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 9
WASABI: Wasserstein Sampling via Bridge Interpolants
Sampling from Boltzmann distributions is a fundamental problem in physics, chemistry, and biology.On Statistical-Computational Gaps in Graph Combinatorial Testing Problems
We study high-dimensional graph combinatorial hypothesis testing problems in which a signal is supported on the edge set of an unknown subgraph drawn from a class invariant under vertex relabeling.Near-Optimal Second-Order Convex Finite-Sum Optimization under Individual Smoothness
We study the stochastic second-order oracle complexity of solving smooth convex finite-sum optimization with $n$ components.Cost-Aware Stopping for Freeze-Thaw Bayesian Optimization: When Does One-Step Lookahead Suffice?
Freeze-thaw Bayesian optimization (BO) evaluates candidates in resumable increments but usually stops at a budget or convergence test.CausalWAM: Learning the Causes of Actions in World Action Models
World action models (WAMs) learn latent variables from observations and use these to both predict the evolution of the scene and to generate actions.Regret-Optimal Wasserstein-Robust Linear Regression
Distributionally robust optimization (DRO) minimizes worst-case risk under distributional uncertainty, but its decisions can be suboptimal in terms of their excess risk, or regret.Gradient Descent on Two ReLU Neurons: Global Landscape and Bifurcation Dynamics
Understanding how gradient descent learns features remains a central challenge in neural network theory.Instance-dependent sequential limits as an exact information ledger
Instance-dependent limits for bandit regret, sequential tests, best-arm identification and Markov decision processes are proved by one chain.The Prior-Force Family of Implicit Denoiser Priors and Its Decisions in Linear Inverse Problems
Implicit priors given by Gaussian denoisers successfully regularize a wide range of inverse problems in approaches such as plug-and-play (PnP), regularization by denoising (RED) and score-based…ProLand: A New Infeasible Manifold Optimization Method for LoRA Fine-Tuning
Low-rank adaptation (LoRA) enables parameter-efficient fine-tuning but can suffer from redundant basis directions.Model Collapse Is Avoidable Even as Real Data Vanishes: A Sharp Criterion for Iterative Gaussian Estimation
Large generative models are commonly trained on data collected from the open web, which may contain data produced by previous generations of the same model.KINDER: Kernel-based Independence for Fair Representation Learning via Prototype-space Erasure
Concept erasure is a prominent approach to achieving fairness in machine learning through the removal of sensitive attributes from learned representations.Outstrumental Variable Regression
Estimating the causal effect of a treatment $X$ on an outcome $Y$ from observational data in the presence of unobserved confounding requires additional structure or assumptions.Optimistic Gradient Algorithms with Randomized Coordinate and Variance Reduction Strategies for Composite Inclusions
In this paper, we consider a class of possibly nonmonotone finite-sum composite inclusions in which evaluating all components or updating all coordinates at every iteration can be computationally…Transformer-Based Reinforcement Learning for Phase I Dose Allocation
Phase I dose-finding trials must learn from sparse toxicity observations while limiting exposure to overly toxic doses.Statistical Inference under Performativity: From Policy Feedback to Self-Evolving Systems
Performativity arises when prediction-informed decisions change the data-generating process, creating feedback between deployment and learning.Mutual Information Estimation via Triangular Transport Maps: Finite Sample Guarantees and Sample Allocation
We study finite sample guarantees for transport based mutual information estimation.Zero-Complement Extrapolation: Parameter Recovery and Classification in Severely Imbalanced Logistic Regression under Rank-Deficient Regimes
Parameter estimation and classification in binary logistic regression deteriorate severely when the number of minority class observations ($n_1$) is smaller than the feature dimension ($p$).Where Position Matters: Profiling-Guided Layer-Wise RoPE Allocation in Transformers
Rotary Position Embedding (RoPE) typically uses uniform coverage across Transformer layers despite heterogeneous positional requirements.There Is No Free Lunch in RLVR: Every pass@1 Gain Has a pass@k Price
Reinforcement learning with verifiable rewards (RLVR) makes a language model solve more problems on the first try and fewer when given many tries: it raises pass@1 while the base model overtakes it…