no code implementations • 31 Mar 2024 • Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Sihan Liu, Nikos Zarifis
We study the efficient learnability of low-degree polynomial threshold functions (PTFs) in the presence of a constant fraction of adversarial corruptions.
1 code implementation • 19 Dec 2023 • Sihan Liu, Yiwei Ma, Xiaoqing Zhang, Haowei Wang, Jiayi Ji, Xiaoshuai Sun, Rongrong Ji
Referring Remote Sensing Image Segmentation (RRSIS) is a new challenge that combines computer vision and natural language processing, delineating specific regions in aerial images as described by textual queries.
no code implementations • 22 Nov 2023 • Ilias Diakonikolas, Daniel M. Kane, Sihan Liu
Our main result is the first closeness tester for this problem with {\em sub-learning} sample complexity in any fixed dimension and a nearly-matching sample complexity lower bound.
no code implementations • 24 Oct 2023 • Daniel M. Kane, Ilias Diakonikolas, Hanshen Xiao, Sihan Liu
We note that if the algorithm is allowed to wait until time $T$ to report its estimate, this reduces to the well-studied problem of robust mean estimation.
no code implementations • 25 Feb 2023 • Daniel Kane, Sihan Liu, Shachar Lovett, Gaurav Mahajan, Csaba Szepesvári, Gellért Weisz
The rewards in this game are chosen such that if the learner achieves large reward, then the learner's actions can be used to simulate solving a variant of 3-SAT, where (a) each variable shows up in a bounded number of clauses (b) if an instance has no solutions then it also has no solutions that satisfy more than (1-$\epsilon$)-fraction of clauses.
no code implementations • 14 Jul 2022 • Clément L. Canonne, Ilias Diakonikolas, Daniel M. Kane, Sihan Liu
We investigate the problem of testing whether a discrete probability distribution over an ordered domain is a histogram on a specified number of bins.
no code implementations • 11 Feb 2022 • Daniel Kane, Sihan Liu, Shachar Lovett, Gaurav Mahajan
In this work, we make progress on this open problem by presenting the first computational lower bound for RL with linear function approximation: unless NP=RP, no randomized polynomial time algorithm exists for deterministic transition MDPs with a constant number of actions and linear optimal value functions.
no code implementations • 1 Dec 2020 • Vasilis Kontonis, Sihan Liu, Christos Tzamos
Our main result is that by training the Generator together with a Discriminator according to the Stochastic Gradient Descent-Ascent iteration proposed by Goodfellow et al. yields a Generator distribution that approaches the target distribution of $f_*$.