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Machine Learning
The newest papers in Machine Learning that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
The newest 5,000 papers, from 12 October 2026 to 5 November 2026: 5,000 arXiv preprints.
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Newest papers, page 80
What Does LOL Measure? Hidden Label Shifts in Low-Light Enhancement Benchmarks
LOL-v1 and LOLv2-Real are the standard benchmarks for supervised low-light image enhancement, and several leading methods ship hand-set test-time adjustments with their evaluation scripts.MiniMMD: Minimization of MMD for Sampling, Inference and Integration
Many problems in computational statistics and machine learning can be formulated as approximating a target distribution by minimizing a discrepancy over a restricted class of candidate distributions.A Separation Principle for Adversarial Attacks and Defense of Graph Neural Networks
Adversarial attacks on graph neural networks (GNNs) have been studied as a model-centric optimization problem driven by gradients, learned policies, or meta-learning, with the graph treated as a…On Worst-Case Guarantees for Graph-Based Nearest-Neighbor Search
Graph-based nearest neighbor search algorithms like HNSW, DiskANN, NSG, and others have surged in popularity in recent years due to their excellent empirical performance in comparison to…Estimating Implicit Expected Log Density Ratios
When exact probabilities cannot be computed, \emph{density ratio estimation (DRE)} is a key subroutine in the wide range of applications where density ratios appear, from experimental design to…MASS: Multi-mask Selective Self-Distillation for Leakage-Resilient Reasoning
On-policy self-distillation enables the same model to act as student and teacher, with dense token-level supervision obtained by the teacher being given access to privileged information.Floor It, Then Fill It: Simulating Creator Ecosystems for Fair Video-Content Recommendation Systems at Billion User Scale
Engagement-optimized content recommendation systems exhibit a well-documented rich-get-richer dynamic that structurally disadvantages emerging creators on large-scale platforms.Replaying the Same Gradients at a Different $\varepsilon$: A Registered Test of the Fixed-Trajectory Account of Adam's $\varepsilon$ for One Rare Row
Adam divides every update by the square root of a running average of squared gradients plus a small constant, epsilon.B2P-Corr: Batch-to-Population Gradient Estimators for Non-Decomposable Correlation Losses
Correlation metrics such as the Pearson Correlation Coefficient (PCC) and Concordance Correlation Coefficient (CCC) are standard regression metrics and learning objectives, but their…Data Amount Heterogeneity Improves Performance in Federated Learning for Neuroimaging
Federated Learning (FL) allows for secure analysis of distributed data without data sharing.A Gradient based Adversarial Augmentation Framework for Mitigating Unlearnable Datasets
Unlearnable data methods (ULD) protect public data from unauthorized model training by adding imperceptible perturbations that create a learning shortcut.In-Context Learning for Multi-Fidelity Multiscale Model Closure and Cross-Operator Out-Of-Distribution Generalization
Multi-fidelity learning uses low-fidelity information (coarse-scale PDE models) together with limited high-fidelity data (fine-scale solutions) to obtain high-accuracy PDE solutions.Barren Plateaus in Quantum Linear Combinations: Interference and Lie-Algebraic Control
A coherent sum of quantum circuits introduces interference that can alter the training signal.GearUp: DAG-aware Acceleration for Serverless Inference Serving via Slack-guided Pre-loading
Serverless platforms increasingly host machine learning (ML) inference workflows, modeled as directed acyclic graphs (DAGs) of interdependent functions.Convex-SKM-Net: Low-Latency Block Halfspace Corrections for Neural Convex Constraint Satisfaction
Fast inference and strict constraint satisfaction are essential for learning-based decision-making.Embodied at Sea: Imitation Learning for Multi-Task Underwater Manipulation
Behavior Cloning has demonstrated promising generalization capabilities for robotic manipulation in terrestrial environments.Averaged Annealed Markov Chains Achieve the Algorithmic Threshold for Tensor PCA, Part II: Langevin Dynamics
We consider the tensor principal components analysis (tensor PCA) problem on the sphere, in which we are asked to recover an unknown vector in $\mathcal{S}_N=\{x\in\mathbb{R}^N:\lVert x\rVert^2=N\}$…Progressive Coupling Method for Diffusion Posterior Sampling in Ptychography
Ptychography is a computational imaging method that reconstructs a sample from overlapping diffraction measurements.RainCheck: Harnessing Historical Weather Data with Machine Learning for Rainfall Forecasting in Bangladesh
Machine-learning rainfall forecasts are often evaluated by shuffling station records into random training and test sets.Skeleton Coding: Program Structure as a Specification for Agents
Coding agents are typically provided end-to-end behavioral specifications that the code they produce should satisfy, such as tests and input--output examples.