Search Results for author: Richard Lanas Phillips

Found 3 papers, 3 papers with code

One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning

1 code implementation4 Mar 2021 Avrim Blum, Nika Haghtalab, Richard Lanas Phillips, Han Shao

In recent years, federated learning has been embraced as an approach for bringing about collaboration across large populations of learning agents.

Federated Learning

Disentangling Influence: Using Disentangled Representations to Audit Model Predictions

1 code implementation NeurIPS 2019 Charles T. Marx, Richard Lanas Phillips, Sorelle A. Friedler, Carlos Scheidegger, Suresh Venkatasubramanian

Specifically, we show that disentangled representations provide a mechanism to identify proxy features in the dataset, while allowing an explicit computation of feature influence on either individual outcomes or aggregate-level outcomes.

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