Search Results for author: Adarsh Barik

Found 10 papers, 0 papers with code

Sparse Mixed Linear Regression with Guarantees: Taming an Intractable Problem with Invex Relaxation

no code implementations2 Jun 2022 Adarsh Barik, Jean Honorio

Since the data is unlabeled, our task is not only to figure out a good approximation of the regression parameter vectors but also to label the dataset correctly.

Information-Theoretic Bounds for Integral Estimation

no code implementations19 Feb 2021 Donald Q. Adams, Adarsh Barik, Jean Honorio

For functions with nonzero fourth derivatives, the Gaussian Quadrature method achieves an upper bound which is not tight with the information-theoretic lower bound.

A Simple Unified Framework for High Dimensional Bandit Problems

no code implementations18 Feb 2021 Wenjie Li, Adarsh Barik, Jean Honorio

Stochastic high dimensional bandit problems with low dimensional structures are useful in different applications such as online advertising and drug discovery.

Drug Discovery

Exact Support Recovery in Federated Regression with One-shot Communication

no code implementations22 Jun 2020 Adarsh Barik, Jean Honorio

Federated learning provides a framework to address the challenges of distributed computing, data ownership and privacy over a large number of distributed clients with low computational and communication capabilities.

Distributed Computing Federated Learning +1

Provable Sample Complexity Guarantees for Learning of Continuous-Action Graphical Games with Nonparametric Utilities

no code implementations1 Apr 2020 Adarsh Barik, Jean Honorio

In this paper, we study the problem of learning the exact structure of continuous-action games with non-parametric utility functions.

Provable Computational and Statistical Guarantees for Efficient Learning of Continuous-Action Graphical Games

no code implementations8 Nov 2019 Adarsh Barik, Jean Honorio

We propose a $\ell_{12}-$ block regularized method which recovers a graphical game, whose Nash equilibria are the $\epsilon$-Nash equilibria of the game from which the data was generated (true game).

Learning Bayesian Networks with Low Rank Conditional Probability Tables

no code implementations NeurIPS 2019 Adarsh Barik, Jean Honorio

In this paper, we provide a method to learn the directed structure of a Bayesian network using data.

Learning discrete Bayesian networks in polynomial time and sample complexity

no code implementations12 Mar 2018 Adarsh Barik, Jean Honorio

The problem is NP-hard in general but we show that under certain conditions we can recover the true structure of a Bayesian network with sufficient number of samples.

Information Theoretic Limits for Linear Prediction with Graph-Structured Sparsity

no code implementations26 Jan 2017 Adarsh Barik, Jean Honorio, Mohit Tawarmalani

We analyze the necessary number of samples for sparse vector recovery in a noisy linear prediction setup.

General Classification

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