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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Newest papers, page 49
Bradley-Terry model under general comparison graphs
The Bradley-Terry model is a parametric model for ranking from pairwise comparisons.When Are Concept Bottleneck Model Explanations Faithful and Compact?
Concept bottleneck models (CBMs) are neural classifiers that allow to explain their decisions via high-level concepts, potentially enabling understanding, steering and debugging.Reconstruction-Aware Distribution Matching for Inverse Problems with Unpaired Data
Recovering the forward operator of an imaging system from unpaired data avoids both calibration hardware and the clean/degraded pairs that supervision requires-pairs that, for a real lens, are often…Watermarking: from Impossibility to Auditable Compliance
Article 50 (2) of the EU Artificial Intelligence Act requires providers of generative systems to make synthetic outputs machine-readable and detectable, while qualifying the effectiveness…Latent Similarity Gaussian Processes: A Theory-Grounded Approach to Personalized Suicide-Risk Forecasting for Clinical Decision-Support
Forecasting suicide risk is difficult due to the high heterogeneity of patients and the low base rate of suicide-related events (SREs).Quantifying the Stability of Multi-Step Reasoning via Error Amplification
We consider the stability of multi-step reasoning processes, which have extensive applications in language models, including chain-of-thought and algorithmic reasoning.On the Comparison of Optimizers for Imbalanced Learning
Data imbalance is pervasive in machine learning, from rare words and anomalies to underrepresented patterns in heterogeneous or cross-tabulated data.Valid Stopping in Adaptive Generator-Verifier Loops
Numerous agentic workflows are based on a generator-verifier loop: a generator proposes candidates, a cheap verifier scores them, and the workflow terminates when a proposal is verified as good…KESurv: A Kernel Ensemble Method for Patient-Specific Survival Prediction
Predicting patient-specific survival functions is crucial for clinicians in making informed decisions about patient care and treatment strategies.SOL: Measuring Gaps between Text Distributions by Double Sliced Wasserstein Metrics
Evaluating text generation requires measuring how well the generated distribution matches the data distribution.LinearPFN: Amortized Variable Selection for Linear Models with Interactions
Spike-and-slab regression is a standard Bayesian formulation of variable selection: it returns a posterior distribution over which candidate effects are active rather than a single selected subset…The Surrogate Is Not the Reward: Post-Surrogate Primary-Outcome Acquisition in Contextual Bandits
We study contextual bandits in which a surrogate is observed after the action but before the learner decides whether to acquire the primary outcome that defines action value and regret.Inverse Cross-spectral Neural Networks for Multivariate Time Series
CoVariance Neural Networks and their extensions have emerged as effective tools for processing multivariate data, deriving graph shift operators directly from second-order statistics.The Birkhoff Geometry of Manifold-Constrained Hyper-Connections: Two Channels, Vertex Viscosity, and Sinkhorn as a Retraction
Hyper-connections widen the residual stream of a Transformer to $n$ parallel streams.A Solvable Model of Adaptive Learning Rate Rescaling: Acceleration, Stability & Scaling
A recurring design principle in modern optimizers is to decouple update magnitude from the raw gradient norm, yet its consequences for learning-curve and resource scaling remain unclear.Hyperbolic Graph Representation Learning: Embed in One Metric, Optimize with Another
Hierarchical graphs embed in hyperbolic space with lower distortion than in Euclidean space owing to its negative curvature.Round-Trip KNN Clustering: multiscale hierarchical cluster detection on directed nearest-neighbour graphs
We introduce Round-Trip KNN Clustering (RTKNNC), a graph-based method for finding cluster structure at several neighbourhood scales without requiring the number of clusters in advance.Block Disentanglement in CRL: Bridging Identifiability and Visual State Estimation
Causal representation learning (CRL) is the process of recovering causally-related latent variables from high-dimensional observations.Private online learning and prediction for Littlestone classes
We study mistake bounds for differentially private online learning and online prediction under oblivious realisable adversaries.Direct Intermediate Initialization for Tilted Diffusion Samplers
Some diffusion posterior samplers construct Gaussian-tilted intermediate distributions along the reverse process.