2 citations · 2 across the 12 of their papers we have counts for
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Sloth: scaling laws for LLM skills to predict multi-benchmark performance across families
Felipe Maia Polo, Seamus Somerstep, Leshem Choshen +2
Scaling laws for large language models (LLMs) predict model performance based on parameters like size and training data. However, differences in training configurations and data pr…
Microfoundation Inference for Strategic Prediction
Daniele Bracale, Subha Maity, Felipe Maia Polo +3
Often in prediction tasks, the predictive model itself can influence the distribution of the target variable, a phenomenon termed performative prediction. Generally, this influence…
Algorithmic Fairness in Performative Policy Learning: Escaping the Impossibility of Group Fairness
Seamus Somerstep, Ya'acov Ritov, Yuekai Sun
In many prediction problems, the predictive model affects the distribution of the prediction target. This phenomenon is known as performativity and is often caused by the behavior…
A transfer learning framework for weak-to-strong generalization
Seamus Somerstep, Felipe Maia Polo, Moulinath Banerjee +3
Modern large language model (LLM) alignment techniques rely on human feedback, but it is unclear whether these techniques fundamentally limit the capabilities of aligned LLMs. In p…
Learning In Reverse Causal Strategic Environments With Ramifications on Two Sided Markets
Seamus Somerstep, Yuekai Sun, Ya'acov Ritov
Motivated by equilibrium models of labor markets, we develop a formulation of causal strategic classification in which strategic agents can directly manipulate their outcomes. As a…