Accuracy
Nearly Tight Black-Box Auditing of Differentially Private Machine Learning
This paper presents an auditing procedure for the Differentially Private Stochastic Gradient Descent (DP-SGD) algorithm in the black-box threat model that is substantially tighter than prior work.
Towards Multi-dimensional Explanation Alignment for Medical Classification
The lack of interpretability in the field of medical image analysis has significant ethical and legal implications.
CLAP4 CLIP: Continual Learning with Probabilistic Finetuning for Vision-Language Models
This makes them overlook the many possible interactions across the input modalities and deems them unsafe for high-risk tasks requiring reliable uncertainty estimation.
Persistent Test-time Adaptation in Recurring Testing Scenarios
Current test-time adaptation (TT A) approaches aim to adapt a machine learning model to environments that change continuously.
RashomonGB: Analyzing the Rashomon Effect and Mitigating Predictive Multiplicity in Gradient Boosting
The Rashomon effect is a mixed blessing in responsible machine learning.