106 citations · 274 across the 11 of their papers we have counts for
12 papers
Unravelling the deterministic effect of the solid-state diffusion energy barrier for charge carrier on the self-discharge of supercapacitors
Xiaohui Yan, Yue He, Xuncheng Liu +7
The further development of fast electrochemical devices is hindered by self-discharge. Current strategies for suppressing self-discharge are mainly focused on the extrinsic and gen…
Label Leakage and Protection in Two-party Split Learning
Oscar Li, Jiankai Sun, Xin Yang +5
Two-party split learning is a popular technique for learning a model across feature-partitioned data. In this work, we explore whether it is possible for one party to steal the pri…
Information-Theoretic Understanding of Population Risk Improvement with Model Compression
Yuheng Bu, Weihao Gao, Shaofeng Zou +1
We show that model compression can improve the population risk of a pre-trained model, by studying the tradeoff between the decrease in the generalization error and the increase in…
Rate Distortion For Model Compression: From Theory To Practice
Weihao Gao, Yu-Han Liu, Chong Wang +1
The enormous size of modern deep neural networks makes it challenging to deploy those models in memory and communication limited scenarios. Thus, compressing a trained model withou…
Learning One-hidden-layer Neural Networks under General Input Distributions
Weihao Gao, Ashok Vardhan Makkuva, Sewoong Oh +1
Significant advances have been made recently on training neural networks, where the main challenge is in solving an optimization problem with abundant critical points. However, exi…
The Nearest Neighbor Information Estimator is Adaptively Near Minimax Rate-Optimal
Jiantao Jiao, Weihao Gao, Yanjun Han
We analyze the Kozachenko--Leonenko (KL) nearest neighbor estimator for the differential entropy. We obtain the first uniform upper bound on its performance over Hölder balls on a…
Estimating Mutual Information for Discrete-Continuous Mixtures
Weihao Gao, Sreeram Kannan, Sewoong Oh +1
Estimating mutual information from observed samples is a basic primitive, useful in several machine learning tasks including correlation mining, information bottleneck clustering,…
Discovering Potential Correlations via Hypercontractivity
Hyeji Kim, Weihao Gao, Sreeram Kannan +2
Discovering a correlation from one variable to another variable is of fundamental scientific and practical interest. While existing correlation measures are suitable for discoverin…
Density Functional Estimators with k-Nearest Neighbor Bandwidths
Weihao Gao, Sewoong Oh, Pramod Viswanath
Estimating expected polynomials of density functions from samples is a basic problem with numerous applications in statistics and information theory. Although kernel density estima…
Breaking the Bandwidth Barrier: Geometrical Adaptive Entropy Estimation
Weihao Gao, Sewoong Oh, Pramod Viswanath
Estimators of information theoretic measures such as entropy and mutual information are a basic workhorse for many downstream applications in modern data science. State of the art…
Demystifying Fixed k-Nearest Neighbor Information Estimators
Weihao Gao, Sewoong Oh, Pramod Viswanath
Estimating mutual information from i.i.d. samples drawn from an unknown joint density function is a basic statistical problem of broad interest with multitudinous applications. The…
Conditional Dependence via Shannon Capacity: Axioms, Estimators and Applications
Weihao Gao, Sreeram Kannan, Sewoong Oh +1
We conduct an axiomatic study of the problem of estimating the strength of a known causal relationship between a pair of variables. We propose that an estimate of causal strength s…