no code implementations • 16 May 2024 • Davin Choo, Themis Gouleakis, Chun Kai Ling, Arnab Bhattacharyya

We study the problem of online unweighted bipartite matching with $n$ offline vertices and $n$ online vertices where one wishes to be competitive against the optimal offline algorithm.

no code implementations • 13 May 2024 • Arnab Bhattacharyya, Sutanu Gayen, Philips George John, Sayantan Sen, N. V. Vinodchandran

This work establishes a novel link between the problem of PAC-learning high-dimensional graphical models and the task of (efficient) counting and sampling of graph structures, using an online learning framework.

no code implementations • 14 Mar 2024 • Vipul Arora, Arnab Bhattacharyya, Mathews Boban, Venkatesan Guruswami, Esty Kelman

Furthermore, we show that it is possible to have the run-time be independent of $1/\sigma$, at the cost of a higher sample complexity.

1 code implementation • 9 Feb 2024 • Yuhao Wang, Ming Gao, Wai Ming Tai, Bryon Aragam, Arnab Bhattacharyya

We develop optimal algorithms for learning undirected Gaussian trees and directed Gaussian polytrees from data.

no code implementations • 10 Oct 2023 • Davin Choo, Joy Qiping Yang, Arnab Bhattacharyya, Clément L. Canonne

We establish finite-sample guarantees for efficient proper learning of bounded-degree polytrees, a rich class of high-dimensional probability distributions and a subclass of Bayesian networks, a widely-studied type of graphical model.

no code implementations • 17 Sep 2023 • Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S. Meel, Dimitrios Myrisiotis, A. Pavan, N. V. Vinodchandran

In particular, we present an efficient, structure-preserving reduction from relative approximation of TV distance to probabilistic inference over directed graphical models.

1 code implementation • 31 May 2023 • Davin Choo, Themis Gouleakis, Arnab Bhattacharyya

When the advice is a DAG $G$, we design an adaptive search algorithm to recover $G^*$ whose intervention cost is at most $O(\max\{1, \log \psi\})$ times the cost for verifying $G^*$; here, $\psi$ is a distance measure between $G$ and $G^*$ that is upper bounded by the number of variables $n$, and is exactly 0 when $G=G^*$.

no code implementations • 13 Apr 2023 • Vipul Arora, Arnab Bhattacharyya, Clément L. Canonne, Joy Qiping Yang

This paper considers the problem of testing the maximum in-degree of the Bayes net underlying an unknown probability distribution $P$ over $\{0, 1\}^n$, given sample access to $P$.

no code implementations • 10 Feb 2023 • Jonas Wildberger, Siyuan Guo, Arnab Bhattacharyya, Bernhard Schölkopf

Modern machine learning approaches excel in static settings where a large amount of i. i. d.

1 code implementation • 1 Jan 2023 • Thanh Vinh Vo, Arnab Bhattacharyya, Young Lee, Tze-Yun Leong

We propose a new causal inference framework to learn causal effects from multiple, decentralized data sources in a federated setting.

4 code implementations • 30 Jun 2022 • Davin Choo, Kirankumar Shiragur, Arnab Bhattacharyya

Our result is the first known algorithm that gives a non-trivial approximation guarantee to the verifying size on general unweighted graphs and with bounded size interventions.

no code implementations • 19 Apr 2022 • Arnab Bhattacharyya, Clément L. Canonne, Joy Qiping Yang

We study the following independence testing problem: given access to samples from a distribution $P$ over $\{0, 1\}^n$, decide whether $P$ is a product distribution or whether it is $\varepsilon$-far in total variation distance from any product distribution.

no code implementations • 25 Jul 2021 • Arnab Bhattacharyya, Sutanu Gayen, Saravanan Kandasamy, Vedant Raval, N. V. Vinodchandran

For sets $\mathbf{X},\mathbf{Y}\subseteq \mathbf{V}$, and setting ${\bf x}$ to $\mathbf{X}$, let $P_{\bf x}(\mathbf{Y})$ denote the interventional distribution on $\mathbf{Y}$ with respect to an intervention ${\bf x}$ to variables ${\bf x}$.

1 code implementation • 22 Jul 2021 • Arnab Bhattacharyya, Davin Choo, Rishikesh Gajjala, Sutanu Gayen, Yuhao Wang

We also study a couple of new algorithms for the problem: - BatchAvgLeastSquares takes the average of several batches of least squares solutions at each node, so that one can interpolate between the batch size and the number of batches.

1 code implementation • 17 Jun 2021 • Yuhao Wang, Arnab Bhattacharyya

AMP models are described by DAGs on chain components which themselves are undirected graphs.

no code implementations • 29 Dec 2020 • Arnab Bhattacharyya, Sutanu Gayen, Saravanan Kandasamy, N. V. Vinodchandran

We study the problems of identity and closeness testing of $n$-dimensional product distributions.

no code implementations • 27 Nov 2020 • R K Ghosh, Vinay R, Arnab Bhattacharyya

A vehicle's fuel consumption depends on its type, the speed, the condition, and the gradients of the road on which it is moving.

no code implementations • 9 Nov 2020 • Arnab Bhattacharyya, Sutanu Gayen, Eric Price, N. V. Vinodchandran

For a distribution $P$ on $\Sigma^n$ and a tree $T$ on $n$ nodes, we say $T$ is an $\varepsilon$-approximate tree for $P$ if there is a $T$-structured distribution $Q$ such that $D(P\;||\;Q)$ is at most $\varepsilon$ more than the best possible tree-structured distribution for $P$.

no code implementations • 17 Jun 2020 • Arnab Bhattacharyya, Rathin Desai, Sai Ganesh Nagarajan, Ioannis Panageas

We show that ${\mu}$ and ${\Sigma}$ can be estimated with error $\epsilon$ in the Frobenius norm, using $\tilde{O}\left(\frac{\textrm{nz}({\Sigma}^{-1})}{\epsilon^2}\right)$ samples from a truncated $\mathcal{N}({\mu},{\Sigma})$ and having access to a membership oracle for $S$.

no code implementations • NeurIPS 2020 • Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S. Meel, N. V. Vinodchandran

We design efficient distance approximation algorithms for several classes of structured high-dimensional distributions.

no code implementations • ICML 2020 • Arnab Bhattacharyya, Sutanu Gayen, Saravanan Kandasamy, Ashwin Maran, N. V. Vinodchandran

Assuming that $G$ has bounded in-degree, bounded c-components ($k$), and that the observational distribution is identifiable and satisfies certain strong positivity condition, we give an algorithm that takes $m=\tilde{O}(n\epsilon^{-2})$ samples from $P$ and $O(mn)$ time, and outputs with high probability a description of a distribution $\hat{P}$ such that $d_{\mathrm{TV}}(P_x, \hat{P}) \leq \epsilon$, and: 1.

no code implementations • NeurIPS 2018 • Jayadev Acharya, Arnab Bhattacharyya, Constantinos Daskalakis, Saravanan Kandasamy

We consider testing and learning problems on causal Bayesian networks as defined by Pearl (Pearl, 2009).

no code implementations • 19 Aug 2015 • Arnab Bhattacharyya, Palash Dey

We investigate the problem of winner determination from computational social choice theory in the data stream model.

no code implementations • 18 Feb 2015 • Arnab Bhattacharyya, Ameet Gadekar, Ninad Rajgopal

We improve the previous best result of Buhrman et al. by an $\exp(k)$ factor in the time complexity.

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