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 31
SpectrumQuant: Spectrum Alignment for LLM Quantization
Orthogonal transforms have emerged as an effective tool for low-bit quantization of large language models (LLMs), as they reshape activation distributions without changing the underlying model…Hierarchical Gaussian Processes for Survey Weighting: Local Equivalent Weights in Structured Populations
Regression-based survey weighting connects model-based population estimation with design-based inference through equivalent weights.Beyond Expert Rankings: Contrast Calibration for Mixed-Precision MoE Quantization
Mixture-of-experts (MoE) language models activate only a few experts per token, yet all expert weights must be stored.Fractional is Better: Learnable Derivative Orders in Neural Operator Learning
Neural operators learn mappings between function spaces, enabling fast surrogate solutions to partial differential equations.Posterior Frequencies and the Cost of Not Fitting: A Correction to a Memorization Lower Bound
Feldman's memorization lower bound assigns a frequency coefficient based on how often a point appears in the training sample.When Does Momentum Help in Reinforcement Learning with Verifiable Rewards?
Momentum is a classical tool for accelerating convex optimization and a standard component of modern large language model training.Tracking a Small Set of Experts in Online Convex Optimization
For the fundamental problem of prediction from expert advice, Freund asked whether one could compete with a comparator that switches $k$ times among only $m$ experts.Testing Conditional Distribution Equality Using Generative Models
Conditional distribution equality testing is a core problem with applications in causal inference, model diagnostics, and transfer learning.A polylogarithmic bound for list replicability
Zhang [Zha26] recently posted a manuscript announcing that the list-replicability number of every class of $M\ge 2$ binary concepts on $N\ge 2$ domain points is $O(\log^2 M\log N)$, which would be…Latent Annealed Posterior Sampling with a Variance-Preserving Bridge for Sparsely Observed Random Fields
Posterior inference for random fields from sparse observations presents two major challenges: accurately characterizing complex spatial uncertainty and preventing conditional sampling costs from…Graph Additive Models disentangling feature, topology, and interactions
Graph Neural Additive Networks (GNANs) have emerged as effective, interpretable-by-design architectures for graph learning.On the Stability and Convergence of Flow Map Training
Flow maps are a recent method of probabilistic generation that amortize diffusion trajectories into one-step samplers, unlocking efficient generation.Spectral Indistinguishability and Learning-Curve Identifiability in Quadratic Networks
Motivated by information-organization sculpting, we study spectral summaries and scalar learning curves in centered quadratic networks.Diminishing Returns of Data in Sparse Autoencoders: Functional Behavior does not Guarantee Feature Reproducibility
Sparse autoencoders (SAEs) are typically trained on hundreds of millions to billions of activation tokens, yet little work has tested whether this scale is necessary.Learning Topology-Aware Representations for Mechanics-Relevant TPMS Metamaterials
Learning informative representations of mechanical Triply Periodic Minimal Surface (TPMS) metamaterials is important for mechanics analysis and structural design, yet existing methods have rarely…RoLaQ: Joint Rotation and Range Calibration for 2-Bit KV Caches in Multi-Head Latent Attention
Multi-head latent attention (MLA) compresses the content of a key-value cache into a shared latent per token, making low-bit storage attractive for long-context inference.Selection-Safe Conformal Early Exit: Distribution-Free Coverage under Adaptive Depth
Calibrating a conformal prediction set at each early exit does not preserve coverage once a data-dependent exit is selected.Complete or Sparse: A Tale of Two Identifiabilities
Understanding when neural networks learn interpretable representations is central to mechanistic interpretability.Time-partitioning Gaussian Process Mixture Model
Nonstationarity and heterogeneous dynamics are common in financial and macroeconomic time series.A Mixture-of-Experts Framework for Multi-Stream Quickest Change Detection
A Mixture-of-Experts (MoE) framework is proposed for quickest change detection (QCD) where there are multiple experts, and each expert produces a sequence of statistics for change detection.