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pro vyhledávání: '"Han, Yanjun"'
In this paper, we develop a unified framework for lower bound methods in statistical estimation and interactive decision making. Classical lower bound techniques -- such as Fano's inequality, Le Cam's method, and Assouad's lemma -- have been central
Externí odkaz:
http://arxiv.org/abs/2410.05117
Autor:
Han, Yanjun, Niles-Weed, Jonathan
We prove bounds on statistical distances between high-dimensional exchangeable mixture distributions (which we call permutation mixtures) and their i.i.d. counterparts. Our results are based on a novel method for controlling $\chi^2$ divergences betw
Externí odkaz:
http://arxiv.org/abs/2408.09341
When estimating causal effects from observational studies, researchers often need to adjust for many covariates to deconfound the non-causal relationship between exposure and outcome, among which many covariates are discrete. The behavior of commonly
Externí odkaz:
http://arxiv.org/abs/2405.00118
Consider the problem of predicting the next symbol given a sample path of length n, whose joint distribution belongs to a distribution class that may have long-term memory. The goal is to compete with the conditional predictor that knows the true mod
Externí odkaz:
http://arxiv.org/abs/2404.15454
$ $The classical theory of statistical estimation aims to estimate a parameter of interest under data generated from a fixed design ("offline estimation"), while the contemporary theory of online learning provides algorithms for estimation under adap
Externí odkaz:
http://arxiv.org/abs/2404.10122
We consider contextual bandits with graph feedback, a class of interactive learning problems with richer structures than vanilla contextual bandits, where taking an action reveals the rewards for all neighboring actions in the feedback graph under al
Externí odkaz:
http://arxiv.org/abs/2402.18591
Feature alignment methods are used in many scientific disciplines for data pooling, annotation, and comparison. As an instance of a permutation learning problem, feature alignment presents significant statistical and computational challenges. In this
Externí odkaz:
http://arxiv.org/abs/2311.13595
Autor:
Zhang, Yuqian, Sun, Changzheng, Xiong, Bing, Wang, Jian, Hao, Zhibiao, Wang, Lai, Han, Yanjun, Li, Hongtao, Luo, Yi
AlGaAs is a promising integrated nonlinear photonics material with enormous optical nonlinearity and high refractive index. Nevertheless, presently AlGaAs microring resonators exhibiting high quality factors and tight optical confinement rely predomi
Externí odkaz:
http://arxiv.org/abs/2308.02988
This paper considers an ML inspired approach to hypothesis testing known as classifier/classification-accuracy testing ($\mathsf{CAT}$). In $\mathsf{CAT}$, one first trains a classifier by feeding it labeled synthetic samples generated by the null an
Externí odkaz:
http://arxiv.org/abs/2306.11085
We consider repeated multi-unit auctions with uniform pricing, which are widely used in practice for allocating goods such as carbon licenses. In each round, $K$ identical units of a good are sold to a group of buyers that have valuations with dimini
Externí odkaz:
http://arxiv.org/abs/2305.17402