AI & Machine Learning · Computer Science & AI
Machine Learning (Statistics)
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1,749 papers, from 4 September 2026 to 5 November 2026: 1,749 arXiv preprints.
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Newest papers, page 68
Beyond Empirical Support: Structured Outlier Generation via Sinkhorn Optimal Transport
Outliers are essential for evaluating and improving the robustness of machine learning systems, especially when future distributions may differ significantly from historical training data.Uncertainty and Explainability in Deep Rough Volatility: A Neural Information-Theoretic Posterior Approach
Deep learning has substantially accelerated the calibration of complex stochastic-volatility models, but neural point calibration alone does not capture the uncertainty remaining after an…First-Order Stationarity of Reverse Diffusions
Recent literature has shown a strong connection between optimization and sampling.Simple Extensions of Single-Objective Acquisition Functions and Hedge Strategies for Multi-Objective Bayesian Optimization
Multi-objective Bayesian optimization (MOBO) is commonly approached through specialized acquisition functions or scalarization schemes designed to explicitly account for trade-offs among…Bridging Stochastic Flow Maps and Boltzmann Generators with Normalizing Flows
Generating independent, equilibrium samples of molecular systems at scale remains a central obstacle in computational statistical mechanics.What Does the Rank Buy? A Spectral and Distributional Analysis of Low-Rank Adaptation
The rank $r$ in LoRA is widely treated as a capacity control: a smaller rank is assumed to yield a simpler model that generalizes better.Continuous-Time Trajectory Generation from Discrete Observations with Stochasticity
Physical systems evolve continuously in time, yet their states are typically observed only at discrete times.Back to the Definition: Estimating Step-Level Advantages via Trajectory Graphs for Agentic Reinforcement Learning
Group-based reinforcement learning (RL) methods, such as GRPO and its variants, have become a leading paradigm for training reasoning and agentic large language models (LLMs).When Does Action Credit Need Updating?
Tool-using agents are continually updated with new interaction data.Personalised federated learning for Riemannian and Euclidean EEG decoding
Federated learning (FL) lets EEG decoders learn from recordings of several subjects without pooling them.Transformers as Cross-Task Learners: Shared Structure Drives Sample Efficiency in In-Context Learning
Transformers achieve remarkable performance by jointly learning broad families of tasks during pretraining and adapting to unseen tasks from only a short prompt.Feature Space Selection and Heterogeneous Effect Estimation for Blood-Brain Barrier Permeability: A Random Forest to the Generalized Random Forest Pipeline
Predicting blood-brain barrier (BBB) permeability is critical for central nervous system drug discovery.Functional dynamic mode decomposition: Learning infinite-dimensional systems from data
Dynamic mode decomposition (DMD) is a data-driven method that computes the best linear approximation of the underlying dynamical system and decomposes the dynamics into a superposition of…FB-GDM: Fully-Bayesian Guided Diffusion Models for High-Dimensional Linear Inverse Problems via Unsupervised Variational Inference
Diffusion models are powerful priors for linear inverse problems, but the reference guidance methods, Diffusion Posterior Sampling (DPS) and Pseudoinverse-Guided Diffusion Models ($Π$GDM), rely on…Sufficiently Reduced Distributional Regression
We propose Sufficiently Reduced Distributional Regression (SRDR), a generative method that combines conditional distribution estimation with nonlinear sufficient dimension reduction (SDR).GCUL: Ambiguity Identification in Text Emotion Classification via Cluster-Guided Learning
Selective classification enables a model to abstain from predictions on uncertain instances, but existing approaches typically reject them through confidence scores, predefined coverage constraints…Learning a Flow to Self-Supervised Representations
Explicit geometric references offer a direct way to structure self-supervised representations.Machine Unlearning for Gibbs Supervised Learning Algorithms
In this paper, a method for achieving exact unlearning for Gibbs supervised learning algorithms is proposed using a variational formulation inspired by empirical risk minimization subject to…Neural Transport Nested Sampling
Sampling from Boltzmann distributions of molecular systems is an inference problem that has seen significant recent developments fuelled by advances in neural density estimation.Direct Message Approximation (DMA): A Consistency-Based Framework for Tractable Approximate Inference on Factor Graphs
Approximate message passing on factor graphs underlies two dominant families of probabilistic inference algorithms: expectation propagation (EP) and variational message passing (VMP).