10 papers
A Profile-Separation Framework for Quantitative Convergence of No-U-Turn Samplers
Krishnakumar Balasubramanian
We study multinomial and biased-progressive No-U-Turn Samplers for strongly log-concave targets satisfying and $\|\nabla^2U(x)-\nabla^2U(y)\…
Improved Finite-Particle Convergence Rates for Stein Variational Gradient Descent
Sayan Banerjee, Krishnakumar Balasubramanian, Promit Ghosal
We provide finite-particle convergence rates for the Stein Variational Gradient Descent (SVGD) algorithm in the Kernelized Stein Discrepancy () and Wasserstein-2 metr…
Uniform-in-time Propagation-of-Chaos for Stein Variational Gradient Descent
Krishnakumar Balasubramanian, Sayan Banerjee, Anna Korba
We study uniform-in-time propagation-of-chaos for continuous-time Stein Variational Gradient Descent (SVGD). Classical finite-time propagation-of-chaos estimates for mean-field sys…
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…
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…
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…