no code implementations • 20 Feb 2024 • Maohao Shen, Subhro Das, Kristjan Greenewald, Prasanna Sattigeri, Gregory Wornell, Soumya Ghosh
Addressing these challenges, we propose THERMOMETER, a calibration approach tailored to LLMs.
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.
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 • 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.
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 • NeurIPS 2019 • Joshua Lee, Prasanna Sattigeri, Gregory Wornell
However, for practical, privacy, or other reasons, in a variety of applications we may have no control over the individual source task training, nor access to source training samples.
no code implementations • NeurIPS 2018 • Abhishek Kumar, Prasanna Sattigeri, Kahini Wadhawan, Leonid Karlinsky, Rogerio Feris, William T. Freeman, Gregory Wornell
Deep neural networks, trained with large amount of labeled data, can fail to generalize well when tested with examples from a \emph{target domain} whose distribution differs from the training data distribution, referred as the \emph{source domain}.