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).
no code implementations • 6 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.
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.
1 code implementation • NeurIPS 2019 • Wenbo Ren, Jia Liu, Ness B. Shroff
This paper studies the problem of finding the exact ranking from noisy comparisons.
no code implementations • 28 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.
no code implementations • 8 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.