5 papers
Counterfactual Generation via Flow Matching: Coupling-Sensitive End-to-End Rates
Yunrui Guan, Krishnakumar Balasubramanian, Shiva Prasad Kasiviswanathan
Counterfactual generation seeks to sample outcomes under a hypothetical intervention or decision using observational data collected under the factual assignment mechanism. We devel…
LIMMT: Less is More for Motion Tracking
Yu Guan, Zekun Qi, Chenghuai Lin +7
We argue that high-quality motion data can steer tracking policies toward better optimization trajectories early in training. In this work, we introduce LIMMT (Less Is More for Mot…
Total Variation Rates for Riemannian Flow Matching
Yunrui Guan, Krishnakumar Balasubramanian, Shiqian Ma
Riemannian flow matching (RFM) extends flow-based generative modeling to data supported on manifolds by learning a time-dependent tangent vector field whose flow-ODE transports a s…
Mirror Flow Matching with Heavy-Tailed Priors for Generative Modeling on Convex Domains
Yunrui Guan, Krishnakumar Balasubramanian, Shiqian Ma
We study generative modeling on convex domains using flow matching and mirror maps, and identify two fundamental challenges. First, standard log-barrier mirror maps induce heavy-ta…
Riemannian Proximal Sampler for High-accuracy Sampling on Manifolds
Yunrui Guan, Krishnakumar Balasubramanian, Shiqian Ma
We introduce the Riemannian Proximal Sampler, a method for sampling from densities defined on Riemannian manifolds. The performance of this sampler critically depends on two key or…