1 citations · 2 across the 27 of their papers we have counts for
7 papers · 1 filter
Perturbative methods for non-parametric instrumental variable
Wei Bu, Arthur Gretton
We introduce a perturbative approach for nonparametric instrumental variable (NPIV) estimation. By drawing inspiration from perturbation theory in physics, we extend standard kerne…
Semiparametrically Efficient Inference for Kernel Measures of Noise Heterogeneity
Jakub Wornbard, Zikai Shen, Dimitri Meunier +1
We develop semiparametrically efficient inference for kernel measures of noise heterogeneity in additive noise models. In many applications, the regression function is estimated us…
The Illusion of Intervention: Your LLM-Simulated Experiment is an Observational Study
Victoria Lin, Taedong Yun, Maja Matarić +3
Large language models (LLMs) show potential as simulators of human behavior, offering a scalable way to study responses to interventions. However, because LLMs are trained largely…
Spectral Souping: A Unified Framework for Online Preference Alignment
Yinlam Chow, Guy Tennenholtz, Ted Yun +4
Reinforcement Learning from Human Feedback (RLHF) effectively aligns Large Language Models (LLMs) with aggregate human preferences but often fails to address the diverse and confli…
Nonparametric Instrumental Variable Analysis Without Structural Equations: Debiased Inference on Functionals of Inverse Problems with No Solutions
Zikai Shen, Nathan Kallus, Dimitri Meunier +3
We consider debiased inference on finite-dimensional functionals of infinite-dimensional least-squares solutions to inverse problems as a way to avoid having to assume exact soluti…
Semiparametric Efficient Test for Interpretable Distributional Treatment Effects
Houssam Zenati, Arthur Gretton
Distributional treatment effects can be invisible to means: a treatment may preserve average outcomes while changing tails, modes, dispersion, or rare-event probabilities. Kernel t…
Efficient Inference after Directionally Stable Adaptive Experiments
Zikai Shen, Houssam Zenati, Nathan Kallus +3
We study inference on scalar-valued pathwise differentiable targets after adaptive data collection, such as a bandit algorithm. We introduce a novel target-specific condition, dire…