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 79
Learning Choice Model Trees for Feature-Based Multi-Product Pricing: Exact Optimization and Field Evidence
Feature-based multi-product pricing uses customer characteristics to identify demand heterogeneity and tailor prices across products.Splitting the Difference: Interpretable Causal Forests for Treatment Effect Heterogeneity and Bias
In various fields, such as medicine and marketing, accurately predicting individual treatment effects holds significant promise.Covariate Selection for Doubly Robust Double/debiased Machine Learning Estimators for Causal Inference
High-dimensional data create challenges for causal effect estimation because identifying the covariates needed for correct model specification becomes increasingly difficult.Personalized Federated Learning through Global Knowledge Distillation and Local Head Adaptation
Statistical heterogeneity limits federated learning when a single global classifier cannot represent client-specific label distributions.Conformal Policy Learning with Distribution-Free Safety Guarantees
Policy learning aims to determine who should be treated based on individual characteristics.Hybrid Variational Quantum Circuits for Multivariate Regression and High-Dimensional Data Reconstruction
Variational quantum circuits (VQCs) are parameterized quantum circuits optimized classically.Coupled Calibration and Learning: Mitigating Teacher Bias in LLM Distillation without Target-Domain Reward Feedback
Large language model (LLM) distillation aims to transfer the capabilities of a powerful teacher to a smaller student.Fenchel-Young Duality Gaps: Certified Early Stopping for Regularized Inverse Problems
We study computable error bounds and certified early stopping for regularized inverse problems, where a data-fidelity term is traded against a regularizer.Efficient Robust Learning at the Information-Theoretic Limit
In an important recent work, Blanc (2026) gave an algorithm for robustly learning Boolean concept classes with respect to a fixed distribution that outputs a (randomized) classifier achieving the…Random tilts to find stationary points in stochastic convex optimization
We consider the problem of finding stationary points of stochastic convex functions and related variational inequalities.Approximating Measures on Function Spaces: Transport and Truncation
Measures on function spaces arise throughout Bayesian inverse problems and generative modeling, often with low-dimensional structure relative to a tractable reference measure.METALICA: METAdynamics and repLICA exchange for enhanced diffusion sampling
Many proteins function through transitions between conformational states, yet rare states are rarely sampled by diffusion models trained on an equilibrium ensemble, demanding better sampling methods.Sharp margin-based generalization bounds for realizable SVM
Let the exact homogeneous hard-margin support vector machine be trained on \(m\) independent observations from a Borel probability law on a real Hilbert space.Bracketing Uncertainty in Clustering Under the Manifold Hypothesis
The manifold hypothesis suggests a natural criterion for clustering: partition data according to the manifold component from which each point is drawn.TabPFN-3.5: Technical Report
We introduce TabPFN-3.5, our new flagship Tabular Foundation Model.Generalized DCCQ: From Binary Quotients to Multinomial Simplex Geometry and Critical-Strip Coordinates
We extend the discrete complex complement quotient (DCCQ) framework from binary Bernoulli counts to multinomial count compositions.Symmetry without a manifold: intrinsic dimension on orbits
The standard geometric derivation of neural scaling exponents takes the intrinsic dimension of a data manifold as its input.Shapley Value Estimation for Multi-Site Data with Blockwise-Missing Features
Shapley value (SV)-based methods are the prevailing framework for feature attribution in machine learning, yet existing population-level Shapley estimators generally assume that observations used to…Steady-State Convergence of Stochastic Approximation
For constant-stepsize stochastic approximation (SA), the iterates converge in distribution to a stationary law that depends on the stepsize $α.$ Steady-state convergence (SSC) concerns the limit of…Distributed Fast Fixed-Point Algorithms for Composite Monotone Inclusions over Networks
This paper aims to develop new and efficient distributed algorithms for solving a class of monotone inclusions, $0 \in \sum_{i=1}^n (G_ix + T_ix)$, over a connected network of $n$ agents, where the…