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Average-reward model-free reinforcement learning: a systematic review and literature mapping
Model-free reinforcement learning (RL) has been an active area of resear...
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Stochastic Normalizing Flows
We introduce stochastic normalizing flows, an extension of continuous no...
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Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite Networks
Analysing and computing with Gaussian processes arising from infinitely ...
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The reproducing Stein kernel approach for post-hoc corrected sampling
Stein importance sampling is a widely applicable technique based on kern...
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Richer priors for infinitely wide multi-layer perceptrons
It is well-known that the distribution over functions induced through a ...
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LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data
We apply methods from randomized numerical linear algebra (RandNLA) to d...
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Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings
Graph embeddings, a class of dimensionality reduction techniques designe...
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Implicit Langevin Algorithms for Sampling From Log-concave Densities
For sampling from a log-concave density, we study implicit integrators r...
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DINGO: Distributed Newton-Type Method for Gradient-Norm Optimization
For optimization of a sum of functions in a distributed computing enviro...
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Newton-MR: Newton's Method Without Smoothness or Convexity
Establishing global convergence of the classical Newton's method has lon...
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