no code implementations • 24 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.
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.
no code implementations • 25 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.
no code implementations • 17 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.
no code implementations • 22 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.
no code implementations • 5 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).