My personal website.
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Updated
Mar 12, 2024 - HTML
My personal website.
Trustworthy AI/ML course by Professor Birhanu Eshete, University of Michigan, Dearborn.
This repo contains the codes, figures and datasets for the paper - U-Trustworthy Models. Reliability, Competence, and Confidence in Decision-Making.
DSPLab@UMich-Dearborn Website
Birhanu Eshete is an Associate Professor of Computer Science at the University of Michigan, Dearborn. His main research focus is in trustworthy machine learning with emphasis on security, safety, privacy, interpretability, fairness, and the dynamics thereof. He also studies online cybercrime and advanced and persistent threats (APTs).
Explainable Debugger for Black-box Machine Learning Models
KDD 2023 tutorial "Trustworthy Transfer Learning: Transferability and Trustworthiness"
Fair and explainable ML workshop
Papers related to Federated Learning in all venue (dblp)
Explanation-guided boosting of machine learning evasion attacks.
Machine Learning Security Library
Fair and explainable ML workshop
Welcome to my Machine Learning repository, where you can find learning materials both from my studies and from various online courses.
Official implementation of NeurIPS 2023 paper "Trade-off Between Efficiency and Consistency for Removal-based Explanations" (https://arxiv.org/abs/2210.17426)
In the dynamic landscape of medical artificial intelligence, this study explores the vulnerabilities of the Pathology Language-Image Pretraining (PLIP) model, a Vision Language Foundation model, under targeted attacks like PGD adversarial attack.
TRIAGE: Characterizing and auditing training data for improved regression (NeurIPS 2023)
Code for the paper "Approximating full conformal prediction at scale via influence functions""
Data-SUITE: Data-centric identification of in-distribution incongruous examples (ICML 2022)
Code from PLDI '21 paper "Provable Repair of Deep Neural Networks."
A School for All Seasons on Trustworthy Machine Learning
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