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arXiv:2603.02337v1 Announce Type: new Abstract: Flow matching and score-based diffusion train vector fields under intermediate distributions $p_t$, whose geometry can strongly affect their optimization. We …
arXiv:2603.02348v1 Announce Type: new Abstract: We study diffusion-based model predictive control (Diffusion-MPC) in discrete combinatorial domains using Tetris as a case study. Our planner samples …
arXiv:2603.02349v1 Announce Type: new Abstract: Metapopulation epidemic models are a valuable tool for studying large-scale outbreaks. With the limited availability of epidemic tracing data, it …
arXiv:2603.02356v1 Announce Type: new Abstract: We explore the question of how to learn an optimal search strategy within the example of a parking problem where …
arXiv:2603.02406v1 Announce Type: new Abstract: Generative models have recently advanced $\textit{de novo}$ protein design by learning the statistical regularities of natural structures. However, current approaches …
arXiv:2603.02426v1 Announce Type: new Abstract: We study personalized multi-agent average reward TD learning, in which a collection of agents interacts with different environments and jointly …
arXiv:2603.02429v1 Announce Type: new Abstract: Underdamped Langevin dynamics (ULD) is a widely-used sampler for Gibbs distributions $\pi\propto e^{-V}$, and is often empirically effective in high …
arXiv:2603.02430v1 Announce Type: new Abstract: A central idea of knowledge distillation is to expose relational structure embedded in the teacher's weights for the student to …
arXiv:2603.02439v1 Announce Type: new Abstract: Data-driven models of dynamical systems require extensive amounts of training data. For many practical applications, gathering sufficient data is not …
arXiv:2603.02447v1 Announce Type: new Abstract: Diffusion models are typically trained using pointwise reconstruction objectives that are agnostic to the spectral and multi-scale structure of natural …
arXiv:2603.02452v1 Announce Type: new Abstract: A major focus in designing methods for learning distributions defined on manifolds is to alleviate the need to implicitly learn …
arXiv:2603.02462v1 Announce Type: new Abstract: A key challenge in deriving unified neural solvers for combinatorial optimization (CO) is efficient generalization of models between a given …