JMLR · Scour

Variational Inference for Uncertainty Quantification: an Analysis of Trade-offs

Sparse Semiparametric Discriminant Analysis for High-dimensional Zero-inflated Data

BoFire: Bayesian Optimization Framework Intended for Real Experiments

Efficient Methods for Non-stationary Online Learning

Decentralized Asynchronous Optimization with DADAO allows Decoupling and Acceleration

Stochastic Interpolants: A Unifying Framework for Flows and Diffusions

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

Generation of Geodesics with Actor-Critic Reinforcement Learning to Predict Midpoints

Geometry and Stability of Supervised Learning Problems

Deep Neural Networks are Adaptive to Function Regularity and Data Distribution in Approximation and Estimation

On the Natural Gradient of the Evidence Lower Bound

Reinforcement Learning for Infinite-Dimensional Systems

Understanding Deep Representation Learning via Layerwise Feature Compression and Discrimination

A Hybrid Weighted Nearest Neighbour Classifier for Semi-Supervised Learning

Biological Sequence Kernels with Guaranteed Flexibility

Unified Discrete Diffusion for Categorical Data

Scalable and Adaptive Variational Bayes Methods for Hawkes Processes

Inferring Change Points in High-Dimensional Regression via Approximate Message Passing

Optimal Rates of Kernel Ridge Regression under Source Condition in Large Dimensions

Lexicographic Lipschitz Bandits: New Algorithms and a Lower Bound

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