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
Satisficing as competitive Bayesian weighting: a minimal model of goal-directed choice under an unattained target
Animals and humans often do not maximize; they act relative to goals (aspiration levels) that are frequently not yet met.Neural Realized Measures: Forecast-Supervised Intraday Aggregation for Volatility Forecasting
High-frequency volatility forecasting typically separates measurement from prediction: intraday observations are first compressed into a daily realized measure using a fixed aggregation rule, and…Last-Iterate Convergence of Stochastic Gradient Methods for Unbounded Problems
Last-iterate convergence guarantees for stochastic gradient methods have attracted significant attention as practical implementations typically return the last iterate instead of an average of the…Changepoint localization via nested exchangeability tests: from point estimates to confidence intervals
We study distribution-free uncertainty quantification for a changepoint estimate in an offline setting.Identification and Bounding of Joint Expectation over Potential Outcomes
The joint expectation of functions of potential outcomes $(Y_1,Y_0)$ is a fundamental quantity in causal inference, particularly when investigating the variation or heterogeneity of causal effects.Optimal Learning in Games with Arbitrary Delays: Best-of-Both-Worlds for Self-Play and Adversarial Opponents
We study learning in zero-sum games with delayed feedback.Network Effect Decomposition for Causal Effect Estimation under Network Interference
Estimating causal effects from observational network data requires accounting for both individual treatment and neighborhood exposure.Joint Modeling of Conditional Mean and Variability in Multilevel Functional Data: Application to Postprandial Glucose Trajectories in the AEGIS Study
Functional data analysis provides a framework for studying time-varying associations between covariates and high-frequency biological signals.Doubly-robust causal inference for multi-valued treatment regimes with recurrent neural networks
We propose a doubly-robust method to estimate the causal effects of multi-valued treatment regimes on patient outcomes.Identifying Neural Dynamics Using Nonlinear Interventional State Space Models
State-space models (SSMs) recover low-dimensional latent dynamics from high-dimensional observations, but latents are generally not identifiable from observational data alone.Patch Attention Wisely: Understanding Patch Length in Attention-Based Forecasting
Attention-based time series forecasters commonly group consecutive observations into patches.BUMP: Brief Unexpected Mid-Interpolation Peak
We identify an intriguing phenomenon when linearly interpolating between permutation-aligned neural networks: test accuracy briefly improves near the interpolation endpoints before dropping toward…Series-wise Cross-validation for Global Time Series Forecasting
We consider the problem of cross-validation (CV) in time series forecasting, with a focus on global forecasting models based on deep learning.Unifying Central Flow, Rod Flow, and the Edge Coupling
At the edge of stability, the iterates of gradient descent oscillate along the sharp directions of the Hessian.Conformal Prediction for Time-Dependent PDEs
Uncertainty quantification is crucial in scientific machine learning, where models inform safety-critical tasks such as flood forecasting, financial risk management, and thermal control in machines.Spectral and Subspace Dynamics of LoRA in Supervised Fine-Tuning and Reinforcement Learning
Low-rank adaptation (LoRA) is commonly used in both supervised fine-tuning (SFT) and reinforcement learning (RL), often under similar optimizer settings.Discovering like a scientist: Literature-informed dimensionless learning for physical closure of data-driven equations
Data-driven methods can recover compact equations, yet the physical dependence of their coefficients across operating conditions often remains unresolved.Towards the Query Complexity of Sampling under Strong Log-Concavity and Log-Sobolev Inequalities
We study the query complexity of sampling from smooth distributions on $\mathbb R^d$ using first-order oracles.A low-rank decoder bottleneck bounds reliability in foundation-model perturbation prediction
Foundation-model predictors of perturbation response are evaluated primarily by aggregate accuracy.Prior-data fitted networks for variable selection
Prior-data fitted networks (PFNs) have become state-of-the-art for tabular prediction but remain underexplored in other areas of statistics.