Search Results for author: R. Ravi

Found 7 papers, 1 papers with code

A new system-wide diversity measure for recommendations with efficient algorithms

no code implementations30 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.

Recommendation Systems

Learnable and Instance-Robust Predictions for Online Matching, Flows and Load Balancing

no code implementations23 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.

Informed Steiner Trees: Sampling and Pruning for Multi-Goal Path Finding in High Dimensions

no code implementations9 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.

Motion Planning

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

Short-lived High-volume Multi-A(rmed)/B(andits) Testing

no code implementations23 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.

Markdown Pricing Under an Unknown Parametric Demand Model

no code implementations23 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.

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