Heteroscedastic Regression

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1 paper in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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Residual Modeling Closes the Regression and Generative Policy Gap in Robot Learning
Oct 8, 2026Yuchen Zhou, Jiacheng You, Weikang Wan +3Robot Policy Learning Robot Imitation Learning
University of Pennsylvania · Tsinghua University · UC San Diego
Fast Non-Parametric Heteroscedastic Imitation Learning With Geometric Priors
Oct 6, 2026Maximilian Mühlbauer, Arne Sachtler, Markus Knauer +4Robotic Manipulation Data-Efficient Robot Learning
Department of Computer Engineering, Technical University of Munich, Friedrich-Ludwig-Bauer-Str. 3, Garching, Germany. · German Aerospace Center (DLR), Robotics and Mechatronics Center (RMC), Münchener Str. 20, 82234 Weßling, Germany. · School of Computer Science, University of Leeds, Leeds, UK.
Halo: Improving forecast accuracy through heteroscedastic estimation
Sep 11, 2026Adam CataldoTime Series Forecasting Heteroscedastic Regression
AI Quant Researcher
Algorithms for adaptive and heteroskedastic linear regression at the computational threshold
Aug 19, 2026Spencer Compton, Tselil SchrammMinimax Estimation Heteroscedastic Regression
Stanford University
Fisher8: Stabilizing Neural Heteroscedastic Regression via Output-Layer Fisher Geometry
Aug 11, 2026Sumedh Vemuganti, Nickvash KaniNatural Gradient Methods Heteroscedastic Regression
Electrical and Computer Engineering Department, University of Illinois at Urbana-Champaign
CUPA-T2*: Covariance-Aware Uncertainty Propagation and Alignment for T2* Mapping in Accelerated MRI
Aug 9, 2026Gideon N. L. Rouwendaal, Natascha Niessen, Hannah Eichhorn +3Uncertainty Quantification Heteroscedastic Regression
Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Germany · School of Computation, Information & Technology, Technical University of Munich, Germany · GE HealthCare, Munich, Germany +6
Multi-Fidelity SINDy: Sparse Discovery of Nonlinear Dynamical Systems with Fidelity-Weighted Measurements
Jun 14, 2026Filippo Zacchei, Ana Larrañaga, Attilio Frangi +2Sparse Identification of Nonlinear Dynamics Nonlinear System Identification
MOX – Department of Mathematics, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy · Department of Mechanical Engineering, University of Washington, Seattle, Washington 98195, United States · Department of Civil and Environmental Engineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
Physically Constrained Ensemble Gaussian Process Modelling for Expensive Quantum Systems with Heteroskedastic Noise
May 29, 2026Arpan Biswas, Sutirtha Paul, Joseph Agada +2Surrogate Modeling Uncertainty Quantification
University of Tennessee-Oak Ridge Innovation Institute, University of Tennessee, Knoxville, TN 37996, USA · Institute for Advanced Materials and Manufacturing, University of Tennessee, Knoxville, Tennessee 37996, USA · Department of Physics and Astronomy, University of Tennessee, Knoxville, TN 37996, USA. +2
A Tale of Two Variances: When Single-Seed Benchmarks Fail in Bayesian Deep Learning
Apr 25, 2026Qishi Zhan, Minxuan Hu, Liang He +2Bayesian Neural Networks Deep Ensembles
Department of Mathematical and Statistical Sciences Marquette University, USA · Cornell Ann S. Bowers College of Computing and Information Science Cornell University, USA · Physics Science and Engineering Tongji University, China +2
Conformalized Super Learner
Apr 24, 2026Zhanli Wu, Fabrizio Leisen, Miguel-Angel Luque-Fernandez +1Ensemble Learning Conformal Prediction
Department of Mathematics King’s College London London, UK · Department of Statistics and Operations Research University of Granada Granada, Spain · Department of Statistical Science University College London London, UK
Gradient Boosted Mixed Models: Flexible Estimation of Mean and Variance Components for Clustered Data
Oct 31, 2025Mitchell L. Prevett, Francis K. C. Hui, Zhi Yang Tho +2Gradient-Boosted Decision Trees Heteroscedastic Regression
Research School of Finance, Actuarial Studies and Statistics The Australian National University Canberra, ACT 2601, Australia

Oct 8, 2026, cs.ROJ/K move · Enter open · S save

Residual Modeling Closes the Regression and Generative Policy Gap in Robot Learning

Yuchen Zhou, Jiacheng You, Weikang Wan, Weijun Dong+2

University of Pennsylvania · Tsinghua University · UC San Diego

Robot Policy Learning Robot Imitation Learning Heteroscedastic Regression Heavy-Tailed Distributions

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Learning from demonstration has enabled impressive robot behaviors. A common choice for policy learning is to use diffusion or flow matching (Flow-Policies), which often outperforms direct action regression trained with mean squared error (MSE-Policies). This gap is commonly attributed to multimodal demonstrations. We revisit this gap from the perspective of statistical modeling: how action-prediction residuals shape policy optimization. Our analysis of real-world robot demonstration data reveals substantial state-dependent variation in residual scales and heavier-than-Gaussian tails. While both MSE-Policies and Flow-Policies exhibit heavy-tailed action residuals, their training gradients behave differently: MSE allocates more gradient magnitude to observations with large action residuals, which hurts optimization. Motivated by these findings, we introduce heteroscedastic Student-t action regression (HT-Policies), which learns input-dependent residual scales and reduces the influence of heavy tails. HT-Policies predict action chunks with a single feed-forward pass and can reuse pretrained flow-matching-based policy networks as the backbone. Across four simulation benchmarks and real-robot evaluations, HT-Policies achieves success rates competitive with generative policy baselines, both when trained from scratch and from pretrained vision-language-action and world-action models, despite being faster in training and inference. Together, these findings shed light on the practical advantages of generative objectives in robot learning from demonstrations and offer an efficient direct-regression alternative for a range of architectures and tasks. Project page: https://the-labone.github.io/regression-policy-project/

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