no code implementations • 8 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.
no code implementations • 31 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.
no code implementations • 14 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.
1 code implementation • 30 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.
no code implementations • 27 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.
no code implementations • 16 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.
1 code implementation • 14 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.
no code implementations • 15 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.
1 code implementation • 11 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.
1 code implementation • 22 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.
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.
no code implementations • 24 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.
1 code implementation • 1 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
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.
no code implementations • 2 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.
1 code implementation • 28 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.
no code implementations • 28 Jul 2021 • Gholamali Aminian, Yuheng Bu, Laura Toni, Miguel R. D. Rodrigues, Gregory Wornell
As a result, they may fail to characterize the exact generalization ability of a learning algorithm.
no code implementations • 30 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.
no code implementations • 4 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.
1 code implementation • 27 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.
no code implementations • 15 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.
no code implementations • 30 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.
no code implementations • 19 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 $\rho$.
no code implementations • 29 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.
no code implementations • 21 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.