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 32
Flexible Doubly Robust Proximal Causal Inference for Censored Outcomes
Estimating causal effects on right-censored time-to-event outcomes is challenging under unmeasured confounding.Stochastic Gradient Descent: A Nonlinear Time Series Perspective
This paper investigates the statistical behavior of Stochastic Gradient Descent (SGD) with constant step size under the framework of iterated random functions.Sign Betting for Misspecified Regression: Variance Adaptivity and Optimal Approximation
We study bounded regression under model misspecification, evaluating a function $f$ by its $L_1(P_X)$ distance to the conditional mean $η(x)=\mathbb{E}[Y\mid X=x]$, rather than the standard…Ranked Right, Sized Wrong: Ensemble Spread Fails Silently at Streamflow Extremes
Deep learning models, particularly Long Short-Term Memory networks, are increasingly used for operational streamflow forecasting, with ensemble spread providing a signal of predictive uncertainty.Two-Stage Score Estimation for Group-Specific Diffusion Models
Generative modeling across related distributions raises a question: how can shared structure support accurate generation for a target distribution with limited observations?Halve-Then-Greedy: Adaptive Sampling for Posterior Success with Near-Optimal Instance Dependence
We study fixed-budget adaptive experimentation aimed at maximizing the probability of obtaining sufficient posterior evidence that a recommended design's mean performance exceeds a target.Failure Is the Mother of Success: Recovering Supervision with Grounding Hindsight
When every rollout for a problem fails, GRPO receives neither a successful exemplar nor a nonzero group-relative advantage, so it learns nothing from exactly the problems it most needs to learn.Acceleration and its Limits in Gaussian Variational Inference
We study whether the classical momentum-based acceleration that improves the condition-number dependence of gradient descent from $κ$ to $\sqrtκ$ for strongly convex, smooth objectives extends to…On the Last-Iterate Convergence of Clipped Gradient Methods
In stochastic (strongly) convex optimization, establishing last-iterate convergence guarantees for first-order methods has recently garnered increasing attention, as this not only deepens the…Risk-Averse Inference-Time Diffusion Model Alignment with Finite-Sample Guarantees
Inference-time alignment steers diffusion models toward downstream objectives without costly fine-tuning, but existing methods typically optimize expected cost (or reward) without explicitly…textGrain watermarking is harder to detect: can sum-tests still do it optimally?
Watermarking embeds an imperceptible signal in text from large language models that is statistically detectable with a secret key.Structured SDEs for Realized GARCH: Continuous-Time Volatility Modelling and Component-Level Analysis
We propose a structured continuous-time stochastic volatility model for one-day-ahead integrated-variance forecasting.Gaussian Process Bandits for AI System Optimization with Selectively Verified Feedback
Gaussian process bandits exploit similarities among configurations to optimize prompts, retrieval systems, and agent workflows.Linear Regression Beyond Covariance: Latent Slope Spectra from Higher-Order Cumulants
Least squares summarizes the joint distribution of a response and regressor through second-order information.Follow the Perturbed Leader for Linear Bandits: $\sqrt{T}$ Regret over General Action Sets
We give the first adversarial linear bandit algorithm based on Follow-the-Perturbed-Leader (FTPL) that achieves $\sqrt{T}$ regret over general action sets.Learning Distributions from Samples and Qualities
We study parameter estimation when each sample from a continuous parametric distribution is accompanied by a binary quality label governed by the same unknown parameter.Counterfactual Certification for Sequential Unlearning with Long-Run Performance Guarantees
Certified machine unlearning ensures the proximity between the unlearned model and a model retrained without the data to be forgotten.Weighting-Based Strategies for Classification: Analysis and Local Peak Dynamics with CART Trees
Tabular learning tasks often suffer from class imbalance, which is often addressed via weighting-based strategies.ndscape: a structural map of the nested dichotomy space
Nested dichotomies (NDs) reduce multiclass classification to a tree of binary classifiers.Reliable Scale-Free Bandits via Empirical Likelihood
We study stochastic bandits without knowledge of reward scales or tail parameters.