🏢 Purdue University

This paper presents novel graphical criteria and estimators for accurately evaluating policy effectiveness across multiple experimental datasets, even when data distributions differ.

ECLipsE: A novel compositional approach drastically accelerates Lipschitz constant estimation for deep neural networks, achieving speedups of thousands of times compared to the state-of-the-art while …

DIGRAF, a novel graph-adaptive activation function, significantly boosts Graph Neural Network performance by dynamically adapting to graph structure, offering consistent superior results across divers…

Deep learning privacy is enhanced by a new membership inference attack using input loss curvature, exceeding existing methods, especially on large datasets.

This paper proposes a novel method to achieve optimal counterfactual fairness in machine learning models while minimizing predictive performance degradation.

ID-GEN: Sample high-dimensional interventional distributions using any conditional generative model!

BISCOPE: AI-generated text detection using a novel bidirectional method that outperforms existing techniques by leveraging both prediction and memorization of preceding tokens.

SFID, a novel debiasing method, effectively mitigates bias in vision-language models across various tasks without retraining, improving fairness and efficiency.

This paper fully characterizes concept classes optimistically universally learnable online, introducing novel algorithms and revealing equivalences between agnostic and realizable settings.