Conditional Distribution Estimation

Ask about this topic

Overview

The field estimates how an outcome varies with observed covariates, not just its average, so predictions can represent uncertainty, skew, and multiple plausible outcomes. Approaches include quantile regression and tree ensembles, mixture and Gaussian-process models, and neural methods such as normalizing flows, energy models, and conditional diffusion. Researchers also study how to estimate these distributions efficiently for high-dimensional, mixed-type, censored, or sequential data, and how to evaluate them with proper scores and calibration. A central tension is balancing flexible modeling of complex distributions with reliable uncertainty estimates, computational scalability, and finite-sample guarantees.

Latest papers

CardsList

78 papers23 papers in the last four weeks, up 130% on the four weeks before. 0.1% of all new papers.

  1. CERBERUS: A Three-Headed Decoder for Vertical Cloud Profiles

    Apr 9, 2026Emily K. deJong, Nipun Gunawardena, Kevin Smalley +2Remote SensingConditional Distribution Estimation

    Lawrence Livermore National Laboratory, Livermore, CA 94550 USA

  2. Accurate and Reliable Uncertainty Estimates for Deterministic Predictions Extensions to Under and Overpredictions

    Apr 9, 2026Rileigh Bandy, Enrico Camporeale, Andong Hu +2Distributional RegressionUncertainty Quantification

    Optimization and Uncertainty Quantification Department, Sandia National Laboratories, Albuquerque, NM · School of Physical and Chemical Sciences, Queen Mary University of London, London, UK and Space Weather Technology, Research and Education Center, University of Colorado, Boulder, CO · Space Weather Technology, Research and Education Center, University of Colorado, Boulder, CO +2

  3. Learning Markov Processes as Sum-of-Square Forms for Analytical Belief Propagation

    Apr 8, 2026Peter Amorese, Morteza LahijanianBelief PropagationMarkov Models

    University of Colorado Boulder

  4. Reconsidering Dependency Networks from an Information Geometry Perspective

    Apr 1, 2026Kazuya Takabatake, Shotaro AkahoGraph Structure LearningMarkov Chain Monte Carlo

    Independent Researcher, Izumisano, Osaka, Japan · The Institute of Statistical Mathematics, Tachikawa, Tokyo, 190-8565, Japan

  5. DiSGMM: A Method for Time-varying Microscopic Weight Completion on Road Networks

    Mar 31, 2026Yan Lin, Jilin Hu, Shengnan Guo +3Learning with Missing DataConditional Distribution Estimation

    Aalborg University · East China Normal University · Beijing Jiaotong University

  6. Benchmarking Tabular Foundation Models for Conditional Density Estimation in Regression

    Mar 27, 2026Rafael Izbicki, Pedro L. C. RodriguesConditional Distribution EstimationTabular Foundation Models

    Department of Statistics, Federal University of São Carlos (UFSCar), Brazil. · Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, France.

  7. A CDF-First Framework for Free-Form Density Estimation

    Mar 26, 2026Chenglong Song, Mazharul Islam, Lin Wang +2Conditional Distribution EstimationDensity Estimation

    Shandong Key Laboratory of Ubiquitous Intelligent Computing, University of Jinan · Shandong Provincial Key Laboratory of Green and Intelligent Building Materials, University of Jinan, Jinan, Shandong 250022, China · Quancheng Shandong Laboratory, Jinan 250100, China +1

  8. CRPS-Optimal Binning for Univariate Conformal Regression

    Mar 23, 2026Paolo ToccaceliConditional Distribution EstimationRegression Prediction Intervals

    Centre for Reliable Machine Learning, Royal Holloway, University of London, Egham, Surrey TW20 0EX, UK

  9. Closed-form conditional diffusion models for data assimilation

    Mar 22, 2026Brianna Binder, Agnimitra Dasgupta, Assad OberaiData AssimilationConditional Diffusion Models

    Department of Aerospace & Mechanical Engineering, University of Southern California, 3650 McClintock Ave, Los Angeles, 90089, California, USA. · Optimization & Uncertainty Quantification, Sandia National Laboratories, 1450 Innovation Pkwy SE, Albuquerque, 87123, New Mexico, USA.

  10. Scalable Learning of Multivariate Distributions via Coresets

    Mar 20, 2026Zeyu Ding, Katja Ickstadt, Nadja Klein +2Coreset SelectionConditional Distribution Estimation

  11. Contextual Preference Distribution Learning

    Mar 17, 2026Benjamin Hudson, Laurent Charlin, Emma FrejingerPreference LearningDecision-Making under Uncertainty

    Mila – Quebec Artificial Intelligence Institute, Montreal, Quebec, Canada · Département d’informatique et de recherche opérationnelle (DIRO), Université de Montréal, Montreal, Quebec, Canada · HEC Montréal, Montreal, Quebec, Canada

  12. GGMPs: Generalized Gaussian Mixture Processes

    Mar 11, 2026Vardaan Tekriwal, Mark D. Risser, Hengrui Luo +1Conditional Distribution EstimationGaussian Processes

    Applied Math & Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA · Department of Electrical Engineering & Computer Sciences, University of California, Berkeley, CA · Engineering Mathematics & Statistics Division, University of California, Berkeley, CA +3

  13. Marginals Before Conditionals

    Mar 10, 2026Mihir SahasrabudheNeural Network Training DynamicsPhase Transitions in Neural Networks

    School of Information Sciences, University of Illinois Urbana-Champaign

  14. Conditional Rank-Rank Regression via Deep Conditional Transformation Models

    Mar 7, 2026Xiaoyi Wang, Long Feng, Zhaojun WangConditional Distribution EstimationSemiparametric Inference

    Nankai University

  15. Discrete Diffusion with Sample-Efficient Estimators for Conditionals

    Feb 23, 2026Karthik Elamvazhuthi, Abhijith Jayakumar, Andrey Y. LokhovDiffusion ModelsConditional Distribution Estimation

  16. Representation Learning for Equivariant Inference with Guarantees

    May 26, 2025Daniel Ordoñez-Apraez, Vladimir Kostić, Alek Fröhlich +3Disentangled Representation LearningRepresentation Learning

    CSML, Italian Institute of Technology, Genova, Italy · Universit`a di Genova, Genova, Italy · University of Novi Sad, Novi Sad, Serbia +2

  17. Discrete distributions are learnable from metastable samples

    Oct 17, 2024Abhijith Jayakumar, Andrey Y. Lokhov, Sidhant Misra +1Conditional Distribution EstimationStatistical Learning Theory

    Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA

  18. Deep conditional distribution learning via conditional Föllmer flow

    Feb 2, 2024Jinyuan Chang, Zhao Ding, Yuling Jiao +2Conditional Distribution EstimationFlow-Based Generative Modeling

    Joint Laboratory of Data Science and Business Intelligence, Southwestern University of Finance and Economics, Chengdu, Sichuan 611130, China · State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China · School of Mathematics and Statistics, Wuhan University, Wuhan, Hubei 430072, China +1