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 70
Atelier: Learning Local Self-Supervised Features for CryoEM Volumes via Hypernetworks
CryoEM map interpretation requires features that are spatially localized, consistent across samples, and informative across spatial scales.Entropy Regularization: A Free Correction to Cross-Entropy for Verified Demonstrations
Large language models are often post-trained on expert demonstrations using cross-entropy (CE), even when the downstream objective is not to imitate the demonstrated solution but to produce any…Change detection with conformal martingales: new optimal constructions, and suboptimality of existing methods
We study distribution-free sequential changepoint detection for independent observations with unknown and unrestricted pre- and post-change laws.Artificial intelligence surrogates for treatment effect estimation with before-and-after data
Estimating the causal effects of medical treatments is difficult when clinically important outcomes are costly to measure or require long follow-up.Prediction with Expert Advice: Anytime Regret with Many Experts Matches the Fixed-Time Constant
Prediction with expert advice is a fundamental problem in online learning.On the Sample Complexity of Active Learning with Membership Queries
This work revisits a fundamental question in active learning: how powerful is the ability to synthesize arbitrary queries?Functional Causal Discovery via Conditional Covariance Ordering
We study causal discovery where each node is a random function.Multitask Regression with Pairwise Fusion
We study multitask regression when coefficient sharing can differ by predictor.Discrete Diffusion Models via Evolving Variational Autoregressive Networks
Conventional score-based diffusion models learn scores without representing normalized densities, whereas tractable normalized models support both sampling and direct likelihood evaluation.Counterfactual Constraint-Conditioned On-Policy Distillation for Multi-Constraint Instruction Following
Multi-constraint instruction following requires a model to respond to a query under many simultaneously active constraints.Robustness of Diffusion Models under Distribution Shift
Score-based diffusion models are increasingly considered in settings where the underlying data distribution may differ from the training distribution, yet existing theoretical guarantees largely…FedIncome: Federated Learning for Income Estimation in Digital Lending Under Data Sovereignty Constraints
Verified income is often unavailable in digital loan applications, forcing lenders to rely on reported income and potentially leading to over-lending, overly conservative offers, or rejection of…Improving Ensemble Filters with Flow Matching
Data assimilation estimates a dynamical state from partial and noisy observations.NPBoost: Neural Processes with Gradient-Boosted Fixed Effects
Neural Processes (NPs) are model-based meta-learners that implicitly learn a stochastic process and adapt to a new task from a small context set.Rank-One Signal Recovery in Sparse Wishart Noise
We study the high-dimensional recovery of a signal vector $\mathbf{x}$ in the presence of sparse Wishart-like noise.How Sensitive Are LLM Leaderboard Claims to Hidden Model Selection?
LLM leaderboard gains can reflect selection among privately evaluated model variants, yet neither the number of variants nor their dependence is public.Local Geometric Mixing via Dobrushin Contraction with Applications to Diffusion Path Monte Carlo and the Proximal Sampler
Local geometric mixing localizes geometric mixing by requiring geometric convergence to equilibrium in total variation only over finitely many transitions.Memory-Conditioned Diffusion Model for Generalized Langevin Dynamics
Generalized Langevin equations describe non-Markovian dynamics in which the evolution of resolved variables depends on their past.An Order-Theoretic Characterization of Consistent Inductive Inference
When can a learner make only finitely many prediction errors along every infinite sequence labeled by a fixed, unknown hypothesis?Speculative Evaluation of Stochastic LLMs
Evaluating a stochastic large language model is costly: benchmark scores estimate expected performance from randomized rollouts, yet uniform repetition ignores sharp differences in task-level…