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 59
Signal-Noise Factorization Isolates Nuisance Variation into Removable Subspaces
Recent theoretical work identified fundamental properties of representation geometry that shape inference ability of deep neural networks.Learning to Price Electricity for Optimal Demand Response
There is considerable interest in using time-varying electricity prices to shape consumer demand response, and better align energy demand with renewable production.Scalable Multi-Task Inverse Reinforcement Learning
By learning transferable rewards, inverse reinforcement learning (IRL) enables counterfactual evaluation of agents under modified environments.Inference for stochastic differential equations driven by weighted sub-fractional Brownian motion using neural networks and the Euler approximation
We consider the estimation of drift, diffusion, and noise covariance from discrete observations of stochastic differential equations driven by Gaussian processes.Nearest-neighbour baselines for fingerprint prediction from MS/MS spectra under different assumptions
It has recently been shown that nearest-neighbour retrieval provides a strong baseline for molecular fingerprint prediction from MS/MS spectra, with several variants matching or outperforming…Counterfactual Predictions in Scientific Emulators Without Controlled Experiments
Many scientific questions require reasoning about what was never observed: What if the conditions, interventions, or history had been different?TRACE: A Reproducible Benchmark for Electricity Price Forecasting with Official Operational Text
Electricity price forecasting (EPF) supports scheduling, bidding, and risk management in electricity markets, yet existing benchmarks focus mainly on numerical inputs, leaving the forecasting value…Optimal Multi-Reward Reinforcement Learning
We study an unknown-transition finite-horizon Markov decision process (MDP) with a finite collection of known reward functions $\{r^1, r^2, \ldots, r^M\}$.Hierarchical Utility Calibration for Structured Multiclass Decisions
In multiclass probabilistic prediction, Utility Calibration (UC), which focuses auditing on specified utilities, has recently received attention as a way to guarantee downstream decisions while…Optimal detection of general moment changes: Simultaneous mean and covariance change detection and beyond
We study multiple change-point detection in multivariate time series whose distributions change in a piecewise constant manner.Second-Moment Stochastic Approximation Methods
Classical stochastic approximation methods rely on estimators of the first moment (mean) of a random regression function.Understanding Private Evolution as Learning-Augmented Clustering
Private Evolution (PE) is a differentially private algorithm for synthetic data generation.When Is Coarse Supervision Worth It? Cost-Aware Learning under Unknown Aggregation
Modern learning systems often acquire supervision at multiple resolutions, trading annotation cost against information content.Into the danger zone: stable extrapolation in high-dimensional function and operator learning
Out-of-distribution (OOD) generalization is a central challenge in scientific machine learning.Federated Clustering with Unknown Local and Global Cluster Cardinalities
Federated clustering methods that do not require the global number of clusters $K$ still assume that each client knows its local number $K_g$.Beyond Conditional Independence: Root Cause Analysis with Deep Causal Models
Root cause analysis (RCA) is a critical problem in many real-world scenarios.Scalable Diffusion SBI for Compositional Inference under Simulator Misspecification
Simulation-based inference is challenging when many heterogeneous observations must be composed, hierarchical latent structure must be preserved, and the simulator is misspecified relative to…Iterative Exact Discrete Guidance for Energy-Based Sampling
Sampling from unnormalized distributions over large discrete state spaces becomes difficult when a multimodal target is far from a tractable reference.Identifying ODEs from Unstructured Data with Causal Representation Learning
We study the problem of recovering the governing ODE of a dynamical system from unstructured, high-dimensional observations such as images.Interpretable intrinsic dimension estimation through componentwise calibration of distance and angle
DANCo (Dimensionality from Angle and Norm Concentration) jointly calibrates nearest-neighbor distance and angular statistics and consistently reaches state-of-the-art accuracy on clean…