Learning-to-Optimize with PAC-Bayesian Guarantees: Theoretical Considerations and Practical Implementation

Subjects: “…Artificial Intelligence & Machine Learning…”
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Sparse Semiparametric Discriminant Analysis for High-dimensional Zero-inflated Data

Subjects: “…Artificial Intelligence & Machine Learning…”
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Stochastic Interpolants: A Unifying Framework for Flows and Diffusions

Subjects: “…Artificial Intelligence & Machine Learning…”
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Efficient Methods for Non-stationary Online Learning

Subjects: “…Artificial Intelligence & Machine Learning…”
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Decentralized Asynchronous Optimization with DADAO allows Decoupling and Acceleration

Subjects: “…Artificial Intelligence & Machine Learning…”
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Mixtures of Gaussian Process Experts with SMC^2

Subjects: “…Artificial Intelligence & Machine Learning…”
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Robust Point Matching with Distance Profiles

Subjects: “…Artificial Intelligence & Machine Learning…”
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BoFire: Bayesian Optimization Framework Intended for Real Experiments

Subjects: “…Artificial Intelligence & Machine Learning…”
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Reliever: Relieving the Burden of Costly Model Fits for Changepoint Detection

Subjects: “…Artificial Intelligence & Machine Learning…”
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Variational Inference for Uncertainty Quantification: an Analysis of Trade-offs

Subjects: “…Artificial Intelligence & Machine Learning…”
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Are Ensembles Getting Better All the Time?

Subjects: “…Artificial Intelligence & Machine Learning…”
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An Adaptive Parameter-free and Projection-free Restarting Level Set Method for Constrained Convex Optimization Under the Error Bound Condition

Subjects: “…Artificial Intelligence & Machine Learning…”
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Operator Learning for Hyperbolic PDEs

Subjects: “…Artificial Intelligence & Machine Learning…”
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Optimal subsampling for high-dimensional partially linear models via machine learning methods

Subjects: “…Artificial Intelligence & Machine Learning…”
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Decentralized Sparse Linear Regression via Gradient-Tracking

Subjects: “…Artificial Intelligence & Machine Learning…”
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Calibrated Inference: Statistical Inference that Accounts for Both Sampling Uncertainty and Distributional Uncertainty

Subjects: “…Artificial Intelligence & Machine Learning…”
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Relaxed Gaussian Process Interpolation: a Goal-Oriented Approach to Bayesian Optimization

Subjects: “…Artificial Intelligence & Machine Learning…”
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Linear Separation Capacity of Self-Supervised Representation Learning

Subjects: “…Artificial Intelligence & Machine Learning…”
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On the Convergence of Projected Policy Gradient for Any Constant Step Sizes

Subjects: “…Artificial Intelligence & Machine Learning…”
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Learning with Linear Function Approximations in Mean-Field Control

Subjects: “…Artificial Intelligence & Machine Learning…”
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