Search Results for author: Yuheng Bu

Found 25 papers, 7 papers with code

Gibbs-Based Information Criteria and the Over-Parameterized Regime

no code implementations8 Jun 2023 Haobo Chen, Yuheng Bu, Gregory W. Wornell

Double-descent refers to the unexpected drop in test loss of a learning algorithm beyond an interpolating threshold with over-parameterization, which is not predicted by information criteria in their classical forms due to the limitations in the standard asymptotic approach.

Model Selection

Feature Learning in Image Hierarchies using Functional Maximal Correlation

no code implementations31 May 2023 Bo Hu, Yuheng Bu, José C. Príncipe

This paper proposes the Hierarchical Functional Maximal Correlation Algorithm (HFMCA), a hierarchical methodology that characterizes dependencies across two hierarchical levels in multiview systems.

Self-Supervised Learning

A Bilateral Bound on the Mean-Square Error for Estimation in Model Mismatch

no code implementations14 May 2023 Amir Weiss, Alejandro Lancho, Yuheng Bu, Gregory W. Wornell

A bilateral (i. e., upper and lower) bound on the mean-square error under a general model mismatch is developed.

Reliable Gradient-free and Likelihood-free Prompt Tuning

1 code implementation30 Apr 2023 Maohao Shen, Soumya Ghosh, Prasanna Sattigeri, Subhro Das, Yuheng Bu, Gregory Wornell

Due to privacy or commercial constraints, large pre-trained language models (PLMs) are often offered as black-box APIs.

On the Generalization Error of Meta Learning for the Gibbs Algorithm

no code implementations27 Apr 2023 Yuheng Bu, Harsha Vardhan Tetali, Gholamali Aminian, Miguel Rodrigues, Gregory Wornell

We analyze the generalization ability of joint-training meta learning algorithms via the Gibbs algorithm.


Group Fairness with Uncertainty in Sensitive Attributes

no code implementations16 Feb 2023 Abhin Shah, Maohao Shen, Jongha Jon Ryu, Subhro Das, Prasanna Sattigeri, Yuheng Bu, Gregory W. Wornell

To overcome this limitation, we propose a bootstrap-based algorithm that achieves the target level of fairness despite the uncertainty in sensitive attributes.


Post-hoc Uncertainty Learning using a Dirichlet Meta-Model

1 code implementation14 Dec 2022 Maohao Shen, Yuheng Bu, Prasanna Sattigeri, Soumya Ghosh, Subhro Das, Gregory Wornell

It is known that neural networks have the problem of being over-confident when directly using the output label distribution to generate uncertainty measures.

Image Classification Transfer Learning

How Does Pseudo-Labeling Affect the Generalization Error of the Semi-Supervised Gibbs Algorithm?

no code implementations15 Oct 2022 Haiyun He, Gholamali Aminian, Yuheng Bu, Miguel Rodrigues, Vincent Y. F. Tan

Our findings offer new insights that the generalization performance of SSL with pseudo-labeling is affected not only by the information between the output hypothesis and input training data but also by the information {\em shared} between the {\em labeled} and {\em pseudo-labeled} data samples.


Data-Driven Blind Synchronization and Interference Rejection for Digital Communication Signals

1 code implementation11 Sep 2022 Alejandro Lancho, Amir Weiss, Gary C. F. Lee, Jennifer Tang, Yuheng Bu, Yury Polyanskiy, Gregory W. Wornell

We study the potential of data-driven deep learning methods for separation of two communication signals from an observation of their mixture.

Exploiting Temporal Structures of Cyclostationary Signals for Data-Driven Single-Channel Source Separation

1 code implementation22 Aug 2022 Gary C. F. Lee, Amir Weiss, Alejandro Lancho, Jennifer Tang, Yuheng Bu, Yury Polyanskiy, Gregory W. Wornell

We study the problem of single-channel source separation (SCSS), and focus on cyclostationary signals, which are particularly suitable in a variety of application domains.

A Maximal Correlation Framework for Fair Machine Learning

no code implementations Entropy 2022 Joshua Lee, Yuheng Bu, Prasanna Sattigeri, Rameswar Panda, Gregory W. Wornell, Leonid Karlinsky and Rogerio Schmidt Feris

As machine learning algorithms grow in popularity and diversify to many industries, ethical and legal concerns regarding their fairness have become increasingly relevant.


Tighter Expected Generalization Error Bounds via Convexity of Information Measures

no code implementations24 Feb 2022 Gholamali Aminian, Yuheng Bu, Gregory Wornell, Miguel Rodrigues

Due to the convexity of the information measures, the proposed bounds in terms of Wasserstein distance and total variation distance are shown to be tighter than their counterparts based on individual samples in the literature.

On Balancing Bias and Variance in Unsupervised Multi-Source-Free Domain Adaptation

1 code implementation1 Feb 2022 Maohao Shen, Yuheng Bu, Gregory Wornell

Due to privacy, storage, and other constraints, there is a growing need for unsupervised domain adaptation techniques in machine learning that do not require access to the data used to train a collection of source models.

Source-Free Domain Adaptation Unsupervised Domain Adaptation

An Exact Characterization of the Generalization Error for the Gibbs Algorithm

no code implementations NeurIPS 2021 Gholamali Aminian, Yuheng Bu, Laura Toni, Miguel Rodrigues, Gregory Wornell

Various approaches have been developed to upper bound the generalization error of a supervised learning algorithm.

Characterizing and Understanding the Generalization Error of Transfer Learning with Gibbs Algorithm

no code implementations2 Nov 2021 Yuheng Bu, Gholamali Aminian, Laura Toni, Miguel Rodrigues, Gregory Wornell

We provide an information-theoretic analysis of the generalization ability of Gibbs-based transfer learning algorithms by focusing on two popular transfer learning approaches, $\alpha$-weighted-ERM and two-stage-ERM.

Transfer Learning

Selective Regression Under Fairness Criteria

1 code implementation28 Oct 2021 Abhin Shah, Yuheng Bu, Joshua Ka-Wing Lee, Subhro Das, Rameswar Panda, Prasanna Sattigeri, Gregory W. Wornell

Selective regression allows abstention from prediction if the confidence to make an accurate prediction is not sufficient.

Fairness regression

A Maximal Correlation Approach to Imposing Fairness in Machine Learning

no code implementations30 Dec 2020 Joshua Lee, Yuheng Bu, Prasanna Sattigeri, Rameswar Panda, Gregory Wornell, Leonid Karlinsky, Rogerio Feris

As machine learning algorithms grow in popularity and diversify to many industries, ethical and legal concerns regarding their fairness have become increasingly relevant.

BIG-bench Machine Learning Fairness

Adaptive Sequential Machine Learning

no code implementations4 Apr 2019 Craig Wilson, Yuheng Bu, Venugopal Veeravalli

A framework previously introduced in [3] for solving a sequence of stochastic optimization problems with bounded changes in the minimizers is extended and applied to machine learning problems such as regression and classification.

BIG-bench Machine Learning Model Selection +1

Information-Theoretic Understanding of Population Risk Improvement with Model Compression

1 code implementation27 Jan 2019 Yuheng Bu, Weihao Gao, Shaofeng Zou, Venugopal V. Veeravalli

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 the empirical risk with model compression.

Clustering Model Compression

Tightening Mutual Information Based Bounds on Generalization Error

no code implementations15 Jan 2019 Yuheng Bu, Shaofeng Zou, Venugopal V. Veeravalli

The bound is derived under more general conditions on the loss function than in existing studies; nevertheless, it provides a tighter characterization of the generalization error.

Active Learning in Recommendation Systems with Multi-level User Preferences

no code implementations30 Nov 2018 Yuheng Bu, Kevin Small

While recommendation systems generally observe user behavior passively, there has been an increased interest in directly querying users to learn their specific preferences.

Active Learning Recommendation Systems

Model change detection with application to machine learning

no code implementations19 Nov 2018 Yuheng Bu, Jiaxun Lu, Venugopal V. Veeravalli

The goal is to detect whether the change in the model is significant, i. e., whether the difference between the pre-change parameter and the post-change parameter $\|\theta-\theta'\|_2$ is larger than a pre-determined threshold $\rho$.

BIG-bench Machine Learning Change Detection +2

Active and Adaptive Sequential learning

no code implementations29 May 2018 Yuheng Bu, Jiaxun Lu, Venugopal V. Veeravalli

Furthermore, an estimator of the change in the learning problems using the active learning samples is constructed, which provides an adaptive sample size selection rule that guarantees the excess risk is bounded for sufficiently large number of time steps.

Active Learning

Linear-Complexity Exponentially-Consistent Tests for Universal Outlying Sequence Detection

no code implementations21 Jan 2017 Yuheng Bu, Shaofeng Zou, Venugopal V. Veeravalli

A sequence is considered as outlying if the observations therein are generated by a distribution different from those generating the observations in the majority of the sequences.


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