Search Results for author: Viswanath Nagarajan

Found 6 papers, 1 papers with code

Semi-Bandit Learning for Monotone Stochastic Optimization

no code implementations24 Dec 2023 Arpit Agarwal, Rohan Ghuge, Viswanath Nagarajan

Stochastic optimization is a widely used approach for optimization under uncertainty, where uncertain input parameters are modeled by random variables.

Stochastic Optimization

Optimal Decision Tree with Noisy Outcomes

1 code implementation NeurIPS 2019 Su Jia, Fatemeh Navidi, Viswanath Nagarajan, R. Ravi

In pool-based active learning, the learner is given an unlabeled data set and aims to efficiently learn the unknown hypothesis by querying the labels of the data points.

Active Learning

An Asymptotically Optimal Batched Algorithm for the Dueling Bandit Problem

no code implementations25 Sep 2022 Arpit Agarwal, Rohan Ghuge, Viswanath Nagarajan

}$ We answer this in the affirmative $\textit{under the Condorcet condition}$, a standard setting of the $K$-armed dueling bandit problem.

Recommendation Systems

Minimum Cost Adaptive Submodular Cover

no code implementations17 Aug 2022 Yubing Cui, Viswanath Nagarajan

We consider the problem of minimum cost cover of adaptive-submodular functions, and provide a 4(ln Q+1)-approximation algorithm, where Q is the goal value.

Batched Dueling Bandits

no code implementations22 Feb 2022 Arpit Agarwal, Rohan Ghuge, Viswanath Nagarajan

The $K$-armed dueling bandit problem, where the feedback is in the form of noisy pairwise comparisons, has been widely studied.

Recommendation Systems

Adaptive Submodular Ranking and Routing

no code implementations5 Jun 2016 Fatemeh Navidi, Prabhanjan Kambadur, Viswanath Nagarajan

We obtain a logarithmic factor approximation algorithm for this adaptive ranking problem, which is the best possible (unless P=NP).

Active Learning

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