🏢 Carnegie Mellon University

This paper presents novel theoretical results enabling the analytical calculation of Partial Information Decomposition for various probability distributions, including those relevant to neuroscience, …

This paper introduces a novel framework for aligning LLMs with honesty, proposing new metrics and training techniques to make LLMs more truthful and less prone to confidently incorrect responses.

This paper introduces Quantitative Relative Judgment Aggregation (QRJA), a novel social choice model, and applies it to ranking prediction, yielding effective and interpretable results on various real…

Improved adversarially robust streaming algorithms for L_p estimation are presented, surpassing previous state-of-the-art space bounds and disproving the existence of inherent barriers.

This paper introduces anytime-valid risk-controlling prediction sets for active learning, guaranteeing low risk even with adaptive data collection and limited label budgets.

Certifying neural network robustness across diverse domains, this paper introduces knowledge continuity—a novel framework ensuring model stability independent of input type, norms, and distribution.

Accelerating ERM for data-driven algorithm design using output-sensitive techniques achieves computationally efficient learning by scaling with the actual number of pieces in the dual loss function, n…

Bounded human feedback hinders large AI model training. This paper introduces hierarchical reinforcement learning to enable scalable oversight, efficiently acquiring feedback and learning optimal poli…