Quantile Regression

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7 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

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Revisiting On-policy Adversarial Black-Box Distillation: Calibrating Groupwise Reward Geometry for Effective Advantage Construction
Sep 30, 2026Xiao Cui, Mo Zhu, Yulei Qin +3Dataset Distillation On-Policy
University of Science and Technology of China · Zhejiang University · Independent Researcher
Quantile Head for Vision-Language-Action Models
Sep 28, 2026Xuan Wang, Yinan Wu, Haoran Duan +1Photometric Supervision Quantile Regression
Department of Automation Tsinghua University
Conformalized Quantile Regression and Minimax Limits of Fixed-Score Calibration under Known Covariate Shift
Sep 21, 2026Rustam Isaev, Anton Conrad, Denis Belomestny +2Quantile Regression Covariate Shift
Faculty of Computer Science, HSE University, Moscow, Russia · Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Moscow, Russia · Laboratoire de Recherche d’EPITA, Le Kremlin-Bicêtre, France +2
Multivariate quantile regression via Kolmogorov-Arnold Networks
Sep 20, 2026Andrew Polar, Michael PoluektovKolmogorov-Arnold Networks Quantile Regression
Independent software consultant, Duluth, GA, USA · School of Computing and Mathematical Sciences, University of Greenwich, Park Row, London SE10 9LS, UK
Causal Discovery via Transformed Low-Rank Quantile Surfaces
Sep 15, 2026Ryo Kamimura, Thong PhamCausal Modeling Quantile Regression
1Shiga University · 3RIKEN AIP · 2The University of Osaka
Accountable and uncertainty-aware evaluation of sensor-based AI under distribution shift: devices, subjects, and nearly three years underground
Sep 8, 2026Benny Platte, Rico Thomanek, Christian Roschke +1Artificial Intelligence Evaluation Evaluation Protocols
Mittweida University of Applied Sciences · Faculty of Applied Computer Sciences and Biosciences, Mittweida University of Applied Sciences, Mittweida, Germany
Quantile-Led Feature Extraction for Multi-Horizon Predictive Maintenance in Industrial Manufacturing Systems
Sep 7, 2026David J Poland, Daniele Ravi, Na HelianPredictive Maintenance Feature Extraction
Department of Computer Science, University of Hertfordshire, College Lane, Hatfield, AL10 9AB, United Kingdom
A location-invariant estimator of extremal quantile treatment effects for heavy-tailed distributions
Sep 3, 2026Xin Yu, Shuwei Huang, Jicheng Liu +4Quantile Regression Heterogeneous Treatment Effects
Huazhong University of Science and Technology · Tencent
Crossing-Free Probabilistic K-Line Forecasts Without Retraining
Jul 29, 2026Runyao Yu, Yuchen Tao, Yujie Chen +2Probabilistic Forecasting Forecast
London Business School London, United Kingdom · RWTH Aachen University Aachen, Germany · The Chinese University of Hong Kong Shenzhen, China +1
Bet on Features: Anytime-Valid and Feature-Aware Auditing of Conditional Quantile Forecasters
Jul 13, 2026Ivane Antonov, Sohom Mukherjee, Richard Pibernik +1Quantile Regression Data-Driven Forecasting
Julius-Maximilians-Universität Würzburg · INSEAD
On Pairwise Quantile Regression -- Statistical Guarantees and Applications
Jul 5, 2026Romain Thérézien, Stephan Clémençon, Fantin Girard +1Multi-Quantile Regression Quantile Regression
1LTCI, Télécom Paris, Institut Polytechnique de Paris · 2Idémia · 3Télécom Sud-Paris, Institut Polytechnique de Paris
Sequential sparse Gaussian process quantile regression
Jun 30, 2026Hugo Nicolas, Olivier Le MaîtreGaussian Process Quantile Regression
PLATON, CMAP · Inria, Center for Applied Mathematics, ´Ecole polytechnique, Institut Polytechnique de Paris, Palaiseau, France · CNRS, Center for Applied Mathematics, ´Ecole polytechnique, Institut Polytechnique de Paris, Palaiseau, France
Quantile of Means: A Bonus-Free Ensemble Method for Minimax Optimal Reinforcement Learning
Jun 18, 2026Asaf Cassel, Aviv RosenbergStochastic Exploration Quantile Regression
Google Research
On the QUEST for Uncertainty Quantification via Highest Density Regions
Jun 17, 2026Sam Goring, Tom Kuipers, Nicola Paoletti +1Uncertainty Quantification Uncertainty
King’s College London · Northeastern University London
Out-of-Distribution generalization of quantile regression with heavy tailed inputs: an SVM approach
May 29, 2026Baptiste Leroux, Clément Dombry, Anne SabourinMulti-Quantile Regression Quantile Regression
Université Paris Cité, CNRS, MAP5, F-75006 Paris, France · Université Marie et Louis Pasteur, CNRS, LmB (UMR 6623), F-25000 Besançon, France · Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, F-75006, Paris, France
Beyond MSE: Improving Precipitation Nowcasting with Multi-Quantile Regression
May 28, 2026Gijs van Nieuwkoop, Siamak MehrkanoonPrecipitation Nowcasting Multi-Quantile Regression
Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands
Stabilizing distribution-free probabilistic forecasts
May 27, 2026Jente Van Belle, Honglin Wen, Wouter Verbeke +1Probabilistic Forecasting Short-Horizon Forecasting
Faculty of Economics and Business, KU Leuven, Belgium · Department of Electrical Engineering, Shanghai Jiao Tong University, China · Dyson School of Design Engineering, Imperial College London, United Kingdom +3
Distribution-Aware Conformal Prediction: A Framework for generating efficient prediction intervals for time series
May 26, 2026Daniel Schweizer, Peter Kuhn, Jayant Sharma +3Online Conformal Prediction Predictive Uncertainty
Fraunhofer Institute for Highspeed Dynamics, Ernst-Mach-Institut, EMI Freiburg, Germany
A Martingale Kernel Independence Test
May 21, 2026Felix Laumann, Zhaolu Liu, Mauricio BarahonaTwo-Sample Testing Conformal Test Martingales
Imperial College London
On Stability and Decomposition of Sample Quantiles under Heavy-Tailed Distributions
May 18, 2026Choudur LakshminarayanQuantile Regression Long-Tailed Distribution
School of Business, Stevens Institute of Technology
Skew-adaptive conformal prediction
May 15, 2026Paulo C. Marques F., Helton GraziadeiOnline Conformal Prediction Skewness
Insper Institute of Education and Research S˜ao Paulo, SP, 04546-042, Brazil · Department of Statistics Federal University of S˜ao Carlos S˜ao Carlos, SP, 13565-905, Brazil
Distributional Process Reward Models: Calibrated Prediction of Future Rewards via Conditional Optimal Transport
May 7, 2026Rachel Ma, Dylan Hadfield-Menell, Kristjan GreenewaldProcess Reward Model Quantile Regression
MIT CSAIL · MIT-IBM Watson AI Lab · IBM Research
Super-Level-Set Regression: Conditional Quantiles via Volume Minimization
May 7, 2026Sacha Braun, Michael I. Jordan, Francis BachQuantile Regression Conditional Distribution
Sierra team, Inria Paris, France · Departments of EECS and Statistics, UC Berkeley, USA
Text-to-Distribution Prediction with Quantile Tokens and Neighbor Context
Apr 22, 2026Yilun Zhu, Yuan Zhuang, Nikhita Vedula +6Multi-Quantile Regression Quantile Regression
Amazon.com, Inc. · Stanford University
High-Dimensional Differentially Private Quantile Regression: Distributed Estimation and Statistical Inference
Aug 7, 2025Ziliang Shen, Caixing Wang, Shaoli Wang +1Multi-Quantile Regression Quantile Regression
School of Statistics and Data Science, Shanghai University of Finance and Economics · School of Statistics and Data Science, Southeast University · School of Statistics and Data Science, Shanghai University of International Business and Economics
CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk
Jul 10, 2025Ilia Azizi, Juraj Bodik, Jakob Heiss +1Uncertainty Quantification Confidence Intervals
Department of Operations, HEC, University of Lausanne, Switzerland · BegooAI, Switzerland · Department of Statistics, University of California, Berkeley, USA +1
Online simultaneous inference for quantiles via smoothed stochastic gradient descent
May 19, 2025Likai Chen, Georg Keilbar, Wei Biao WuQuantile Regression Exponential Family
Department of Mathematics and Statistics · Washington University in St.Louis · St.Louis, MO, USA +6
Conformal Predictive Programming for Chance Constrained Optimization
Feb 12, 2024Yiqi Zhao, Xinyi Yu, Matteo Sesia +2Online Conformal Prediction Quantile Regression
Thomas Lord Department of Computer Science, University of Southern California · Department of Data Sciences and Operations, University of Southern California · Automatic Control Laboratory, ETH Zürich

Sep 30, 2026, cs.LGJ/K move · Enter open · S save

Revisiting On-policy Adversarial Black-Box Distillation: Calibrating Groupwise Reward Geometry for Effective Advantage Construction

Xiao Cui, Mo Zhu, Yulei Qin, Yuze Wu+2

University of Science and Technology of China · Zhejiang University · Independent Researcher

Dataset Distillation On-Policy Critic Black Box Quantile Regression

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Black-box distillation is a practical route for transferring capabilities from API-accessible large language models that expose only text outputs into smaller student models. Recent on-policy adversarial methods such as GAD improve over SeqKD by forming an adversarial loop between a critic and a student, where the critic provides rewards for GRPO-based student policy optimization over the student's sampled responses. However, GRPO computes advantages from the within-group relative rewards of student samples for the same prompt, whereas the critic is trained primarily to distinguish teacher responses from student responses. This objective mismatch can produce reward groups with collapsed scale or fragile margins, leading to brittle grouped optimization signals. We propose Groupwise Reward Geometry Conditioning (GRGC), a two-stage framework that improves advantage construction by shaping student-side reward groups during both critic training and policy optimization. To improve critic-side conditioning, Gaussian groupwise Optimal Transport calibration regularizes the critic during training to produce reward groups with non-collapsed spread and smooth rank-wise gaps by matching sorted prompt-wise rewards to group-centered Gaussian quantiles. Building on this conditioned reward geometry, policy-side group power modulation reshapes the prompt-wise reward groups before they are converted into advantages, preserving the critic-induced ordering while increasing optimization-relevant margin separability. Extensive experiments across diverse teachers, student model families and scales, and training datasets demonstrate the effectiveness of GRGC on both in-distribution and out-of-distribution evaluations, while introducing negligible overhead over GAD. The code is available at https://github.com/2018cx/GRGC.

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