8 papers
From Deep Additive Kernel Learning to Last-Layer Bayesian Neural Networks via Induced Prior Approximation
Wenyuan Zhao, Haoyuan Chen, Tie Liu +2
With the strengths of both deep learning and kernel methods like Gaussian Processes (GPs), Deep Kernel Learning (DKL) has gained considerable attention in recent years. From the co…
From Function to Distribution Modeling: A PAC-Generative Approach to Offline Optimization
Qiang Zhang, Ruida Zhou, Yang Shen +1
This paper considers the problem of offline optimization, where the objective function is unknown except for a collection of ``offline" data examples. While recent years have seen…
Exactly Tight Information-Theoretic Generalization Error Bound for the Quadratic Gaussian Problem
Ruida Zhou, Chao Tian, Tie Liu
We provide a new information-theoretic generalization error bound that is exactly tight (i.e., matching even the constant) for the canonical quadratic Gaussian (location) problem.…
Improved Weakly Private Information Retrieval Codes
Chengyuan Qian, Ruida Zhou, Chao Tian +1
We study the problem of weakly private information retrieval (W-PIR), where a user wishes to retrieve a desired message from non-colluding servers in a way that the privacy lea…
On Linear Separability under Linear Compression with Applications to Hard Support Vector Machine
Paul McVay, Tie Liu, Krishna Narayanan
This paper investigates the theoretical problem of maintaining linear separability of the data-generating distribution under linear compression. While it has been long known that l…
Stochastic Chaining and Strengthened Information-Theoretic Generalization Bounds
Ruida Zhou, Chao Tian, Tie Liu
We propose a new approach to apply the chaining technique in conjunction with information-theoretic measures to bound the generalization error of machine learning algorithms. Diffe…
Multiple Sample Clustering
Xiang Wang, Tie Liu
The clustering algorithms that view each object data as a single sample drawn from a certain distribution, Gaussian distribution, for example, has been a hot topic for decades. Man…
New Results on Multilevel Diversity Coding with Secure Regeneration
Shuo Shao, Tie Liu, Chao Tian +1
The problem of multilevel diversity coding with secure regeneration is revisited. Under the assumption that the eavesdropper can access the repair data for all compromised storage…