Search Results for author: Soumya Basu

Found 15 papers, 1 papers with code

Generalization Properties of Retrieval-based Models

no code implementations6 Oct 2022 Soumya Basu, Ankit Singh Rawat, Manzil Zaheer

The second class of retrieval-based approaches we explore learns a global model using kernel methods to directly map an input instance and retrieved examples to a prediction, without explicitly solving a local learning task.

Protein Folding Retrieval

No Regrets for Learning the Prior in Bandits

no code implementations NeurIPS 2021 Soumya Basu, Branislav Kveton, Manzil Zaheer, Csaba Szepesvári

We propose ${\tt AdaTS}$, a Thompson sampling algorithm that adapts sequentially to bandit tasks that it interacts with.

Thompson Sampling

Episodic Bandits with Stochastic Experts

no code implementations7 Jul 2021 Nihal Sharma, Soumya Basu, Karthikeyan Shanmugam, Sanjay Shakkottai

The agent interacts with the environment over episodes, with each episode having different context distributions; this results in the `best expert' changing across episodes.

Combinatorial Blocking Bandits with Stochastic Delays

no code implementations22 May 2021 Alexia Atsidakou, Orestis Papadigenopoulos, Soumya Basu, Constantine Caramanis, Sanjay Shakkottai

Recent work has considered natural variations of the multi-armed bandit problem, where the reward distribution of each arm is a special function of the time passed since its last pulling.


Beyond $\log^2(T)$ Regret for Decentralized Bandits in Matching Markets

no code implementations12 Mar 2021 Soumya Basu, Karthik Abinav Sankararaman, Abishek Sankararaman

We design decentralized algorithms for regret minimization in the two-sided matching market with one-sided bandit feedback that significantly improves upon the prior works (Liu et al. 2020a, 2020b, Sankararaman et al. 2020).

Recoverability Landscape of Tree Structured Markov Random Fields under Symmetric Noise

1 code implementation17 Feb 2021 Ashish Katiyar, Soumya Basu, Vatsal Shah, Constantine Caramanis

Furthermore, we present a polynomial time, sample efficient algorithm that recovers the exact tree when this is possible, or up to the unidentifiability as promised by our characterization, when full recoverability is impossible.

On Generalization of Adaptive Methods for Over-parameterized Linear Regression

no code implementations28 Nov 2020 Vatsal Shah, Soumya Basu, Anastasios Kyrillidis, Sujay Sanghavi

In this paper, we aim to characterize the performance of adaptive methods in the over-parameterized linear regression setting.


Stochastic Linear Bandits with Protected Subspace

no code implementations2 Nov 2020 Advait Parulekar, Soumya Basu, Aditya Gopalan, Karthikeyan Shanmugam, Sanjay Shakkottai

We study a variant of the stochastic linear bandit problem wherein we optimize a linear objective function but rewards are accrued only orthogonal to an unknown subspace (which we interpret as a \textit{protected space}) given only zero-order stochastic oracle access to both the objective itself and protected subspace.

Dominate or Delete: Decentralized Competing Bandits in Serial Dictatorship

no code implementations26 Jun 2020 Abishek Sankararaman, Soumya Basu, Karthik Abinav Sankararaman

Online learning in a two-sided matching market, with demand side agents continuously competing to be matched with supply side (arms), abstracts the complex interactions under partial information on matching platforms (e. g. UpWork, TaskRabbit).

Contextual Blocking Bandits

no code implementations6 Mar 2020 Soumya Basu, Orestis Papadigenopoulos, Constantine Caramanis, Sanjay Shakkottai

Assuming knowledge of the context distribution and the mean reward of each arm-context pair, we cast the problem as an online bipartite matching problem, where the right-vertices (contexts) arrive stochastically and the left-vertices (arms) are blocked for a finite number of rounds each time they are matched.

Blocking Novel Concepts +1

On Under-exploration in Bandits with Mean Bounds from Confounded Data

no code implementations19 Feb 2020 Nihal Sharma, Soumya Basu, Karthikeyan Shanmugam, Sanjay Shakkottai

We study a variant of the multi-armed bandit problem where side information in the form of bounds on the mean of each arm is provided.

Blocking Bandits

no code implementations NeurIPS 2019 Soumya Basu, Rajat Sen, Sujay Sanghavi, Sanjay Shakkottai

We show that with prior knowledge of the rewards and delays of all the arms, the problem of optimizing cumulative reward does not admit any pseudo-polynomial time algorithm (in the number of arms) unless randomized exponential time hypothesis is false, by mapping to the PINWHEEL scheduling problem.

Blocking Product Recommendation +1

Learning Mixtures of Graphs from Epidemic Cascades

no code implementations ICML 2020 Jessica Hoffmann, Soumya Basu, Surbhi Goel, Constantine Caramanis

When the conditions are met, i. e., when the graphs are connected with at least three edges, we give an efficient algorithm for learning the weights of both graphs with optimal sample complexity (up to log factors).

Decentralization in Bitcoin and Ethereum Networks

no code implementations11 Jan 2018 Adem Efe Gencer, Soumya Basu, Ittay Eyal, Robbert van Renesse, Emin Gün Sirer

Blockchain-based cryptocurrencies have demonstrated how to securely implement traditionally centralized systems, such as currencies, in a decentralized fashion.

Cryptography and Security

Effective Evaluation using Logged Bandit Feedback from Multiple Loggers

no code implementations17 Mar 2017 Aman Agarwal, Soumya Basu, Tobias Schnabel, Thorsten Joachims

In this paper, we address the question of how to estimate the performance of a new target policy when we have log data from multiple historic policies.


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