Sham Kakade

Q: What Schools Are Affiliated With Sham Kakade

Sham Kakade is affiliated with the following schools: California Institute of Technology, University College London, University of Washington, Toyota Technological Institute, Harvard University, University of Pennsylvania

Sham Kakade's AcademicInfluence.com Rankings

Sham Kakade

Computer Science

#4647

World Rank

#4903

Historical Rank

Machine Learning

#977

World Rank

#992

Historical Rank

Artificial Intelligence

#1200

World Rank

#1222

Historical Rank

Database

#1855

World Rank

#1946

Historical Rank

computer-science Degrees

Download Badge

Computer Science

Sham Kakade's Degrees

PhD Computer Science University of Pennsylvania

Similar Degrees You Can Earn

Best Online PhD of Computer Science (Doctorates) 2025

Why Is Sham Kakade Influential?

(Suggest an Edit or Addition)

According to Wikipedia, Sham Machandranath Kakade is an American computer scientist. He is a Gordon McKay Professor in Computer Science at Harvard University, with a joint appointment in the Department of Statistics. He co-founded the Algorithmic Foundations of Data Science Institute.

(See a Problem?)

Sham Kakade's Published Works

Number of citations in a given year to any of this author's works

Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author

Published Works

Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design (2009) (1890)
Tensor decompositions for learning latent variable models (2012) (1050)
A Natural Policy Gradient (2001) (978)
Cover trees for nearest neighbor (2006) (867)
Opponent interactions between serotonin and dopamine (2002) (791)
Approximately Optimal Approximate Reinforcement Learning (2002) (785)
Stochastic Linear Optimization under Bandit Feedback (2008) (767)
Multi-view clustering via canonical correlation analysis (2009) (720)
How to Escape Saddle Points Efficiently (2017) (681)
Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting (2012) (674)
On the sample complexity of reinforcement learning. (2003) (615)
Meta-Learning with Implicit Gradients (2019) (496)
Learning and selective attention (2000) (461)
Dopamine: generalization and bonuses (2002) (434)
Multi-Label Prediction via Compressed Sensing (2009) (431)
A Spectral Algorithm for Learning Hidden Markov Models (2008) (423)
Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator (2018) (394)
A tail inequality for quadratic forms of subgaussian random vectors (2011) (373)
On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization (2008) (345)
A Method of Moments for Mixture Models and Hidden Markov Models (2012) (323)
Learning mixtures of spherical gaussians: moment methods and spectral decompositions (2012) (309)
Online Meta-Learning (2019) (294)
A Spectral Algorithm for Latent Dirichlet Allocation (2012) (294)
On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift (2019) (248)
Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes (2019) (241)
Robust Aggregation for Federated Learning (2019) (236)
Random Design Analysis of Ridge Regression (2011) (232)
Towards Generalization and Simplicity in Continuous Control (2017) (232)
Robust Matrix Decomposition With Sparse Corruptions (2011) (221)
Learning from Logged Implicit Exploration Data (2010) (221)
Stochastic Convex Optimization with Bandit Feedback (2011) (220)
Multi-view Regression Via Canonical Correlation Analysis (2007) (209)
The Price of Bandit Information for Online Optimization (2007) (208)
Regularization Techniques for Learning with Matrices (2009) (198)
Efficient bandit algorithms for online multiclass prediction (2008) (194)
Stochastic Subgradient Method Converges on Tame Functions (2018) (178)
Policy Search by Dynamic Programming (2003) (174)
Provably Efficient Maximum Entropy Exploration (2018) (173)
PACT: Privacy-Sensitive Protocols And Mechanisms for Mobile Contact Tracing (2020) (168)
Online Markov Decision Processes (2009) (167)
Online Control with Adversarial Disturbances (2019) (165)
Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control (2018) (162)
Learning Features of Music from Scratch (2016) (157)
On the Generalization Ability of Online Strongly Convex Programming Algorithms (2008) (156)
Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression (2011) (152)
Few-Shot Learning via Learning the Representation, Provably (2020) (152)
Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning? (2019) (152)
A tensor approach to learning mixed membership community models (2013) (147)
On the duality of strong convexity and strong smoothness : Learning applications and matrix regularization (2009) (140)
Optimal Dynamic Mechanism Design and the Virtual Pivot Mechanism (2013) (137)
Exploration in Metric State Spaces (2003) (136)
Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization (2015) (134)
Soft Threshold Weight Reparameterization for Learnable Sparsity (2020) (132)
FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs (2020) (127)
Towards Minimax Policies for Online Linear Optimization with Bandit Feedback (2012) (127)
Domain Adaptation with Coupled Subspaces (2011) (125)
Reinforcement Learning: Theory and Algorithms (2019) (122)
Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines (2018) (122)
The price of truthfulness for pay-per-click auctions (2009) (121)
A Tensor Spectral Approach to Learning Mixed Membership Community Models (2013) (120)
Model-Based Reinforcement Learning with a Generative Model is Minimax Optimal (2019) (116)
Parallelizing Stochastic Gradient Descent for Least Squares Regression: Mini-batching, Averaging, and Model Misspecification (2016) (116)
An Information Theoretic Framework for Multi-view Learning (2008) (115)
Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja's Algorithm (2016) (112)
Learning Mixtures of Gaussians in High Dimensions (2015) (111)
What are the Statistical Limits of Offline RL with Linear Function Approximation? (2020) (109)
Mind the Duality Gap: Logarithmic regret algorithms for online optimization (2008) (108)
High-Probability Regret Bounds for Bandit Online Linear Optimization (2008) (105)
On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points (2019) (104)
Correlated equilibria in graphical games (2003) (104)
Bilinear Classes: A Structural Framework for Provable Generalization in RL (2021) (100)
Leveraging archival video for building face datasets (2007) (100)
Playing games with approximation algorithms (2007) (100)
Competing with the Empirical Risk Minimizer in a Single Pass (2014) (96)
Economic Properties of Social Networks (2004) (96)
The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure (2019) (95)
Accelerating Stochastic Gradient Descent for Least Squares Regression (2017) (89)
On the Insufficiency of Existing Momentum Schemes for Stochastic Optimization (2018) (89)
A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Norm (2019) (86)
Optimal Regularization Can Mitigate Double Descent (2020) (86)
Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity (2020) (84)
Acquisition and extinction in autoshaping. (2002) (83)
Experts in a Markov Decision Process (2004) (83)
Information Theoretic Regret Bounds for Online Nonlinear Control (2020) (82)
An Alternate Objective Function for Markovian Fields (2002) (82)
Two SVDs Suffice: Spectral decompositions for probabilistic topic modeling and latent Dirichlet allocation (2012) (81)
Information Consistency of Nonparametric Gaussian Process Methods (2008) (80)
PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning (2020) (80)
The Nonstochastic Control Problem (2019) (79)
Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent (2016) (78)
Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity (2009) (72)
Tail inequalities for sums of random matrices that depend on the intrinsic dimension (2012) (71)
Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis (2016) (70)
Deterministic calibration and Nash equilibrium (2004) (70)
The Implicit and Explicit Regularization Effects of Dropout (2020) (69)
Explaining Away in Weight Space (2000) (68)
Faster Eigenvector Computation via Shift-and-Invert Preconditioning (2016) (68)
Competitive algorithms for VWAP and limit order trading (2004) (68)
Learning Linear Bayesian Networks with Latent Variables (2012) (66)
The Statistical Complexity of Interactive Decision Making (2021) (64)
Stochastic Gradient Descent Escapes Saddle Points Efficiently (2019) (60)
Global Convergence of Policy Gradient Methods for Linearized Control Problems (2018) (60)
Graphical Economics (2004) (57)
Online Bounds for Bayesian Algorithms (2004) (57)
Convergence Rates of Active Learning for Maximum Likelihood Estimation (2015) (57)
Accelerating Stochastic Gradient Descent (2017) (54)
Locally Weighted Regression (2009) (54)
Applications of strong convexity--strong smoothness duality to learning with matrices (2009) (54)
Spectral Methods for Learning Multivariate Latent Tree Structure (2011) (53)
Invariances and Data Augmentation for Supervised Music Transcription (2017) (51)
When are overcomplete topic models identifiable? uniqueness of tensor tucker decompositions with structured sparsity (2013) (51)
Learning Mixtures of Tree Graphical Models (2012) (49)
Reinforcement Learning in POMDPs Without Resets (2005) (48)
Gaussian Process Bandits without Regret: An Experimental Design Approach (2009) (44)
Tensor Decompositions for Learning Latent Variable Models (A Survey for ALT) (2015) (43)
Meta-learning for mixed linear regression (2020) (42)
A risk comparison of ordinary least squares vs ridge regression (2011) (42)
Optimizing Average Reward Using Discounted Rewards (2001) (41)
Parallelizing Stochastic Approximation Through Mini-Batching and Tail-Averaging (2016) (41)
Is Long Horizon Reinforcement Learning More Difficult Than Short Horizon Reinforcement Learning? (2020) (40)
Robust Shift-and-Invert Preconditioning: Faster and More Sample Efficient Algorithms for Eigenvector Computation (2015) (39)
Identifiability and Unmixing of Latent Parse Trees (2012) (39)
Robust and Differentially Private Mean Estimation (2021) (38)
How Important is the Train-Validation Split in Meta-Learning? (2020) (38)
An Exponential Lower Bound for Linearly-Realizable MDPs with Constant Suboptimality Gap (2021) (36)
Variance Reduction Methods for Sublinear Reinforcement Learning (2018) (34)
Provable Representation Learning for Imitation Learning via Bi-level Optimization (2020) (34)
Provably Correct Automatic Subdifferentiation for Qualified Programs (2018) (33)
Benign Overfitting of Constant-Stepsize SGD for Linear Regression (2021) (32)
Sample-Efficient Reinforcement Learning of Undercomplete POMDPs (2020) (31)
Outpacing the Virus: Digital Response to Containing the Spread of COVID-19 while Mitigating Privacy Risks (2020) (31)
Understanding Contrastive Learning Requires Incorporating Inductive Biases (2022) (29)
A Linear Dynamical System Model for Text (2015) (28)
A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares) (2017) (28)
Computing Matrix Squareroot via Non Convex Local Search (2015) (28)
Least Squares Revisited: Scalable Approaches for Multi-class Prediction (2013) (27)
Dopamine Bonuses (2000) (27)
Revisiting the Polyak step size (2019) (27)
Maximum Entropy Correlated Equilibria (2007) (26)
Acquisition and extinction in autoshaping (2002) (26)
Global Convergence of Non-Convex Gradient Descent for Computing Matrix Squareroot (2015) (25)
An Analysis of Random Design Linear Regression (2011) (25)
Robust Meta-learning for Mixed Linear Regression with Small Batches (2020) (24)
Maximum Likelihood Estimation for Learning Populations of Parameters (2019) (24)
An Optimal Dynamic Mechanism for Multi-Armed Bandit Processes (2010) (24)
Robust Matrix Decomposition with Outliers (2010) (23)
Worst-Case Bounds for Gaussian Process Models (2005) (23)
Gone Fishing: Neural Active Learning with Fisher Embeddings (2021) (23)
From Batch to Transductive Online Learning (2005) (22)
Dimension-free tail inequalities for sums of random matrices (2011) (22)
On the Optimality of Sparse Model-Based Planning for Markov Decision Processes (2019) (22)
Instabilities of Offline RL with Pre-Trained Neural Representation (2021) (22)
Calibration, Entropy Rates, and Memory in Language Models (2019) (20)
Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit (2022) (20)
Prediction with a short memory (2016) (20)
Inductive Biases and Variable Creation in Self-Attention Mechanisms (2021) (19)
Is Long Horizon RL More Difficult Than Short Horizon RL? (2020) (18)
Learning Overcomplete HMMs (2017) (18)
The Value of Observation for Monitoring Dynamic Systems (2007) (17)
Leverage Score Sampling for Faster Accelerated Regression and ERM (2017) (17)
(weak) Calibration is Computationally Hard (2012) (16)
Learning Gaussian Mixture Models: Moment Methods and Spectral Decompositions (2012) (15)
Minimal Realization Problems for Hidden Markov Models (2014) (14)
A Smoother Way to Train Structured Prediction Models (2019) (14)
Matching Matrix Bernstein with Little Memory: Near-Optimal Finite Sample Guarantees for Oja's Algorithm (2016) (13)
Planning in POMDPs Using Multiplicity Automata (2005) (12)
The Benefits of Implicit Regularization from SGD in Least Squares Problems (2021) (12)
Acquisition in Autoshaping (1999) (12)
Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression (2021) (12)
The Central Limit Theorem (2003) (11)
On Nonconvex Optimization for Machine Learning (2021) (11)
Recovering Structured Probability Matrices (2016) (11)
Rethinking learning rate schedules for stochastic optimization (2018) (11)
Trading in Markovian Price Models (2005) (10)
Optimal Gradient-based Algorithms for Non-concave Bandit Optimization (2021) (9)
Coupled Recurrent Models for Polyphonic Music Composition (2018) (9)
Super-Resolution Off the Grid (2015) (8)
(In)Stability properties of limit order dynamics (2006) (8)
Learning Topic Models and Latent Bayesian Networks Under Expansion Constraints (2012) (8)
Minimal realization problem for Hidden Markov Models (2014) (8)
Learning High-Dimensional Mixtures of Graphical Models (2012) (7)
Sparsity in Partially Controllable Linear Systems (2021) (7)
Going Beyond Linear RL: Sample Efficient Neural Function Approximation (2021) (7)
Competitive Analysis of the Explore/Exploit Tradeoff (2002) (6)
LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes (2021) (6)
A Short Note on the Relationship of Information Gain and Eluder Dimension (2021) (6)
Multi-Stage Episodic Control for Strategic Exploration in Text Games (2022) (5)
Anti-Concentrated Confidence Bonuses for Scalable Exploration (2021) (5)
Calibration via Regression (2006) (5)
A Sharp Characterization of Linear Estimators for Offline Policy Evaluation (2022) (5)
An Optimal Algorithm for Linear Bandits (2011) (4)
Koopman Spectrum Nonlinear Regulator and Provably Efficient Online Learning (2021) (4)
The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift (2022) (4)
Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity (2022) (4)
Exponentiated) Stochastic Gradient Descent for L1 Constrained Problems (2008) (3)
The Role of Coverage in Online Reinforcement Learning (2022) (3)
Canonical Correlation Analysis for Analyzing Sequences of Medical Billing Codes (2016) (3)
Analysis of a randomized approximation scheme for matrix multiplication (2012) (3)
Optimal Estimation of Change in a Population of Parameters (2019) (3)
Matryoshka Representations for Adaptive Deployment (2022) (2)
Approximation Algorithms Going Online (2007) (2)
Optimal dynamic mechanism design via a virtual VCG mechanism (2011) (2)
On-line Markov Decision Processes (2006) (2)
Matryoshka Representation Learning (2022) (2)
Opponent interactions between serotonin and dopamine for classical and operant conditioning (2000) (2)
Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime (2022) (1)
Lecture Notes on the Theory of Reinforcement Learning (2019) (1)
Lecture Notes on the Theory of Reinforcement Learning (2019) (1)
Domain Adaptation: Overfitting and Small Sample Statistics (2011) (1)
Domain Adaptation: A Small Sample Statistical Approach (2012) (1)
A Spectral Algorithm for Latent Dirichlet Allocation (2014) (1)
O C ] 1 M ay 2 01 9 Revisiting the Polyak step size (2019) (0)
Stochastic Subgradient Method Converges on Tame Functions (2019) (0)
L EARNING F EATURES OF M USIC FROM S CRATCH (2017) (0)
ACTION-DEPENDENT FACTORIZED BASELINES (2018) (0)
When are Overcomplete Representations Identifiable? Uniqueness of Tensor Decompositions Under Expansion Constraints (2013) (0)
Semi-Supervised, Dimensionality Reduction via Canonical Correlation Analysis (2007) (0)
24th Annual Conference on Learning Theory, 9-11 June 2011, Budapest, Hungary (2011) (0)
Dimensionality Reduction and Learning: Ridge Regression vs. PCA (2010) (0)
Lectures on the Theory of Reinforcement Learning (2019) (0)
Lectures on the Theory of Reinforcement Learning (2019) (0)
Guest editorial: special issue on learning theory (2010) (0)
L G ] 2 9 A ug 2 01 9 Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes (2019) (0)
Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games (2023) (0)
Clustering ; Single Linkage ; and Pairwise Distance Concentration (2010) (0)
37th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science, FSTTCS 2017, December 11-15, 2017, Kanpur, India (2018) (0)
PACT : P rivacy-Sensitive Protocols A nd Mechanisms for Mobile C ontact T racing (2020) (0)
Feature Selection , Empirical Risk Minimization , and The Orthogonal Case (2011) (0)
COLT 2011, 24th Annual Conference on Learning Theory- (2011) (0)
Learning High-Dimensional Single-Neuron ReLU Networks with Finite Samples (2023) (0)
Random Design Analysis of Ridge Regression (2014) (0)
Multi-Armed Bandits : Non-adaptive and Adaptive Sampling (2018) (0)
Empirical Process Theory and Oracle Inequalities (2011) (0)
A Complete Characterization of Linear Estimators for Offline Policy Evaluation (2022) (0)
1 Using Online Algorithms in a Batch Setting (2011) (0)
THE INSUFFICIENCY OF EXISTING MOMENTUM SCHEMES FOR S TOCHASTIC O PTIMIZATION (2018) (0)
Open Problem: Do Good Algorithms Necessarily Query Bad Points? (2019) (0)
Addressing Novel Sources of Bias for Change Detection on Large Social Networks (2019) (0)
Recurrent Convolutional Neural Networks Learn Succinct Learning Algorithms (2022) (0)
The Illusion of Change: Correcting for Biases in Change Inference for Sparse, Societal-Scale Data (2019) (0)
Game Theoretical Algorithms for Intelligent Networking (2005) (0)
L G ] 6 F eb 2 01 8 Recovering Structured Probability Matrices (2018) (0)
Tensor Decompositions for Learning Latent Variable Models Report Title (2013) (0)
Learning Hidden Markov Models Using Conditional Samples (2023) (0)
2-2008 Deterministic Calibration and Nash Equilibrium (2017) (0)

This paper list is powered by the following services:

Other Resources About Sham Kakade

What Schools Are Affiliated With Sham Kakade?

Sham Kakade is affiliated with the following schools:

Sham Kakade's Academic­Influence.com Rankings

Sham Kakade's Degrees

Similar Degrees You Can Earn

Why Is Sham Kakade Influential?

Sham Kakade's Published Works

Published Works

Other Resources About Sham Kakade

What Schools Are Affiliated With Sham Kakade?

Sham Kakade's AcademicInfluence.com Rankings