196 citations · 203 across the 12 of their papers we have counts for
5 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…
Measuring Representational Robustness of Neural Networks Through Shared Invariances
Vedant Nanda, Till Speicher, Camila Kolling +3
A major challenge in studying robustness in deep learning is defining the set of ``meaningless'' perturbations to which a given Neural Network (NN) should be invariant. Most work o…
A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual & Group Unfairness via Inequality Indices
Till Speicher, Hoda Heidari, Nina Grgic-Hlaca +4
Discrimination via algorithmic decision making has received considerable attention. Prior work largely focuses on defining conditions for fairness, but does not define satisfactory…