117 citations · 124 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…
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…
Leveraging Task Structures for Improved Identifiability in Neural Network Representations
Wenlin Chen, Julien Horwood, Juyeon Heo +1
This work extends the theory of identifiability in supervised learning by considering the consequences of having access to a distribution of tasks. In such cases, we show that line…
Use Perturbations when Learning from Explanations
Juyeon Heo, Vihari Piratla, Matthew Wicker +1
Machine learning from explanations (MLX) is an approach to learning that uses human-provided explanations of relevant or irrelevant features for each input to ensure that model pre…