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Found 8 papers, 1 papers with code
Super Non-singular Decompositions of Polynomials and their Application to Robustly Learning Low-degree PTFs
We study the efficient learnability of low-degree polynomial threshold functions (PTFs) in the presence of a constant fraction of adversarial corruptions.
Rotated Multi-Scale Interaction Network for Referring Remote Sensing Image Segmentation
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.
Testing Closeness of Multivariate Distributions via Ramsey Theory
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.
Online Robust Mean Estimation
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.
Exponential Hardness of Reinforcement Learning with Linear Function Approximation
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.
Near-Optimal Bounds for Testing Histogram Distributions
We investigate the problem of testing whether a discrete probability distribution over an ordered domain is a histogram on a specified number of bins.
Computational-Statistical Gaps in Reinforcement Learning
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.
Convergence and Sample Complexity of SGD in GANs
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 $f_*$.
