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.
Get the few papers worth your time in Machine Learning (Statistics) every morning.
Follow Machine Learning (Statistics) and ReadSearch reads its new papers each day, then brings you the few worth reading, each with its key finding in a sentence.
Newest papers, page 60
Pointwise or Pairwise: When Do Pairwise Losses Help Reward Learning, Provably?
Pairwise losses are increasingly used for reward learning even when pointwise rewards are observed, with mixed empirical results.Interacting particle guidance for sampling reward-tilted generative priors
Inference-time steering adapts pretrained diffusion and flow-based models to new tasks, e.g., to generate samples from a conditional distribution or samples with desired properties, without…A Sharp Transition in Data Reconstruction under Differential Privacy
Data reconstruction attacks have empirically been successful in recovering training samples from learned models, raising privacy concerns and motivating defenses with guarantees that remain valid…High-Dimensional Simulation-Based Inference in Latent Spaces
Neural simulation-based inference (SBI) has been widely successful in inferring a relatively small number of interpretable parameters from potentially high-dimensional observations, such as images…A stochastic subgradient method with optimal failure exponent
Fix a target accuracy $\varepsilon$, a gradient-noise level $s$, and a horizon $N$.Physical Muon: Orthogonalization as an Equilibrium Computation
Physical neural networks and analog in-memory computing could reduce the energy cost of neural network training.Why Adaptive Optimizers Underestimate Rare Tokens
In the softmax output layer, a rare token receives a small positive logit gradient on most steps and a much larger negative gradient on the few steps when it is the target.Ornstein-Uhlenbeck Is Hard to Beat, Yet Superlinear Drift Ships Lower Transport Costs
Brešar and Mijatović \cite{bresar2025} show that Ornstein--Uhlenbeck diffusion is hard to beat in forward convergence under assumptions that exclude superlinear drift.XU-RS: Explaining Credal Width in Random-Set Language Models
Uncertainty estimates tell us how unsure a model is, but not why.RLTL;DR: Self-improvement by Internalizing Self-generated Feedback
The common paradigm of reinforcement learning with verifiable rewards (RLVR) is to let agents make multiple attempts at a task, and optimize towards the successful ones.A Finslerian Approach for Embedding Directed Data
Many datasets carry an intrinsic directionality: citations point backward in time, cells differentiate along lineages, and traffic follows preferred routes.The double descent and Runge phenomena in overparametrized polynomial interpolation
The Runge phenomenon in polynomial interpolation is often considered a classical analogue of the double descent phenomenon in machine learning.SPARK: A General Goodness-of-Fit Assessment via Residual Projection
Goodness-of-fit testing is a basic tool for assessing whether a fitted procedure has captured the systematic information contained in the covariates.When Noise Estimation Hides Basis Misspecification in Repeated Bayesian Inverse Problems
Basis-restricted priors in Bayesian inverse problems can lose coverage when the truth has components outside the basis.Counterfactual Probing for Parallel Unmasking with Hidden Forest Structure
Masked generative models offer parallel token prediction, but accurate parallel sampling must account for dependencies among tokens.Search Dimension in Unlabeled Projection Pursuit: A Scaling Law for Subspace Restriction
Projection pursuit searches for a direction along which the data look least Gaussian.Post-Anomaly Detection Inference for Deep SVDD
Deep Support Vector Data Description (Deep SVDD) has become a prominent framework for unsupervised anomaly detection by learning latent representations that compactly characterize normal data around…Identifiability Guarantees for Drivers and Dynamics of Delayed Physical Systems
A wide range of methods have been proposed, including physics-informed neural networks, which are powerful but do not guarantee identifiability of the dynamics, symbolic regression, which requires a…A shape-similarity latent space for fluid interfaces: invertible reduced-order modelling of droplet morphology
A droplet breaks up in tens of microseconds, and a recording captures perhaps a dozen frames.Mutual Information Constrained Chernoff Bottleneck
The classical information bottleneck (IB) measures the relevance of a representation $U$ of $X$ to a target $Y$ by $I(U;Y)$, which does not directly characterize the error of downstream decisions.