AI & Machine Learning · Computer Science & AI
Machine Learning (Statistics)
The newest papers in Machine Learning (Statistics) that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
1,749 papers, from 4 September 2026 to 5 November 2026: 1,749 arXiv preprints.
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From Global to Tail Convergence: Dynamics of $α$-Stable Score-Based Generative Models
Score-based generative models driven by $α$-stable L{é}vy processes have been proposed as an alternative to Brownian diffusion models, with empirical evidence suggesting improved sampling and better…SNACK: A Sequential Notation Framework for Probabilistic Graph Generation
We introduce SNACK, a string representation for probabilistic graph generation that enables sequence models to generate and model graphs with discrete node and edge features.Asymptotically Exact Negative Guidance of Diffusion Models via Positive-Unlabeled Learning
Negative guidance of diffusion models generates samples that avoid an undesired condition, specified either by reference samples or by a conditional score alongside an unconditional model.Efficient contextual partial monitoring with general function approximation
We study finite stochastic partial monitoring (PM) games with side information, a paradigm of sequential learning with partial feedback.Calibration and Risk-Budgeted Decisions with System One Models
LLM workflows and agents rely on a set of binary decisions like allow/block an input, or discard/retain a retrieved document.Text-Informed Training Data Filtering for Enhanced Virtual Flow Meter Performance
In sectors such as energy, aquaculture and chemical processing, the effectiveness of both mechanistic and data-driven models is frequently limited by poor-quality training data, which obscures…On the Optimisation Landscape of Overparameterised Weighted MMD
We study the optimisation landscape of the maximum mean discrepancy (MMD) over weighted atomic measures.Neural Wasserstein Splines: Representation-Invariant Second-Order Priors for Trajectory Inference
Population dynamics are often observed only as unpaired snapshots, which leave the evolution between measurements underdetermined.Maximum likelihood, neural networks and posterior mean for inferring fractional Brownian motion parameters
Fractional Brownian motion (fBM) is a model of anomalous diffusion parameterised by a Hurst exponent $H \in (0,1)$ and a generalised diffusion coefficient $D_H > 0$.Convergence Rates of Gaussian Process Thompson Sampling for Efficient Global Optimization
Efficient global optimization aims to identify a global optimum of an expensive black-box function within a limited evaluation budget $N$.Minimax estimation of the pseudo-spectral gap of continuous Markov chains, with applications to empirical concentration bounds
We study the minimax estimation of the pseudo-spectral gap $γ_{ps}$ of a Markov chain from the observation of a single trajectory.Outperforming 1000 Euclidean Layers With Hyperbolic Goal-Conditioned RL
Scaling self-supervised contrastive reinforcement learning (CRL) to 1000-layer Euclidean neural networks has enabled state-of-the-art goal-reaching performance, yet wall-clock time scales linearly…MCMC Can See Can't See
It is folklore that passing "convergence" diagnostics does not guarantee that a Markov chain has converged.Weighted Adjoint Matching: Finetuning Flow Matching Models with Time-Dependent Control Cost
We introduce Weighted Adjoint Matching, a method to finetune a pretrained diffusion or flow matching model given a differentiable reward.A Large-Dimensional Analysis of Least-Squares Federated Learning via Generalized Total Variation Minimization
Federated learning over decentralized networks inherently trades off the suppression of local finite-sample noise through collaboration against the bias that collaboration introduces between…Optimizing Sparse Boolean Rule Networks using Mixed Integer Linear Programming
We study the problem of learning accurate and cognitively simple binary classifiers that are directly interpretable as Boolean rules.Mixed Poisson diffusion models and application to posterior sampling
Diffusion models are a powerful framework for learning complex probability distributions.The minimal width of neural networks and immersions of real projective space
We investigate the minimal width of deep neural networks that can approximate all continuous functions from $\mathbb{R}^n$ to $\mathbb{R}^m$ in the topology of uniform convergence on compact subsets.MAEM: Mitigating Recency Bias via a Replay-Free, Capacity-Controlled Dynamic Mixture of Experts for Class-Incremental Graph Learning
Graph Neural Networks (GNNs) excel on graph-structured data but suffer catastrophic forgetting when classes arrive sequentially.From Plato to Aristotle: Toward a General Theory of Representation Hypotheses
The Platonic Representation Hypothesis (PRH) holds that increasingly capable models converge toward a common representation.