mlsanityData to 7 Oct 2026

Calibrated Reliable Regression using Maximum Mean Discrepancy

arXiv 2006.10255 ↗First posted 18 Jun 2020cs.LG stat.MLcited by 36

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Abstract (arXiv)

Accurate quantification of uncertainty is crucial for real-world applications of machine learning. However, modern deep neural networks still produce unreliable predictive uncertainty, often yielding over-confident predictions. In this paper, we are concerned with getting well-calibrated predictions in regression tasks. We propose the calibrated regression method using the maximum mean discrepancy by minimizing the kernel embedding measure. Theoretically, the calibration error of our method asymptotically converges to zero when the sample size is large enough. Experiments on non-trivial real datasets show that our method can produce well-calibrated and sharp prediction intervals, which outperforms the related state-of-the-art methods.

Key facts. Cited by 36 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Jun 2020.

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Citing papers per year: 2021: 5; 2022: 7; 2023: 10; 2024: 2; 2025: 8; 2026: 4.

36 papers.

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  1. 2022Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysiscited by 104
  2. 2021Classifier Calibration: A survey on how to assess and improve predicted class probabilitiescited by 71
  3. 2021Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantificationcited by 57
  4. 2023How Reliable is Your Regression Model's Uncertainty Under Real-World Distribution Shifts?cited by 14
  5. 2022Calibration and generalizability of probabilistic models on low-data chemical datasets with DIONYSUScited by 10
  6. 2022Evaluating Aleatoric Uncertainty via Conditional Generative Modelscited by 8
  7. 2022Parametric and Multivariate Uncertainty Calibration for Regression and Object Detectioncited by 8
  8. 2023Client: Cross-variable Linear Integrated Enhanced Transformer for Multivariate Long-Term Time Series Forecastingcited by 8
  9. 2022Estimating Regression Predictive Distributions with Sample Networkscited by 4
  10. 2021f-Cal: Calibrated aleatoric uncertainty estimation from neural networks for robot perceptioncited by 3
  11. 2023Likelihood Annealing: Fast Calibrated Uncertainty for Regressioncited by 3
  12. 2023Distribution-Free Model-Agnostic Regression Calibration via Nonparametric Methodscited by 3
24 more
  1. 2023Uncertainty Calibration for Counterfactual Propensity Estimation in Recommendationcited by 2
  2. 2023Parity Calibrationcited by 2
  3. 2024Conformal Depression Predictioncited by 2
  4. 2025FlowECG: Using Flow Matching to Create a More Efficient ECG Signal Generatorcited by 2
  5. 2021Accurate and Reliable Forecasting using Stochastic Differential Equationscited by 1
  6. 2022Ensemble Multi-Quantiles: Adaptively Flexible Distribution Prediction for Uncertainty Quantificationcited by 1
  7. 2022Improving Uncertainty Quantification of Variance Networks by Tree-Structured Learningcited by 1
  8. 2023Measuring and Modeling Uncertainty Degree for Monocular Depth Estimationcited by 1
  9. 2023Deep sub-ensembles meets quantile regression: uncertainty-aware imputation for time seriescited by 1
  10. 2025Localization Meets Uncertainty: Uncertainty-Aware Multi-Modal Localizationcited by 1
  11. 2025Cooperative Variance Estimation and Bayesian Neural Networks for Disentangling Aleatoric and Epistemic Uncertaintiescited by 1
  12. 2025Deep Hidden Cognition Facilitates Reliable Chain-of-Thought Reasoningcited by 1
  13. 2025Evaluating the Quality of the Quantified Uncertainty for (Re)Calibration of Data-Driven Regression Modelscited by 1
  14. 2025Microstructure-based Variational Neural Networks for Robust Uncertainty Quantification in Materials Digital Twinscited by 1
  15. 2026Transfer Learning Through Conditional Quantile Matchingcited by 1
  16. 2026Pointwise Metrics Mislead: An Evaluation Protocol for Multimodal Inverse Problemscited by 1
  17. 2026Decision-Aligned Evaluation of Uncertainty Quantificationcited by 1
  18. 2021Diffusion Tensor Estimation with Uncertainty Calibrationcited by 0
  19. 2023A Hierarchical Bayesian Model for Deep Few-Shot Meta Learningcited by 0
  20. 2023Meta-learning to Calibrate Gaussian Processes with Deep Kernels for Regression Uncertainty Estimationcited by 0
  21. 2024Causally-Aware Unsupervised Feature Selection Learningcited by 0
  22. 2025Beyond Correlation: Causal Multi-View Unsupervised Feature Selection Learningcited by 0
  23. 2025ProTerrain: Probabilistic Physics-Informed Rough Terrain World Modelingcited by 0
  24. 2026Learning Context-conditioned Gaussian Overbounds for Convolution-Based Uncertainty Propagationcited by 0