
Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes
We study minimax optimal reinforcement learning in episodic factored Mar...
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SGD with shuffling: optimal rates without component convexity and large epoch requirements
We study withoutreplacement SGD for solving finitesum optimization pro...
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Stochastic Optimization with Nonstationary Noise
We investigate stochastic optimization problems under relaxed assumption...
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On Tight Convergence Rates of Withoutreplacement SGD
For solving finitesum optimization problems, SGD without replacement sa...
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Strength from Weakness: Fast Learning Using Weak Supervision
We study generalization properties of weakly supervised learning. That i...
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On Complexity of Finding Stationary Points of Nonsmooth Nonconvex Functions
We provide the first nonasymptotic analysis for finding stationary poin...
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From Nesterov's Estimate Sequence to Riemannian Acceleration
We propose the first global accelerated gradient method for Riemannian m...
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Why ADAM Beats SGD for Attention Models
While stochastic gradient descent (SGD) is still the de facto algorithm ...
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Metrics Induced by Quantum JensenShannonRenyí and Related Divergences
We study symmetric divergences on Hermitian positive definite matrices g...
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Nonconvex stochastic optimization on manifolds via Riemannian FrankWolfe methods
We study stochastic projectionfree methods for constrained optimization...
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Efficient Policy Learning for NonStationary MDPs under Adversarial Manipulation
A Markov Decision Process (MDP) is a popular model for reinforcement lea...
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Are deep ResNets provably better than linear predictors?
Recently, a residual network (ResNet) with a single residual block has b...
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Near Optimal Stratified Sampling
The performance of a machine learning system is usually evaluated by usi...
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Flexible Modeling of Diversity with Strongly LogConcave Distributions
Strongly logconcave (SLC) distributions are a rich class of discrete pr...
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Analysis of Gradient Clipping and Adaptive Scaling with a Relaxed Smoothness Condition
We provide a theoretical explanation for the fast convergence of gradien...
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Escaping Saddle Points with Adaptive Gradient Methods
Adaptive methods such as Adam and RMSProp are widely used in deep learni...
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DeepRBF Networks Revisited: Robust Classification with Rejection
One of the main drawbacks of deep neural networks, like many other class...
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RSPIDER: A Fast Riemannian Stochastic Optimization Algorithm with Curvature Independent Rate
We study smooth stochastic optimization problems on Riemannian manifolds...
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Finite sample expressive power of smallwidth ReLU networks
We study universal finite sample expressivity of neural networks, define...
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Efficiently testing local optimality and escaping saddles for ReLU networks
We provide a theoretical algorithm for checking local optimality and esc...
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Random Shuffling Beats SGD after Finite Epochs
A longstanding problem in the theory of stochastic gradient descent (SG...
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Towards Riemannian Accelerated Gradient Methods
We propose a Riemannian version of Nesterov's Accelerated Gradient algor...
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Direct RungeKutta Discretization Achieves Acceleration
We study gradientbased optimization methods obtained by directly discre...
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NonLinear Temporal Subspace Representations for Activity Recognition
Representations that can compactly and effectively capture the temporal ...
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Learning Determinantal Point Processes by Sampling Inferred Negatives
Determinantal Point Processes (DPPs) have attracted significant interest...
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A Critical View of Global Optimality in Deep Learning
We investigate the loss surface of deep linear and nonlinear neural netw...
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A Generic Approach for Escaping Saddle points
A central challenge to using firstorder methods for optimizing nonconve...
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Unsupervised robust nonparametric learning of hidden community properties
We consider learning of fundamental properties of communities in large n...
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Global optimality conditions for deep neural networks
We study the error landscape of deep linear and nonlinear neural network...
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An Alternative to EM for Gaussian Mixture Models: Batch and Stochastic Riemannian Optimization
We consider maximum likelihood estimation for Gaussian Mixture Models (G...
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Sequence Summarization Using Orderconstrained Kernelized Feature Subspaces
Representations that can compactly and effectively capture temporal evol...
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Polynomial Time Algorithms for Dual Volume Sampling
We study dual volume sampling, a method for selecting k columns from an ...
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Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling
We study probability measures induced by set functions with constraints....
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Stochastic FrankWolfe Methods for Nonconvex Optimization
We study FrankWolfe methods for nonconvex stochastic and finitesum opt...
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Geometric Mean Metric Learning
We revisit the task of learning a Euclidean metric from data. We approac...
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Fast Sampling for Strongly Rayleigh Measures with Application to Determinantal Point Processes
In this note we consider sampling from (nonhomogeneous) strongly Raylei...
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Kronecker Determinantal Point Processes
Determinantal Point Processes (DPPs) are probabilistic models over all s...
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Fast Stochastic Methods for Nonsmooth Nonconvex Optimization
We analyze stochastic algorithms for optimizing nonconvex, nonsmooth fin...
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Directional Statistics in Machine Learning: a Brief Review
The modern data analyst must cope with data encoded in various forms, ve...
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Combinatorial Topic Models using SmallVariance Asymptotics
Topic models have emerged as fundamental tools in unsupervised machine l...
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Stochastic Variance Reduction for Nonconvex Optimization
We study nonconvex finitesum problems and analyze stochastic variance r...
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Fast Incremental Method for Nonconvex Optimization
We analyze a fast incremental aggregated gradient method for optimizing ...
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Firstorder Methods for Geodesically Convex Optimization
Geodesic convexity generalizes the notion of (vector space) convexity to...
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Gauss quadrature for matrix inverse forms with applications
We present a framework for accelerating a spectrum of machine learning a...
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Diversity Networks: Neural Network Compression Using Determinantal Point Processes
We introduce Divnet, a flexible technique for learning networks with div...
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AdaDelay: Delay Adaptive Distributed Stochastic Convex Optimization
We study distributed stochastic convex optimization under the delayed gr...
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Riemannian Dictionary Learning and Sparse Coding for Positive Definite Matrices
Data encoded as symmetric positive definite (SPD) matrices frequently ar...
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Manifold Optimization for Gaussian Mixture Models
We take a new look at parameter estimation for Gaussian Mixture Models (...
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On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants
We study optimization algorithms based on variance reduction for stochas...
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Convex Optimization for Parallel Energy Minimization
Energy minimization has been an intensely studied core problem in comput...
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Suvrit Sra
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Researcher at Massachusetts Institute of Technology (MIT), Cofounder; Chief AI Officer at macroeyes