no code implementations • 30 Nov 2018 • Arda Antikacioglu, Tanvi Bajpai, R. Ravi
(2) In the case of disjoint item categories and user types, we show that the resulting problems can be solved exactly in polynomial time, by a reduction to a minimum cost flow problem.
no code implementations • 10 Jun 2019 • Jing-Yan Wang, Nihar B. Shah, R. Ravi
We show that the MLE incurs a suboptimal rate in terms of bias.
no code implementations • 23 Nov 2020 • Thomas Lavastida, Benjamin Moseley, R. Ravi, Chenyang Xu
Instance robustness ensures that the prediction is robust to modest changes in the problem input, where the measure of the change may be problem specific.
no code implementations • 9 May 2022 • Nikhil Chandak, Kenny Chour, Sivakumar Rathinam, R. Ravi
We interleave sampling based motion planning methods with pruning ideas from minimum spanning tree algorithms to develop a new approach for solving a Multi-Goal Path Finding (MGPF) problem in high dimensional spaces.
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 • 23 Dec 2023 • Su Jia, Andrew Li, R. Ravi, Nishant Oli, Paul Duff, Ian Anderson
We aim to minimize the loss due to not knowing the mean rewards, averaged over instances generated from a given prior distribution.
no code implementations • 23 Dec 2023 • Su Jia, Andrew Li, R. Ravi
Without monotonicity, the minimax regret is $\tilde O(n^{2/3})$ for the Lipschitz demand family and $\tilde O(n^{1/2})$ for a general class of parametric demand models.