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Found 5 papers, 1 papers with code
Informed Steiner Trees: Sampling and Pruning for Multi-Goal Path Finding in High Dimensions
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.
Learnable and Instance-Robust Predictions for Online Matching, Flows and Load Balancing
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.
Optimal Decision Tree with Noisy Outcomes
Our new approximation algorithms provide guarantees that are nearly best-possible and work for the general case of a large number of noisy outcomes per test or per hypothesis where the performance degrades smoothly with this number.
Stretching the Effectiveness of MLE from Accuracy to Bias for Pairwise Comparisons
We show that the MLE incurs a suboptimal rate in terms of bias.
A new system-wide diversity measure for recommendations with efficient algorithms
(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.
