108 citations · 320 across the 25 of their papers we have counts for
12 papers · 1 filter
Understanding the Role of Invariance in Transfer Learning
Till Speicher, Vedant Nanda, Krishna P. Gummadi
Transfer learning is a powerful technique for knowledge-sharing between different tasks. Recent work has found that the representations of models with certain invariances, such as…
Diffused Redundancy in Pre-trained Representations
Vedant Nanda, Till Speicher, John P. Dickerson +3
Representations learned by pre-training a neural network on a large dataset are increasingly used successfully to perform a variety of downstream tasks. In this work, we take a clo…
Investigating the Effects of Fairness Interventions Using Pointwise Representational Similarity
Camila Kolling, Till Speicher, Vedant Nanda +2
Machine learning (ML) algorithms can often exhibit discriminatory behavior, negatively affecting certain populations across protected groups. To address this, numerous debiasing me…
Detecting and Mitigating Test-time Failure Risks via Model-agnostic Uncertainty Learning
Preethi Lahoti, Krishna P. Gummadi, Gerhard Weikum
Reliably predicting potential failure risks of machine learning (ML) systems when deployed with production data is a crucial aspect of trustworthy AI. This paper introduces Risk Ad…
Loss-Aversively Fair Classification
Junaid Ali, Muhammad Bilal Zafar, Adish Singla +1
The use of algorithmic (learning-based) decision making in scenarios that affect human lives has motivated a number of recent studies to investigate such decision making systems fo…
Accounting for Model Uncertainty in Algorithmic Discrimination
Junaid Ali, Preethi Lahoti, Krishna P. Gummadi
Traditional approaches to ensure group fairness in algorithmic decision making aim to equalize ``total'' error rates for different subgroups in the population. In contrast, we argu…