270 citations · 1.1k across the 72 of their papers we have counts for
5 papers · 1 filter
Post-hoc Uncertainty Learning using a Dirichlet Meta-Model
Maohao Shen, Yuheng Bu, Prasanna Sattigeri +3
It is known that neural networks have the problem of being over-confident when directly using the output label distribution to generate uncertainty measures. Existing methods mainl…
Fair Infinitesimal Jackknife: Mitigating the Influence of Biased Training Data Points Without Refitting
Prasanna Sattigeri, Soumya Ghosh, Inkit Padhi +2
In consequential decision-making applications, mitigating unwanted biases in machine learning models that yield systematic disadvantage to members of groups delineated by sensitive…
Equi-Tuning: Group Equivariant Fine-Tuning of Pretrained Models
Sourya Basu, Prasanna Sattigeri, Karthikeyan Natesan Ramamurthy +4
We introduce equi-tuning, a novel fine-tuning method that transforms (potentially non-equivariant) pretrained models into group equivariant models while incurring minimum los…
Causal Bandits for Linear Structural Equation Models
Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri +1
This paper studies the problem of designing an optimal sequence of interventions in a causal graphical model to minimize cumulative regret with respect to the best intervention in…
Attribute Graphs Underlying Molecular Generative Models: Path to Learning with Limited Data
Samuel C. Hoffman, Payel Das, Karthikeyan Shanmugam +2
Training generative models that capture rich semantics of the data and interpreting the latent representations encoded by such models are very important problems in un-/self-superv…