1 citations · 2 across the 27 of their papers we have counts for
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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…
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…
Density Ratio-Free Doubly Robust Proxy Causal Learning
Bariscan Bozkurt, Houssam Zenati, Dimitri Meunier +2
We study the problem of causal function estimation in the Proxy Causal Learning (PCL) framework, where confounders are not observed but proxies for the confounders are available. T…
Regularized least squares learning with heavy-tailed noise is minimax optimal
Mattes Mollenhauer, Nicole Mücke, Dimitri Meunier +1
This paper examines the performance of ridge regression in reproducing kernel Hilbert spaces in the presence of noise that exhibits a finite number of higher moments. We establish…
Density Ratio-based Proxy Causal Learning Without Density Ratios
Bariscan Bozkurt, Ben Deaner, Dimitri Meunier +2
We address the setting of Proxy Causal Learning (PCL), which has the goal of estimating causal effects from observed data in the presence of hidden confounding. Proxy methods accom…
Learning-Order Autoregressive Models with Application to Molecular Graph Generation
Zhe Wang, Jiaxin Shi, Nicolas Heess +2
Autoregressive models (ARMs) have become the workhorse for sequence generation tasks, since many problems can be modeled as next-token prediction. While there appears to be a natur…
A Unified Data Representation Learning for Non-parametric Two-sample Testing
Xunye Tian, Liuhua Peng, Zhijian Zhou +3
Learning effective data representations has been crucial in non-parametric two-sample testing. Common approaches will first split data into training and test sets and then learn da…
Foundations of Multivariate Distributional Reinforcement Learning
Harley Wiltzer, Jesse Farebrother, Arthur Gretton +1
In reinforcement learning (RL), the consideration of multivariate reward signals has led to fundamental advancements in multi-objective decision-making, transfer learning, and repr…
Spectral Representation for Causal Estimation with Hidden Confounders
Haotian Sun, Antoine Moulin, Tongzheng Ren +2
We address the problem of causal effect estimation where hidden confounders are present, with a focus on two settings: instrumental variable regression with additional observed con…
Mind the Graph When Balancing Data for Fairness or Robustness
Jessica Schrouff, Alexis Bellot, Amal Rannen-Triki +5
Failures of fairness or robustness in machine learning predictive settings can be due to undesired dependencies between covariates, outcomes and auxiliary factors of variation. A c…
Deep MMD Gradient Flow without adversarial training
Alexandre Galashov, Valentin de Bortoli, Arthur Gretton
We propose a gradient flow procedure for generative modeling by transporting particles from an initial source distribution to a target distribution, where the gradient field on the…
Proxy Methods for Domain Adaptation
Katherine Tsai, Stephen R. Pfohl, Olawale Salaudeen +5
We study the problem of domain adaptation under distribution shift, where the shift is due to a change in the distribution of an unobserved, latent variable that confounds both the…
A Distributional Analogue to the Successor Representation
Harley Wiltzer, Jesse Farebrother, Arthur Gretton +5
This paper contributes a new approach for distributional reinforcement learning which elucidates a clean separation of transition structure and reward in the learning process. Anal…