; Eric P. Xing; Himabindu Lakkaraju; Sham M. Kakade (NAACL-HLT, 2024)

CoLoR-Filter: Conditional Loss Reduction Filtering for Targeted Language Model Pre-training. - David Brandfonbrener; Hanlin Zhang 002; Andreas Kirsch 002; Jonathan Richard Schwarz; Sham M. Kakade (NeurIPS, 2024)

MatFormer: Nested Transformer for Elastic Inference. - Devvrit; Sneha Kudugunta; Aditya Kusupati; Tim Dettmers; Kaifeng Chen; Inderjit S. Dhillon; Yulia Tsvetkov; Hanna Hajishirzi; Sham M. Kakade; Ali Farhadi; Prateek Jain 002 (NeurIPS, 2024)

Matching the Statistical Query Lower Bound for k-Sparse Parity Problems with Sign Stochastic Gradient Descent. - Yiwen Kou; Zixiang Chen; Quanquan Gu; Sham M. Kakade (NeurIPS, 2024)

DataComp-LM: In search of the next generation of training sets for language models. - Jeffrey Li; Alex Fang; Georgios Smyrnis; Maor Ivgi; Matt Jordan; Samir Yitzhak Gadre; Hritik Bansal; Etash Kumar Guha; Sedrick Scott Keh; Kushal Arora; Saurabh Garg; Rui Xin; et al. (NeurIPS, 2024)

Scaling Laws in Linear Regression: Compute, Parameters, and Data. - Licong Lin; Jingfeng Wu; Sham M. Kakade; Peter L. Bartlett; Jason D. Lee (NeurIPS, 2024)

Superposed Decoding: Multiple Generations from a Single Autoregressive Inference Pass. - Ethan Shen; Alan Fan; Sarah M. Pratt; Jae Sung Park; Matthew Wallingford; Sham M. Kakade; Ari Holtzman; Ranjay Krishna; Ali Farhadi; Aditya Kusupati (NeurIPS, 2024)

Transcendence: Generative Models Can Outperform The Experts That Train Them. - Edwin Zhang; Vincent Zhu; Naomi Saphra; Anat Kleiman; Benjamin L. Edelman; Milind Tambe; Sham M. Kakade; Eran Malach (NeurIPS, 2024)

Matching the Statistical Query Lower Bound for k-sparse Parity Problems with Stochastic Gradient Descent. - Yiwen Kou; Zixiang Chen; Quanquan Gu; Sham M. Kakade (CoRR, 2024)

Universal Length Generalization with Turing Programs. - Kaiying Hou; David Brandfonbrener; Sham M. Kakade; Samy Jelassi; Eran Malach (CoRR, 2024)

Deconstructing What Makes a Good Optimizer for Language Models. - Rosie Zhao; Depen Morwani; David Brandfonbrener; Nikhil Vyas 001; Sham M. Kakade (CoRR, 2024)

Multi-Agent Reinforcement Learning from Human Feedback: Data Coverage and Algorithmic Techniques. - Natalia Zhang; Xinqi Wang; Qiwen Cui; Runlong Zhou; Sham M. Kakade; Simon S. Du (CoRR, 2024)

SOAP: Improving and Stabilizing Shampoo using Adam. - Nikhil Vyas 001; Depen Morwani; Rosie Zhao; Itai Shapira; David Brandfonbrener; Lucas Janson; Sham M. Kakade (CoRR, 2024)

Neural Coordination and Capacity Control for Inventory Management. - Carson Eisenach; Udaya Ghai; Dhruv Madeka; Kari Torkkola; Dean P. Foster; Sham M. Kakade (CoRR, 2024)

From an Image to a Scene: Learning to Imagine the World from a Million 360 Videos. - Matthew Wallingford; Anand Bhattad; Aditya Kusupati; Vivek Ramanujan; Matt Deitke; Sham M. Kakade; Aniruddha Kembhavi; Roozbeh Mottaghi; Wei-Chiu Ma; Ali Farhadi (CoRR, 2024)

Koopman Spectrum Nonlinear Regulators and Efficient Online Learning. - Motoya Ohnishi; Isao Ishikawa; Kendall Lowrey; Masahiro Ikeda; Sham M. Kakade; Yoshinobu Kawahara (Trans. Mach. Learn. Res., 2024)

Scaling Laws for Imitation Learning in Single-Agent Games. - Jens Tuyls; Dhruv Madeka; Kari Torkkola; Dean P. Foster; Karthik R. Narasimhan; Sham M. Kakade (Trans. Mach. Learn. Res., 2024)

LoRA Soups: Merging LoRAs for Practical Skill Composition Tasks. - Akshara Prabhakar; Yuanzhi Li; Karthik Narasimhan; Sham M. Kakade; Eran Malach; Samy Jelassi (COLING (Industry), 2025)

SOAP: Improving and Stabilizing Shampoo using Adam for Language Modeling. - Nikhil Vyas 001; Depen Morwani; Rosie Zhao; Itai Shapira; David Brandfonbrener; Lucas Janson; Sham M. Kakade (ICLR, 2025)

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models. - Yuda Song 001; Hanlin Zhang 002; Carson Eisenach; Sham M. Kakade; Dean P. Foster; Udaya Ghai (ICLR, 2025)

Mixture of Parrots: Experts improve memorization more than reasoning. - Samy Jelassi; Clara Mohri; David Brandfonbrener; Alex Gu; Nikhil Vyas 001; Nikhil Anand; David Alvarez-Melis; Yuanzhi Li; Sham M. Kakade; Eran Malach (ICLR, 2025)

A New Perspective on Shampoo's Preconditioner. - Depen Morwani; Itai Shapira; Nikhil Vyas 001; Eran Malach; Sham M. Kakade; Lucas Janson (ICLR, 2025)

Flash Inference: Near Linear Time Inference for Long Convolution Sequence Models and Beyond. - Costin-Andrei Oncescu; Sanket Purandare; Stratos Idreos; Sham M. Kakade (ICLR, 2025)

Follow My Instruction and Spill the Beans: Scalable Data Extraction from Retrieval-Augmented Generation Systems. - Zhenting Qi; Hanlin Zhang 002; Eric P. Xing; Sham M. Kakade; Himabindu Lakkaraju (ICLR, 2025)

Eliminating Position Bias of Language Models: A Mechanistic Approach. - Ziqi Wang 003; Hanlin Zhang 002; Xiner Li; Kuan-Hao Huang; Chi Han; Shuiwang Ji; Sham M. Kakade; Hao Peng 009; Heng Ji 001 (ICLR, 2025)

How Does Critical Batch Size Scale in Pre-training? - Hanlin Zhang 002; Depen Morwani; Nikhil Vyas 001; Jingfeng Wu; Difan Zou; Udaya Ghai; Dean P. Foster; Sham M. Kakade (ICLR, 2025)

Deconstructing What Makes a Good Optimizer for Autoregressive Language Models. - Rosie Zhao; Depen Morwani; David Brandfonbrener; Nikhil Vyas 001; Sham M. Kakade (ICLR, 2025)

Loss-to-Loss Prediction: Scaling Laws for All Datasets. - David Brandfonbrener; Nikhil Anand; Nikhil Vyas 001; Eran Malach; Sham M. Kakade (Trans. Mach. Learn. Res., 2025)

Soup to go: mitigating forgetting during continual learning with model averaging. - Anat Kleiman; Gintare Karolina Dziugaite; Jonathan Frankle; Sham M. Kakade; Mansheej Paul (CoRR, 2025)

Connections between Schedule-Free Optimizers, AdEMAMix, and Accelerated SGD Variants. - Depen Morwani; Nikhil Vyas 001; Hanlin Zhang 002; Sham M. Kakade (CoRR, 2025)

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 (CoRR, 2025)

The Role of Sparsity for Length Generalization in Transformers. - Noah Golowich; Samy Jelassi; David Brandfonbrener; Sham M. Kakade; Eran Malach (CoRR, 2025)

Distributional Scaling Laws for Emergent Capabilities. - Rosie Zhao; Tian Qin; David Alvarez-Melis; Sham M. Kakade; Naomi Saphra (CoRR, 2025)

Data-Efficient Multi-Agent Spatial Planning with LLMs. - Huangyuan Su; Aaron Walsman; Daniel Garces; Sham M. Kakade; Stephanie Gil (CoRR, 2025)

Echo Chamber: RL Post-training Amplifies Behaviors Learned in Pretraining. - Rosie Zhao; Alexandru Meterez; Sham M. Kakade; Cengiz Pehlevan; Samy Jelassi; Eran Malach (CoRR, 2025)

Interpreting the Linear Structure of Vision-language Model Embedding Spaces. - Isabel Papadimitriou; Huangyuan Su; Thomas Fel; Naomi Saphra; Sham M. Kakade; Stephanie Gil (CoRR, 2025)

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning. - Jikai Jin; Vasilis Syrgkanis; Sham M. Kakade; Hanlin Zhang 002 (CoRR, 2025)

A Simplified Analysis of SGD for Linear Regression with Weight Averaging. - Alexandru Meterez; Depen Morwani; Costin-Andrei Oncescu; Jingfeng Wu; Cengiz Pehlevan; Sham M. Kakade (CoRR, 2025)

EvoLM: In Search of Lost Language Model Training Dynamics. - Zhenting Qi; Fan Nie; Alexandre Alahi; James Zou 001; Himabindu Lakkaraju; Yilun Du; Eric P. Xing; Sham M. Kakade; Hanlin Zhang 002 (CoRR, 2025)

Inside you are many wolves: Using cognitive models to interpret value trade-offs in LLMs. - Sonia K. Murthy; Rosie Zhao; Jennifer Hu 001; Sham M. Kakade; Markus Wulfmeier; Peng Qian; Tomer D. Ullman (CoRR, 2025)

Characterization and Mitigation of Training Instabilities in Microscaling Formats. - Huangyuan Su; Mujin Kwun; Stephanie Gil; Sham M. Kakade; Nikhil Anand (CoRR, 2025)