
Optimal Robust Linear Regression in Nearly Linear Time
We study the problem of highdimensional robust linear regression where ...
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On Linear Stochastic Approximation: Finegrained PolyakRuppert and NonAsymptotic Concentration
We undertake a precise study of the asymptotic and nonasymptotic proper...
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On Thompson Sampling with Langevin Algorithms
Thompson sampling is a methodology for multiarmed bandit problems that ...
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SelfDistillation Amplifies Regularization in Hilbert Space
Knowledge distillation introduced in the deep learning context is a meth...
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Oracle lower bounds for stochastic gradient sampling algorithms
We consider the problem of sampling from a strongly logconcave density ...
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Sampling for Bayesian Mixture Models: MCMC with PolynomialTime Mixing
We study the problem of sampling from the power posterior distribution i...
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Hebbian Synaptic Modifications in Spiking Neurons that Learn
In this paper, we derive a new model of synaptic plasticity, based on re...
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Learning Nearoptimal Convex Combinations of Basis Models with Generalization Guarantees
The problem of learning an optimal convex combination of basis models ha...
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An Efficient Sampling Algorithm for Nonsmooth Composite Potentials
We consider the problem of sampling from a density of the form p(x) ∝(f...
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HighOrder Langevin Diffusion Yields an Accelerated MCMC Algorithm
We propose a Markov chain Monte Carlo (MCMC) algorithm based on thirdor...
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Bayesian Robustness: A Nonasymptotic Viewpoint
We study the problem of robustly estimating the posterior distribution f...
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Improved Bounds for Discretization of Langevin Diffusions: NearOptimal Rates without Convexity
We present an improved analysis of the EulerMaruyama discretization of ...
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Quantitative W_1 Convergence of LangevinLike Stochastic Processes with NonConvex Potential StateDependent Noise
We prove quantitative convergence rates at which discrete Langevinlike ...
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Benign Overfitting in Linear Regression
The phenomenon of benign overfitting is one of the key mysteries uncover...
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Langevin Monte Carlo without Smoothness
Langevin Monte Carlo (LMC) is an iterative algorithm used to generate sa...
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OSOM: A Simultaneously Optimal Algorithm for MultiArmed and Linear Contextual Bandits
We consider the stochastic linear (multiarmed) contextual bandit proble...
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Testing Markov Chains without Hitting
We study the problem of identity testing of markov chains. In this setti...
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Fast Mean Estimation with SubGaussian Rates
We propose an estimator for the mean of a random vector in R^d that can ...
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Quantitative Central Limit Theorems for Discrete Stochastic Processes
In this paper, we establish a generalization of the classical Central Li...
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LargeScale Markov Decision Problems via the Linear Programming Dual
We consider the problem of controlling a fully specified Markov decision...
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DerivativeFree Methods for Policy Optimization: Guarantees for Linear Quadratic Systems
We study derivativefree methods for policy optimization over the class ...
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GenOja: A Simple and Efficient Algorithm for Streaming Generalized Eigenvector Computation
In this paper, we study the problems of principal Generalized Eigenvecto...
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A simple parameterfree and adaptive approach to optimization under a minimal local smoothness assumption
We study the problem of optimizing a function under a budgeted number of...
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Best of many worlds: Robust model selection for online supervised learning
We introduce algorithms for online, fullinformation prediction that are...
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Sharp Convergence Rates for Langevin Dynamics in the Nonconvex Setting
We study the problem of sampling from a distribution where the negative ...
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Representing smooth functions as compositions of nearidentity functions with implications for deep network optimization
We show that any smooth biLipschitz h can be represented exactly as a c...
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Online learning with kernel losses
We present a generalization of the adversarial linear bandits framework,...
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Gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks
We analyze algorithms for approximating a function f(x) = Φ x mapping ^d...
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On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo
We provide convergence guarantees in Wasserstein distance for a variety ...
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Alternating minimization for dictionary learning with random initialization
We present theoretical guarantees for an alternating minimization algori...
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Acceleration and Averaging in Stochastic Mirror Descent Dynamics
We formulate and study a general family of (continuoustime) stochastic ...
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Underdamped Langevin MCMC: A nonasymptotic analysis
We study the underdamped Langevin diffusion when the log of the target d...
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Recovery Guarantees for Onehiddenlayer Neural Networks
In this paper, we consider regression problems with onehiddenlayer neu...
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RL^2: Fast Reinforcement Learning via Slow Reinforcement Learning
Deep reinforcement learning (deep RL) has been successful in learning so...
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HitandRun for Sampling and Planning in NonConvex Spaces
We propose the HitandRun algorithm for planning and sampling problems ...
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FLAG n' FLARE: Fast LinearlyCoupled Adaptive Gradient Methods
We consider first order gradient methods for effectively optimizing a co...
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Linear Programming for LargeScale Markov Decision Problems
We consider the problem of controlling a Markov decision process (MDP) w...
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Bounding Embeddings of VC Classes into Maximum Classes
One of the earliest conjectures in computational learning theorythe Sam...
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Online Learning in Markov Decision Processes with Adversarially Chosen Transition Probability Distributions
We study the problem of learning Markov decision processes with finite s...
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Oracle inequalities for computationally adaptive model selection
We analyze general model selection procedures using penalized empirical ...
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Randomized Smoothing for Stochastic Optimization
We analyze convergence rates of stochastic optimization procedures for n...
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Informationtheoretic lower bounds on the oracle complexity of stochastic convex optimization
Relative to the large literature on upper bounds on complexity of convex...
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Marginadaptive model selection in statistical learning
A classical condition for fast learning rates is the margin condition, f...
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