Search Results for author: Wenbo Ren

Found 6 papers, 3 papers with code

Sample Complexity Bounds for Active Ranking from Multi-wise Comparisons

1 code implementation NeurIPS 2021 Wenbo Ren, Jia Liu, Ness Shroff

Here, a multi-wise comparison takes $m$ items as input and returns a (noisy) result about the best item (the winner feedback) or the order of these items (the full-ranking feedback).

The Sample Complexity of Best-$k$ Items Selection from Pairwise Comparisons

1 code implementation ICML 2020 Wenbo Ren, Jia Liu, Ness B. Shroff

From a given set of items, the learner can make pairwise comparisons on every pair of items, and each comparison returns an independent noisy result about the preferred item.

Active Learning

Multi-Armed Bandits with Local Differential Privacy

no code implementations6 Jul 2020 Wenbo Ren, Xingyu Zhou, Jia Liu, Ness B. Shroff

To handle this dilemma, we adopt differential privacy and study the regret upper and lower bounds for MAB algorithms with a given LDP guarantee.

Multi-Armed Bandits

Exploring $k$ out of Top $ρ$ Fraction of Arms in Stochastic Bandits

no code implementations28 Oct 2018 Wenbo Ren, Jia Liu, Ness Shroff

Results in this paper provide up to $\rho n/k$ reductions compared with the "$k$-exploration" algorithms that focus on finding the (PAC) best $k$ arms out of $n$ arms.

PAC Ranking from Pairwise and Listwise Queries: Lower Bounds and Upper Bounds

no code implementations8 Jun 2018 Wenbo Ren, Jia Liu, Ness B. Shroff

For the PAC top-$k$ ranking problem, we derive a lower bound on the sample complexity (aka number of queries), and propose an algorithm that is sample-complexity-optimal up to an $O(\log(k+l)/\log{k})$ factor.

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