Derives non-asymptotic error bounds for standard, defensive, and self-normalized importance sampling with random KDE proposals from geometrically ergodic Markov chains, separating n^{-1/2} Monte Carlo error from MIAE/MISE proposal error.
and Mannor, Shie and Rubinstein, Reuven Y
8 Pith papers cite this work. Polarity classification is still indexing.
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Authors compute new small two-color ordered and cyclic Ramsey numbers for monotone paths, cycles, stars, complete graphs and nested matchings via SAT solving, determine closed forms for several pairs of graph classes, obtain bounds, apply reinforcement learning for lower bounds, and introduce permut
RLGT is a modular reinforcement learning framework for extremal graph theory that handles undirected, directed, looped, and multi-colored graphs to facilitate future research.
Recasts sampling-based nonconvex optimization as smoothed gradient descent to obtain non-asymptotic convergence guarantees and introduces the DIDA annealed algorithm that converges to the global optimum.
SAM 3D reconstructs 3D objects from single images with geometry, texture, and pose using human-model annotated data at scale and synthetic-to-real training, achieving 5:1 human preference wins.
An active inference model shows normative and explicit cues raise the chance of successful road conflict resolution but can cause collisions if agents violate expectations.
Active inference model unifies human collision avoidance by reproducing meta-analysis aggregates and simulator-specific effects on response timing, maneuver selection, and execution.
Mandatory fresh-air ventilation erodes battery thermal reserve in shared-cooling EVs under derated compressor conditions, and a reserve-aware controller using physics-guided ML surrogate and barrier functions restores joint feasibility with lower energy.
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RLGT: A reinforcement learning framework for extremal graph theory
RLGT is a modular reinforcement learning framework for extremal graph theory that handles undirected, directed, looped, and multi-colored graphs to facilitate future research.
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Global Convergence of Sampling-Based Nonconvex Optimization through Diffusion-Style Smoothing
Recasts sampling-based nonconvex optimization as smoothed gradient descent to obtain non-asymptotic convergence guarantees and introduces the DIDA annealed algorithm that converges to the global optimum.