
Parallelizing Contextual Linear Bandits
Standard approaches to decisionmaking under uncertainty focus on sequen...
read it

Optimal Mean Estimation without a Variance
We study the problem of heavytailed mean estimation in settings where t...
read it

Optimal Robust Linear Regression in Nearly Linear Time
We study the problem of highdimensional robust linear regression where ...
read it

On the Theory of Transfer Learning: The Importance of Task Diversity
We provide new statistical guarantees for transfer learning via represen...
read it

Provable MetaLearning of Linear Representations
Metalearning, or learningtolearn, seeks to design algorithms that can...
read it

Algorithms for HeavyTailed Statistics: Regression, Covariance Estimation, and Beyond
We study efficient algorithms for linear regression and covariance estim...
read it

Debiasing Linear Prediction
Standard methods in supervised learning separate training and prediction...
read it

RaoBlackwellized Stochastic Gradients for Discrete Distributions
We wish to compute the gradient of an expectation over a finite or count...
read it

Averaging Stochastic Gradient Descent on Riemannian Manifolds
We consider the minimization of a function defined on a Riemannian manif...
read it

Stochastic Cubic Regularization for Fast Nonconvex Optimization
This paper proposes a stochastic variant of a classic algorithmthe cu...
read it

Lost Relatives of the Gumbel Trick
The Gumbel trick is a method to sample from a discrete probability distr...
read it

Magnetic Hamiltonian Monte Carlo
Hamiltonian Monte Carlo (HMC) exploits Hamiltonian dynamics to construct...
read it

A LinearTime Particle Gibbs Sampler for Infinite Hidden Markov Models
Infinite Hidden Markov Models (iHMM's) are an attractive, nonparametric ...
read it
Nilesh Tripuraneni
is this you? claim profile