15 citations · 22 across the 15 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…