Algorithmic Fairness in Performative Policy Learning: Escaping the Impossibility of Group Fairness. - Seamus Somerstep; Yaacov Ritov; Yuekai Sun (FAccT, 2024)

Learning in reverse causal strategic environments with ramifications on two sided markets. - Seamus Somerstep; Yuekai Sun; Yaacov Ritov (ICLR, 2024)

A statistical framework for weak-to-strong generalization. - Seamus Somerstep; Felipe Maia Polo; Moulinath Banerjee; Yaacov Ritov; Mikhail Yurochkin; Yuekai Sun (CoRR, 2024)

Sloth: scaling laws for LLM skills to predict multi-benchmark performance across families. - Felipe Maia Polo; Seamus Somerstep; Leshem Choshen; Yuekai Sun; Mikhail Yurochkin (CoRR, 2024)

Microfoundation inference for strategic prediction. - Daniele Bracale; Subha Maity; Felipe Maia Polo; Seamus Somerstep; Moulinath Banerjee; Yuekai Sun (AISTATS, 2025)

A transfer learning framework for weak to strong generalization. - Seamus Somerstep; Felipe Maia Polo; Moulinath Banerjee; Yaacov Ritov; Mikhail Yurochkin; Yuekai Sun (ICLR, 2025)

CARROT: A Cost Aware Rate Optimal Router. - Seamus Somerstep; Felipe Maia Polo; Allysson Flavio Melo de Oliveira; Prattyush Mangal; Mírian Silva; Onkar Bhardwaj; Mikhail Yurochkin; Subha Maity (CoRR, 2025)

Learning to Choose or Choosing to Learn: Best-of-N vs. Supervised Fine-Tuning for Bit String Generation. - Seamus Somerstep; Vinod Raman; Unique Subedi; Yuekai Sun (CoRR, 2025)