AI & Machine Learning · Computer Science & AI
Machine Learning (Statistics)
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1,749 papers, from 4 September 2026 to 5 November 2026: 1,749 arXiv preprints.
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Newest papers, page 84
Chopthin-Consensus Power Sampling: A Diversity-Preserving Approach to LLM Decoding
Inference-time power sampling via Sequential Monte Carlo (SMC) can substantially improve large language model (LLM) reasoning without requiring post-training.Adaptive Conformal Redistribution for Inter-class Transitional Uncertainty in Medical Image Classification
Medical image classification is frequently complicated by transitional categories whose feature distributions overlap those of adjacent classes, producing ambiguous decision boundaries.High-probability guarantees for linear accessibility in feature superposition
Neural networks can leverage feature superposition to encode more concepts than dimensions, but cross-feature interference constrains the linear accessibility of simultaneously active features.Learning with Synthetic Data via SGD in High-Dimensional Linear Regression
Synthetic data has become a promising way to scale model training beyond limited human-generated data but it may also induce strong model collapse (Dohmatob et al., 2024), where any fixed fraction…Distillation of Synthetic Data for Time Series Foundation Models
Time series foundation models (TSFMs) are increasingly pre-trained on synthetically generated time series trajectories, where the data generating process is known.Why Learning Rediscovers the Closed-Form Diagonal Regularizer
We identify a diagonal saturation principle in modal inverse problems: when truncation noise is isotropic, the Bayes-optimal Tikhonov shape is a closed-form power law Gamma_k proportional to…Muon-C: Operator-Aligned Muon for Convolutional Kernels
Muon replaces matrix momentum with an approximately orthogonal polar direction, but its geometry depends on the matrix representation.A Unifying Perspective on Probabilities as Model Predictions
Although probabilistic statements are ubiquitous, foundational disagreements persist about their understanding, as exemplified by debates between Bayesians and frequentists; moreover, it is unclear…Beyond Conventional Federated Learning via High-Order Regularization
Federated clients that perform several local optimization steps can return parameter displacements with widely different magnitudes.FlowCPO: A Unified Divergence View of Preference Alignment for Flow Models
Preference alignment for flow and diffusion models now spans online reinforcement learning and offline preference optimization, but the relation between these methods remains unclear.Adversarial Training for Tabular Credit Scoring: A Multi-Attack Robustness Evaluation in P2P Lending
Machine learning-based credit scoring is increasingly central to Peer-to-Peer (P2P) lending, yet its resilience to adversarial manipulation, where applicants strategically alter self-reported inputs…Optimal Value Inference for Reinforcement Learning
We study offline inference for the optimal value in reinforcement learning.A statistical approach to bias in zero-shot learning: the lens of handwriting recognition
Generalized zero-shot learning (GZSL) has emerged as an important paradigm for visual recognition systems that must generalize to classes that were not observed during training.An Exponential Deterministic--Randomized Gap in ERM-Oracle Complexity for Thresholds on an Unknown Order
Attias, Hanneke and Ramaswami (NeurIPS 2025) asked whether randomization provably reduces the oracle calls needed for online learning when the class is accessible only through an oracle.Semiparametric Inference for Conditional Shapley Feature Importance
Shapley values are widely used for post-hoc feature attribution, but most estimators return point quantities and do not quantify uncertainty, and popular implementations sample out-of-coalition…A positive resolution of the gap-entropy conjecture
We prove the gap-entropy conjecture for fixed-confidence best-arm identification with independent unit-variance Gaussian arms, means in $[0,1]$, and a unique optimal arm.Likelihood-free inference with nuisance parameters through normalizing flows
We present a simple decomposition of a neural-network-based normalizing flow that naturally uncovers a pivotal statistic (or something close) in the presence of nuisance parameters, based only on a…A Quantum-Inspired Dequantization Method for Diagonally Weighted Matrix Functions: Application to Learning with Optimized Random Features
Quantum-inspired classical algorithms have dequantized several quantum machine learning routines by replacing quantum linear-algebra subroutines with classical counterparts.Conformal Calibration Transfer
Conformal prediction converts point predictions into set-valued predictions with coverage guarantees under exchangeability between calibration and deployment data.Weighted Empirical Risk Minimization for Machine Learning under Long-Range Dependence: Exact Pathwise Rates and Learning-Error Geometry
We develop an exact almost-sure learning theory for smooth parametric models trained by regularly weighted empirical risk minimization on long-range dependent data.