10 papers
Model Confidence Under Answer-Preserving Attacks: An Informativeness-Manipulability Frontier
Reza Khanmohammadi, Ivan Brugere, Simerjot Kaur +3
Deployed vision-language systems often gate their answers on confidence, making confidence robustness relevant to oversight. We study confidence readouts under white-box, image-onl…
Confidence Estimation for Financial Vision-Language Models in Chart and Document Understanding
Reza Khanmohammadi, Simerjot Kaur, Charese H. Smiley +2
LVLMs are increasingly used to read financial charts, tables, and documents, where a single misread figure can move a decision and the most authoritative-looking answer is sometime…
Revisiting ML Training under Fully Homomorphic Encryption: Convergence Guarantees, Differential Privacy, and Efficient Algorithms
Yvonne Zhou, Mingyu Liang, Ivan Brugere +5
We present the first theoretical convergence analysis of machine learning training under fully homomorphic encryption (FHE), combined with a differentially private (DP) training al…
Grounded or Guessing? LVLM Confidence Estimation via Blind-Image Contrastive Ranking
Reza Khanmohammadi, Erfan Miahi, Simerjot Kaur +4
Large vision-language models suffer from visual ungroundedness: they can produce a fluent, confident, and even correct response driven entirely by language priors, with the image c…
MAFE: Enabling Equitable Algorithm Design in Multi-Agent Multi-Stage Decision-Making Systems
Zachary McBride Lazri, Anirudh Nakra, Ivan Brugere +5
Algorithmic fairness is often studied in static or single-agent settings, yet many real-world decision-making systems involve multiple interacting entities whose multi-stage action…
The Unseen Threat: Residual Knowledge in Machine Unlearning under Perturbed Samples
Hsiang Hsu, Pradeep Niroula, Zichang He +3
Machine unlearning offers a practical alternative to avoid full model re-training by approximately removing the influence of specific user data. While existing methods certify unle…