14 citations · 28 across the 10 of their papers we have counts for
12 papers
The Role of Causal Features in Strategic Classification for Robustness and Alignment
Antonio Gois, Sophia Gunluk, Nir Rosenfeld +3
In strategic classification, an institution (e.g., a bank) anticipates adaptation from users who change their features to increase utility in a classification task (e.g., loan repa…
RobustDebias: Debiasing Language Models using Distributionally Robust Optimization
Deep Gandhi, Katyani Singh, Nidhi Hegde
Pretrained language models have been shown to exhibit biases and social stereotypes. Prior work on debiasing these models has largely focused on modifying embedding spaces during p…
FineDeb: A Debiasing Framework for Language Models
Akash Saravanan, Dhruv Mullick, Habibur Rahman +1
As language models are increasingly included in human-facing machine learning tools, bias against demographic subgroups has gained attention. We propose FineDeb, a two-phase debias…
Long Term Fairness for Minority Groups via Performative Distributionally Robust Optimization
Liam Peet-Pare, Nidhi Hegde, Alona Fyshe
Fairness researchers in machine learning (ML) have coalesced around several fairness criteria which provide formal definitions of what it means for an ML model to be fair. However,…
Resonance in Weight Space: Covariate Shift Can Drive Divergence of SGD with Momentum
Kirby Banman, Liam Peet-Pare, Nidhi Hegde +2
Most convergence guarantees for stochastic gradient descent with momentum (SGDm) rely on iid sampling. Yet, SGDm is often used outside this regime, in settings with temporally corr…
Multi Type Mean Field Reinforcement Learning
Sriram Ganapathi Subramanian, Pascal Poupart, Matthew E. Taylor +1
Mean field theory provides an effective way of scaling multiagent reinforcement learning algorithms to environments with many agents that can be abstracted by a virtual mean agent.…