19 citations · 22 across the 7 of their papers we have counts for
5 papers · 1 filter
Adjusting Pretrained Backbones for Performativity
Berker Demirel, Lingjing Kong, Kun Zhang +3
With the widespread deployment of deep learning models, they influence their environment in various ways. The induced distribution shifts can lead to unexpected performance degrada…
Collaborative Learning via Prediction Consensus
Dongyang Fan, Celestine Mendler-Dünner, Martin Jaggi
We consider a collaborative learning setting where the goal of each agent is to improve their own model by leveraging the expertise of collaborators, in addition to their own train…
Causal Inference out of Control: Estimating the Steerability of Consumption
Gary Cheng, Moritz Hardt, Celestine Mendler-Dünner
Regulators and academics are increasingly interested in the causal effect that algorithmic actions of a digital platform have on consumption. We introduce a general causal inferenc…
Algorithmic Collective Action in Machine Learning
Moritz Hardt, Eric Mazumdar, Celestine Mendler-Dünner +1
We initiate a principled study of algorithmic collective action on digital platforms that deploy machine learning algorithms. We propose a simple theoretical model of a collective…
Test-time Collective Prediction
Celestine Mendler-Dünner, Wenshuo Guo, Stephen Bates +1
An increasingly common setting in machine learning involves multiple parties, each with their own data, who want to jointly make predictions on future test points. Agents wish to b…