no code implementations • 10 Jul 2023 • Adarsh Barik, Suvrit Sra, Jean Honorio
Invex programs are a special kind of non-convex problems which attain global minima at every stationary point.
no code implementations • 22 Jun 2023 • Adarsh Barik, Jean Honorio
In this paper, we study problem of estimating a sparse regression vector with correct support in the presence of outlier samples.
no code implementations • 19 Aug 2022 • Deepak Maurya, Adarsh Barik, Jean Honorio
In this work, we propose a robust framework that employs adversarially robust training to safeguard the machine learning models against perturbed testing data.
no code implementations • 2 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.
no code implementations • NeurIPS 2021 • Adarsh Barik, Jean Honorio
To the best of our knowledge, this is the first invex relaxation for a combinatorial problem.
no code implementations • 19 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.
no code implementations • 18 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.
no code implementations • 22 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.
no code implementations • 1 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.
no code implementations • 8 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).
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.
no code implementations • 12 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.
no code implementations • 26 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.