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On the Efficiency of ERM in Feature Learning
Ayoub El Hanchi, Chris J. Maddison, Murat A. Erdogdu
Given a collection of feature maps indexed by a set , we study the performance of empirical risk minimization (ERM) on regression problems with square loss over the un…
End-To-End Causal Effect Estimation from Unstructured Natural Language Data
Nikita Dhawan, Leonardo Cotta, Karen Ullrich +2
Knowing the effect of an intervention is critical for human decision-making, but current approaches for causal effect estimation rely on manual data collection and structuring, reg…
APPL: A Prompt Programming Language for Harmonious Integration of Programs and Large Language Model Prompts
Honghua Dong, Qidong Su, Yubo Gao +5
Large Language Models (LLMs) have become increasingly capable of handling diverse tasks with the aid of well-crafted prompts and integration of external tools, but as task complexi…
Minimax Linear Regression under the Quantile Risk
Ayoub El Hanchi, Chris J. Maddison, Murat A. Erdogdu
We study the problem of designing minimax procedures in linear regression under the quantile risk. We start by considering the realizable setting with independent Gaussian noise, w…
Test-Time Fairness and Robustness in Large Language Models
Leonardo Cotta, Chris J. Maddison
Frontier Large Language Models (LLMs) can be socially discriminatory or sensitive to spurious features of their inputs. Because only well-resourced corporations can train frontier…
MixMax: Distributional Robustness in Function Space via Optimal Data Mixtures
Anvith Thudi, Chris J. Maddison
Machine learning models are often required to perform well across several pre-defined settings, such as a set of user groups. Worst-case performance is a common metric to capture t…