2 citations · 6 across the 8 of their papers we have counts for
4 papers · 1 filter
Estimation of Concept Explanations Should be Uncertainty Aware
Vihari Piratla, Juyeon Heo, Katherine M. Collins +2
Model explanations can be valuable for interpreting and debugging predictive models. We study a specific kind called Concept Explanations, where the goal is to interpret a model us…
Certification of Distributional Individual Fairness
Matthew Wicker, Vihari Piratia, Adrian Weller
Providing formal guarantees of algorithmic fairness is of paramount importance to socially responsible deployment of machine learning algorithms. In this work, we study formal guar…
Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization
Weiyang Liu, Zeju Qiu, Yao Feng +11
Large foundation models are becoming ubiquitous, but training them from scratch is prohibitively expensive. Thus, efficiently adapting these powerful models to downstream tasks is…
Pairwise Similarity Learning is SimPLE
Yandong Wen, Weiyang Liu, Yao Feng +5
In this paper, we focus on a general yet important learning problem, pairwise similarity learning (PSL). PSL subsumes a wide range of important applications, such as open-set face…