no code implementations • 15 May 2023 • Lianke Qin, Zhao Song, Yitan Wang
We consider both the online and offline versions of the problem: in each iteration, the data set changes incrementally or is not changed, and a user can issue a query to maximize the function on a given subset of the data.
no code implementations • 20 Mar 2023 • Siyu Chen, Yitan Wang, Zhaoran Wang, Zhuoran Yang
We study the offline contextual bandit problem, where we aim to acquire an optimal policy using observational data.
no code implementations • 15 Oct 2022 • Zhao Song, Yitan Wang, Zheng Yu, Lichen Zhang
In this paper, we propose a novel sketching scheme for the first order method in large-scale distributed learning setting, such that the communication costs between distributed agents are saved while the convergence of the algorithms is still guaranteed.
no code implementations • 10 Aug 2022 • Yeqi Gao, Lianke Qin, Zhao Song, Yitan Wang
For a neural network of width $m$, $n$ input training data in $d$ dimension, it takes $\Omega(mnd)$ time cost per training iteration for the forward and backward computation.
no code implementations • 13 Oct 2020 • Jialu Zhang, Yitan Wang, Mark Santolucito, Ruzica Piskac
The decision tree is one of the most popular and classical machine learning models from the 1980s.
no code implementations • 17 May 2018 • Guangzeng Xie, Yitan Wang, Shuchang Zhou, Zhihua Zhang
In this paper we explore acceleration techniques for large scale nonconvex optimization problems with special focuses on deep neural networks.