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 56
Conformal Prediction for Time Series with Deep Sequence Models
Recent advances in deep learning for time series prediction have amplified the need for reliable uncertainty quantification.Learning Style, Forgetting Semantics: A Case Study of SFT and RFT on Classification Tasks
Why does supervised fine-tuning (SFT) lead to more forgetting than reinforcement fine-tuning (RFT), even when all teacher demonstrations are semantically correct?ENCORE: Exact Non-equilibrium COntrol with Replica Exchange for Diffusion Generation
Inference-time control steers a pretrained generative model towards a target distribution without retraining.High-Dimensional Asymptotics and Dataset Selection for Private Transfer Learning
To commit to buying external data or participate in collaborative learning, one must decide whether the additional data will improve prediction enough to justify the cost.Optimal VC Dimension of Contrastive Learning with Margin
Contrastive learning is a successful paradigm for learning $d$-dimensional geometric representations from a collection of ``anchor--positive--negative'' triplets $(i,j^{+},k^{-})$, indicating that…Optimal Allocation and Volume under Surface
This paper develops a framework for estimation and inference on the volumes of sets that are projections of critical function sets, focusing particularly on the convex body beneath the optimal…Sharp Statistical Rates for Asynchronous TD Learning with Markovian Data
We study the last iterate of standard tabular temporal-difference (TD) learning from a single trajectory of a finite Markov reward process.Amortized Data Borrowing with Exchangeability-Aware Neural Posterior Estimation
Augmenting small concurrent studies with external or historical cohorts is attractive in drug development, where enrollment is slow, follow-up is expensive, and closely related trial or real-world…Warm-starting PDE solvers with any-dimensional machine learning
Any-dimensional machine learning models, such as graph neural networks (GNNs), can be naturally trained and evaluated on inputs of different sizes and dimensions.Flow Matching under Noisy Latent Structure: Beyond Exact Low-Dimensional Support
Flow Matching (FM) learns a velocity field whose ODE transports a simple source distribution to a target law.On the Relaxation of Conditional Independence Assumption for Image Segmentation
In semantic segmentation, a recent line of RankSEG methods directly optimizes Dice/IoU scores at inference time, improving alignment with evaluation metrics without modifying model training.Minimax rates for learning spectral Barron functions by deep ReLU neural networks
We study how well deep neural networks approximate and learn spectral Barron functions.A Rank Graduation metric for Algorithmic fairness
Fairness assessment in algorithmic decisions that affect individuals, such as credit scoring, often relies on parity measures calculated at the aggregate group level.Steepest Guidance: A Practical and Principled Approach to Inference-Time Alignment of Flow and Diffusion-based Models
Inference-time alignment of flow and diffusion-based models is critical for achieving flexible generative modeling.Asymptotic Properties of Support Vector Machines in High-Dimension, Low-Sample-Size Settings under a Spiked Model
In this paper, we consider asymptotic properties of the support vector machine (SVM) in high-dimension, low-sample-size (HDLSS) settings under a spiked model.Minimax Additive Regression under Unknown Dependent Designs
We study additive regression under a potentially non-product random design on $[0,1]^d$, allowing the dimension $d$ to grow with the sample size $n$.Discrete Score Matching Enables Causal Discovery from Count Data
Count data pose a challenge for score-matching-based causal discovery: derivatives are unavailable, and simply replacing them with finite differences does not generally suffice for causal discovery.A Unified Dual Method for Matching Problems
Matching problems are ubiquitous in data science as they enable the alignment of structured objects and distributions.A Dynamical Theory of LoRA in Continual Learning
Despite the widespread use of Low-Rank Adaptation (LoRA), little is known about its dynamics in continual learning and the mechanisms by which low-rank updates affect catastrophic forgetting.Towards Optimal Inventory Control under Censored Demand: A Biased Sample-Average Approximation Approach
We study data-driven multi-period lost-sales inventory control under censored demand, where a stockout reveals only that demand exceeded the stocking level.