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stat.ML2026
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…
stat.ML2026
Finite-Particle Rates for Regularized Stein Variational Gradient Descent
Ye He, Krishnakumar Balasubramanian, Sayan Banerjee +1
We derive finite-particle rates for the regularized Stein variational gradient descent (R-SVGD) algorithm introduced by He et al. (2024) that corrects the constant-order bias of th…
stat.ML2026
Dependence-Aware Label Aggregation for LLM-as-a-Judge via Ising Models
Krishnakumar Balasubramanian, Aleksandr Podkopaev, Shiva Prasad Kasiviswanathan
Large-scale AI evaluation increasingly relies on aggregating binary judgments from annotators, including LLMs used as judges. Most classical methods, e.g., Dawid-Skene or (weig…