Niels Bracher, Lars K\"uhmichel, Desi R. Ivanova, Xavier Intes, Paul-Christian B\"urkner, Stefan T. Radev JADAI: Jointly Amortizing Adaptive Design and Bayesian Inference https://arxiv.org/abs/2512.22999

Krishna Harsha Kovelakuntla Huthasana, Alireza Olama, Andreas Lundell Federated Learning With L0 Constraint Via Probabilistic Gates For Sparsity https://arxiv.org/abs/2512.23071

Bruno Mlodozeniec, David Krueger, Richard E. Turner Probabilistic Modelling is Sufficient for Causal Inference https://arxiv.org/abs/2512.23408

Agostino Capponi, Chengpiao Huang, J. Antonio Sidaoui, Kaizheng Wang, Jiacheng Zou The Nonstationarity-Complexity Tradeoff in Return Prediction https://arxiv.org/abs/2512.23596

Tiffany Ding, Isaac Gibbs, Ryan J. Tibshirani Calibrated Multi-Level Quantile Forecasting https://arxiv.org/abs/2512.23671

Lars van der Laan, Nathan Kallus Bellman Calibration for V-Learning in Offline Reinforcement Learning https://arxiv.org/abs/2512.23694

Bing Cheng, Howell Tong An approach to Fisher-Rao metric for infinite dimensional non-parametric information geometry https://arxiv.org/abs/2512.21451

Takuro Kutsuna Residual Prior Diffusion: A Probabilistic Framework Integrating Coarse Latent Priors with Diffusion Models https://arxiv.org/abs/2512.21593

Peter Potaptchik, Cheuk-Kit Lee, Michael S. Albergo Tilt Matching for Scalable Sampling and Fine-Tuning https://arxiv.org/abs/2512.21829

Stefan Volz, Martin Storath, Andreas Weinmann Fast and Exact Least Absolute Deviations Line Fitting via Piecewise Affine Lower-Bounding https://arxiv.org/abs/2512.20682

Jonas Arruda, Niels Bracher, Ullrich K\"othe, Jan Hasenauer, Stefan T. Radev Diffusion Models in Simulation-Based Inference: A Tutorial Review https://arxiv.org/abs/2512.20685

Rommel Cortez, Bala Krishnamoorthy Weighted MCC: A Robust Measure of Multiclass Classifier Performance for Observations with Individual Weights https://arxiv.org/abs/2512.20811

Edwin Fong, Lancelot F. James, Juho Lee Learning from Neighbors with PHIBP: Predicting Infectious Disease Dynamics in Data-Sparse Environments https://arxiv.org/abs/2512.21005

Li Cunzhi, Louis Kang, Hideaki Shimazaki Enhancing diffusion models with Gaussianization preprocessing https://arxiv.org/abs/2512.21020

Georgios Filippou, Boi Mai Quach, Diana Lenghel, Arthur White, Ashish Kumar Jha Causal-driven attribution (CDA): Estimating channel influence without user-level data https://arxiv.org/abs/2512.21211

Joonsung Kang Robust Causal Directionality Inference in Quantum Inference under MNAR Observation and High-Dimensional Noise https://arxiv.org/abs/2512.19746

Matthew Drnevich, Stephen Jiggins, Kyle Cranmer Quasiprobabilistic Density Ratio Estimation with a Reverse Engineered Classification Loss Function https://arxiv.org/abs/2512.19913

Zhihan Huang, Ziang Niu Semiparametric KSD test: unifying score and distance-based approaches for goodness-of-fit testing https://arxiv.org/abs/2512.20007

Anna R. Flowers, Christopher T. Franck, Robert B. Gramacy, Justin A. Krometis Gaussian Process Assisted Meta-learning for Image Classification and Object Detection Models https://arxiv.org/abs/2512.20021

Hedibert Lopes, Nick Polson, Vadim Sokolov Generative Bayesian Hyperparameter Tuning https://arxiv.org/abs/2512.20051

Mebin Jose, Jisha Francis, Sudheesh Kumar Kattumannil KAN-AFT: An Interpretable Nonlinear Survival Model Integrating Kolmogorov-Arnold Networks with Accelerated Failure Time Analysis https://arxiv.org/abs/2512.20305

Samya Praharaj, Koulik Khamaru Avoiding the Price of Adaptivity: Inference in Linear Contextual Bandits via Stability https://arxiv.org/abs/2512.20368

Yingzhen Yang Shallow Neural Networks Learn Low-Degree Spherical Polynomials with Learnable Channel Attention https://arxiv.org/abs/2512.20562

Holden Lee, Matheau Santana-Gijzen Sampling from multimodal distributions with warm starts: Non-asymptotic bounds for the Reweighted Annealed Leap-Point Sampler https://arxiv.org/abs/2512.17977

Ashley Zhang Causal Inference as Distribution Adaptation: Optimizing ATE Risk under Propensity Uncertainty https://arxiv.org/abs/2512.18083

Feng Yu, MD Saifur Rahman Mazumder, Ying Su, Oscar Contreras Velasco Unsupervised Feature Selection via Robust Autoencoder and Adaptive Graph Learning https://arxiv.org/abs/2512.18720

Shuntuo Xu, Zhou Yu, Jian Huang On Conditional Stochastic Interpolation for Generative Nonlinear Sufficient Dimension Reduction https://arxiv.org/abs/2512.18971

Xin Huang, Jia Li, Jun Yu Cluster-Based Generalized Additive Models Informed by Random Fourier Features https://arxiv.org/abs/2512.19373

Benjamin S. H. Lyo, Eero P. Simoncelli, Cristina Savin Disentangled representations via score-based variational autoencoders https://arxiv.org/abs/2512.17127

Jikai Jin, Vasilis Syrgkanis Sharp Structure-Agnostic Lower Bounds for General Functional Estimation https://arxiv.org/abs/2512.17341

Fatima-Zahrae Akhyar, Wei Zhang, Gabriel Stoltz, Christof Sch\"utte Generative modeling of conditional probability distributions on the level-sets of collective variables https://arxiv.org/abs/2512.17374

Xiaosi Gu, Ayaka Sakata, Tomoyuki Obuchi Perfect reconstruction of sparse signals using nonconvexity control and one-step RSB message passing https://arxiv.org/abs/2512.17426

Even He Fast and Robust: Computationally Efficient Covariance Estimation for Sub-Weibull Vectors https://arxiv.org/abs/2512.17632

Madhav R. Muthyala, Farshud Sorourifar, Tianhong Tan, You Peng, Joel A. Paulson Generative Multi-Objective Bayesian Optimization with Scalable Batch Evaluations for Sample-Efficient De Novo Molecular Design https://arxiv.org/abs/2512.17659

Pegah Golchian, Marvin N. Wright Imputation Uncertainty in Interpretable Machine Learning Methods https://arxiv.org/abs/2512.17689

Sophia Seulkee Kang, Fran\c{c}ois-Xavier Briol, Toni Karvonen, Zonghao Chen BayesSum: Bayesian Quadrature in Discrete Spaces https://arxiv.org/abs/2512.16105

Noriaki Sato, Marco Scutari, Shuichi Kawano, Rui Yamaguchi, Seiya Imoto DAG Learning from Zero-Inflated Count Data Using Continuous Optimization https://arxiv.org/abs/2512.16233

Seyda Betul Aydin, Holger Brandt Advantages and limitations in the use of transfer learning for individual treatment effects in causal machine learning https://arxiv.org/abs/2512.16489

Louis Grenioux, Leonardo Galliano, Ludovic Berthier, Giulio Biroli, Marylou Gabri\'e Riemannian Stochastic Interpolants for Amorphous Particle Systems https://arxiv.org/abs/2512.16607

Soon Hoe Lim On The Hidden Biases of Flow Matching Samplers https://arxiv.org/abs/2512.16768

John D. Foley, Justin T. Lee Online Partitioned Local Depth for semi-supervised applications https://arxiv.org/abs/2512.15436

Carlos Couto, Jos\'e Mour\~ao, M\'ario A. T. Figueiredo, Pedro Ribeiro A Teacher-Student Perspective on the Dynamics of Learning Near the Optimal Point https://arxiv.org/abs/2512.15606

Victor L\'eger, Florent Chatelain High-Dimensional Partial Least Squares: Spectral Analysis and Fundamental Limitations https://arxiv.org/abs/2512.15684

Albert Dorador One Permutation Is All You Need: Fast, Reliable Variable Importance and Model Stress-Testing https://arxiv.org/abs/2512.13892

Aaron Wei, Milad Jalali, Danica J. Sutherland Maximum Mean Discrepancy with Unequal Sample Sizes via Generalized U-Statistics https://arxiv.org/abs/2512.13997

Zheng He, Roman Pogodin, Yazhe Li, Namrata Deka, Arthur Gretton, Danica J. Sutherland On the Hardness of Conditional Independence Testing In Practice https://arxiv.org/abs/2512.14000

Jiarong Fan, Juhyun Park. Thi Phuong Thuy Vo, Nicolas Brunel Weighted Conformal Prediction Provides Adaptive and Valid Mask-Conditional Coverage for General Missing Data Mechanisms https://arxiv.org/abs/2512.14221

\v{S}imon Kucharsk\'y, Aayush Mishra, Daniel Habermann, Stefan T. Radev, Paul-Christian B\"urkner Improving the Accuracy of Amortized Model Comparison with Self-Consistency https://arxiv.org/abs/2512.14308

Pablo Garc\'ia-Santaclara, Bruno Fern\'andez-Castro, Rebeca P. D\'iaz-Redondo, Carlos Calvo-Moa, Henar Mari\~no-Bodel\'on Continual Learning at the Edge: An Agnostic IIoT Architecture https://arxiv.org/abs/2512.14311

Hangjun Cho, Fabio V. G. Amaral, Andrei A. Klishin, Cassio M. Oishi, Steven L. Brunton From STLS to Projection-based Dictionary Selection in Sparse Regression for System Identification https://arxiv.org/abs/2512.14404