15 citations · 22 across the 15 of their papers we have counts for
15 papers
A Gradient Flow Perspective on Minimum MMD Estimation
Sophia Seulkee Kang, Louis Sharrock, Xiaoyuan Cheng +2
Minimum maximum mean discrepancy (MMD) estimation has emerged as a robust and likelihood-free alternative to maximum likelihood estimation for parameter estimation. Yet, despite it…
Thinned Mean Field Langevin Dynamics
Zonghao Chen, Heishiro Kanagawa, François-Xavier Briol +2
Several important learning tasks can be formulated as minimizing an entropy-regularized objective over an appropriate space of probability distributions. Mean-field Langevin dynami…
Sobolev Regularized MMD Gradient Flow
Chenyang Tian, Bharath K. Sriperumbudur, Arthur Gretton +1
We propose Sobolev-regularized Maximum Mean Discrepancy (SrMMD) gradient flow, a regularized variant of maximum mean discrepancy (MMD) gradient flow based on a gradient penalty on…
Fisher Decorator: Refining Flow Policy via a Local Transport Map
Xiaoyuan Cheng, Haoyu Wang, Wenxuan Yuan +4
Recent advances in flow-based offline reinforcement learning (RL) have achieved strong performance by parameterizing policies via flow matching. However, they still face critical t…
BayesSum: Bayesian Quadrature in Discrete Spaces
Sophia Seulkee Kang, François-Xavier Briol, Toni Karvonen +1
This paper addresses the challenging computational problem of estimating intractable expectations over discrete domains. Existing approaches, including Monte Carlo and Russian Roul…
Nonparametric Instrumental Variable Regression with Observed Covariates
Zikai Shen, Zonghao Chen, Dimitri Meunier +3
We study the problem of nonparametric instrumental variable regression with observed covariates, which we refer to as NPIV-O. Compared with standard nonparametric instrumental vari…
Towards a Unified Analysis of Neural Networks in Nonparametric Instrumental Variable Regression: Optimization and Generalization
Zonghao Chen, Atsushi Nitanda, Arthur Gretton +1
We establish the first global convergence result of neural networks for two stage least squares (2SLS) approach in nonparametric instrumental variable regression (NPIV). This is ac…
Stationary MMD Points
Zonghao Chen, Toni Karvonen, Heishiro Kanagawa +2
Approximation of a target probability distribution using a finite set of points is a problem of fundamental importance in numerical integration. Several authors have proposed to se…
Nested Expectations with Kernel Quadrature
Zonghao Chen, Masha Naslidnyk, François-Xavier Briol
This paper considers the challenging computational task of estimating nested expectations. Existing algorithms, such as nested Monte Carlo or multilevel Monte Carlo, are known to b…
(De)-regularized Maximum Mean Discrepancy Gradient Flow
Zonghao Chen, Aratrika Mustafi, Pierre Glaser +3
We introduce a (de)-regularization of the Maximum Mean Discrepancy (DrMMD) and its Wasserstein gradient flow. Existing gradient flows that transport samples from source distributio…
Conditional Bayesian Quadrature
Zonghao Chen, Masha Naslidnyk, Arthur Gretton +1
We propose a novel approach for estimating conditional or parametric expectations in the setting where obtaining samples or evaluating integrands is costly. Through the framework o…
Conformal Counterfactual Inference under Hidden Confounding
Zonghao Chen, Ruocheng Guo, Jean-François Ton +1
Personalized decision making requires the knowledge of potential outcomes under different treatments, and confidence intervals about the potential outcomes further enrich this deci…
Tractable Function-Space Variational Inference in Bayesian Neural Networks
Tim G. J. Rudner, Zonghao Chen, Yee Whye Teh +1
Reliable predictive uncertainty estimation plays an important role in enabling the deployment of neural networks to safety-critical settings. A popular approach for estimating the…
Probabilistic Bilevel Coreset Selection
Xiao Zhou, Renjie Pi, Weizhong Zhang +2
The goal of coreset selection in supervised learning is to produce a weighted subset of data, so that training only on the subset achieves similar performance as training on the en…
Efficient Neural Network Training via Forward and Backward Propagation Sparsification
Xiao Zhou, Weizhong Zhang, Zonghao Chen +2
Sparse training is a natural idea to accelerate the training speed of deep neural networks and save the memory usage, especially since large modern neural networks are significantl…