Towards Optimal Algorithms for Multi-Player Bandits without Collision Sensing Information

no code implementations • 24 Mar 2021 • Wei Huang, Richard Combes, Cindy Trinh

We propose a novel algorithm for multi-player multi-armed bandits without collision sensing information.

Multi-Armed Bandits

A High Performance, Low Complexity Algorithm for Multi-Player Bandits Without Collision Sensing Information

1 code implementation • 19 Feb 2021 • Cindy Trinh, Richard Combes

Motivated by applications in cognitive radio networks, we consider the decentralized multi-player multi-armed bandit problem, without collision nor sensing information.

MLPerf Mobile Inference Benchmark

1 code implementation • 3 Dec 2020 • Vijay Janapa Reddi, David Kanter, Peter Mattson, Jared Duke, Thai Nguyen, Ramesh Chukka, Ken Shiring, Koan-Sin Tan, Mark Charlebois, William Chou, Mostafa El-Khamy, Jungwook Hong, Tom St. John, Cindy Trinh, Michael Buch, Mark Mazumder, Relia Markovic, Thomas Atta, Fatih Cakir, Masoud Charkhabi, Xiaodong Chen, Cheng-Ming Chiang, Dave Dexter, Terry Heo, Gunther Schmuelling, Maryam Shabani, Dylan Zika

This paper presents the first industry-standard open-source machine learning (ML) benchmark to allow perfor mance and accuracy evaluation of mobile devices with different AI chips and software stacks.

Solving Bernoulli Rank-One Bandits with Unimodal Thompson Sampling

no code implementations • 6 Dec 2019 • Cindy Trinh, Emilie Kaufmann, Claire Vernade, Richard Combes

Stochastic Rank-One Bandits (Katarya et al, (2017a, b)) are a simple framework for regret minimization problems over rank-one matrices of arms.

Thompson Sampling