no code implementations • 8 Aug 2023 • Liyuan Xu, Arthur Gretton
We consider the problem of causal effect estimation with an unobserved confounder, where we observe a proxy variable that is associated with the confounder.
no code implementations • 12 Oct 2022 • Liyuan Xu, Arthur Gretton
We consider the estimation of average and counterfactual treatment effects, under two settings: back-door adjustment and front-door adjustment.
no code implementations • 5 Feb 2022 • Liyuan Xu, Yutian Chen, Arnaud Doucet, Arthur Gretton
We study a nonparametric approach to Bayesian computation via feature means, where the expectation of prior features is updated to yield expected kernel posterior features, based on regression from learned neural net or kernel features of the observations.
no code implementations • 6 Nov 2021 • Rahul Singh, Liyuan Xu, Arthur Gretton
We propose simple nonparametric estimators for mediated and time-varying dose response curves based on kernel ridge regression.
1 code implementation • NeurIPS 2021 • Liyuan Xu, Heishiro Kanagawa, Arthur Gretton
Proxy causal learning (PCL) is a method for estimating the causal effect of treatments on outcomes in the presence of unobserved confounding, using proxies (structured side information) for the confounder.
1 code implementation • 21 May 2021 • Yutian Chen, Liyuan Xu, Caglar Gulcehre, Tom Le Paine, Arthur Gretton, Nando de Freitas, Arnaud Doucet
By applying different IV techniques to OPE, we are not only able to recover previously proposed OPE methods such as model-based techniques but also to obtain competitive new techniques.
1 code implementation • ICLR 2021 • Liyuan Xu, Yutian Chen, Siddarth Srinivasan, Nando de Freitas, Arnaud Doucet, Arthur Gretton
We propose a novel method, deep feature instrumental variable regression (DFIV), to address the case where relations between instruments, treatments, and outcomes may be nonlinear.
no code implementations • 10 Oct 2020 • Rahul Singh, Liyuan Xu, Arthur Gretton
We propose estimators based on kernel ridge regression for nonparametric causal functions such as dose, heterogeneous, and incremental response curves.
no code implementations • 11 Jun 2020 • Han Bao, Takuya Shimada, Liyuan Xu, Issei Sato, Masashi Sugiyama
A classifier built upon the representations is expected to perform well in downstream classification; however, little theory has been given in literature so far and thereby the relationship between similarity and classification has remained elusive.
1 code implementation • NeurIPS 2019 • Liyuan Xu, Junya Honda, Gang Niu, Masashi Sugiyama
We propose two practical methods for uncoupled regression from pairwise comparison data and show that the learned regression model converges to the optimal model with the optimal parametric convergence rate when the target variable distributes uniformly.
no code implementations • 27 Feb 2019 • Yuko Kuroki, Liyuan Xu, Atsushi Miyauchi, Junya Honda, Masashi Sugiyama
Based on our approximation algorithm, we propose novel bandit algorithms for the top-k selection problem, and prove that our algorithms run in polynomial time.
no code implementations • 15 Sep 2018 • Masahiro Kato, Liyuan Xu, Gang Niu, Masashi Sugiyama
In this paper, we propose a novel unified approach to estimating the class-prior and training a classifier alternately.
no code implementations • 14 Sep 2018 • Liyuan Xu, Junya Honda, Masashi Sugiyama
We formulate and study a novel multi-armed bandit problem called the qualitative dueling bandit (QDB) problem, where an agent observes not numeric but qualitative feedback by pulling each arm.
no code implementations • 16 Oct 2017 • Liyuan Xu, Junya Honda, Masashi Sugiyama
We propose the first fully-adaptive algorithm for pure exploration in linear bandits---the task to find the arm with the largest expected reward, which depends on an unknown parameter linearly.