Cover Image

Nonparametric Distribution Regression Re-calibration

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Jung, Ádám, Kelen, Domokos M., Benczúr, András A.
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Mesterséges Intelligencia Kutatások Magyarországon
by: Benczúr, András A., et al.
Published: (2025)

Unimodal Distributions for Ordinal Regression
by: Cardoso, Jaime S., et al.
Published: (2023)

Benign Overfitting for Regression with Trained Two-Layer ReLU Networks
by: Park, Junhyung, et al.
Published: (2024)

A Lower Bound for the Number of Linear Regions of Ternary ReLU Regression Neural Networks
by: Nakahara, Yuta, et al.
Published: (2025)

ViSymRe: Vision Multimodal Symbolic Regression
by: Li, Da, et al.
Published: (2024)

Transformers Learn Robust In-Context Regression under Distributional Uncertainty
by: Cao, Hoang T. H., et al.
Published: (2026)

Beyond the Mean: Distribution-Aware Loss Functions for Bimodal Regression
by: Mohammadi-Seif, Abolfazl, et al.
Published: (2026)

Covering Numbers for Deep ReLU Networks with Applications to Function Approximation and Nonparametric Regression
by: Ou, Weigutian, et al.
Published: (2024)

Whom to Trust? Elective Learning for Distributed Gaussian Process Regression
by: Yang, Zewen, et al.
Published: (2024)

Error Distribution Smoothing:Advancing Low-Dimensional Imbalanced Regression
by: Chen, Donghe, et al.
Published: (2025)

Do Attention Heads Compete or Cooperate during Counting?
by: Zsámboki, Pál, et al.
Published: (2025)

Nonparametric Identification of Latent Concepts
by: Zheng, Yujia, et al.
Published: (2025)

Verification of the Implicit World Model in a Generative Model via Adversarial Sequences
by: Balogh, András, et al.
Published: (2026)

Stochastic Re-weighted Gradient Descent via Distributionally Robust Optimization
by: Kumar, Ramnath, et al.
Published: (2023)

Isotonic Survival Regression: Calibrated Survival Distributions from Deep Cox Models
by: Jain, Anchit, et al.
Published: (2026)

Surprisal Driven $k$-NN for Robust and Interpretable Nonparametric Learning
by: Banerjee, Amartya, et al.
Published: (2023)

Adaptive Transformer Modelling of Density Function for Nonparametric Survival Analysis
by: Zhang, Xin, et al.
Published: (2024)

DistPred: A Distribution-Free Probabilistic Inference Method for Regression and Forecasting
by: Liang, Daojun, et al.
Published: (2024)

Measuring multi-calibration
by: Guy, Ido, et al.
Published: (2025)

Dist Loss: Enhancing Regression in Few-Shot Region through Distribution Distance Constraint
by: Nie, Guangkun, et al.
Published: (2024)

Extending confidence calibration to generalised measures of variation
by: Thompson, Andrew, et al.
Published: (2026)

Quantile Regression for Distributional Reward Models in RLHF
by: Dorka, Nicolai
Published: (2024)

Imbalance in Regression Datasets
by: Kowatsch, Daniel, et al.
Published: (2024)

Distributional Regression with Tabular Foundation Models: Evaluating Probabilistic Predictions via Proper Scoring Rules
by: Landsgesell, Jonas, et al.
Published: (2026)

A Slices Perspective for Incremental Nonparametric Inference in High Dimensional State Spaces
by: Shienman, Moshe, et al.
Published: (2024)

Thought calibration: Efficient and confident test-time scaling
by: Wu, Menghua, et al.
Published: (2025)

Distribution-Aware Reward: Reinforcement Learning over Predictive Distributions for LLM Regression
by: Park, Jungsoo, et al.
Published: (2026)

Federated Neural Nonparametric Point Processes
by: Chen, Hui, et al.
Published: (2024)

Performance Metric for Multiple Anomaly Score Distributions with Discrete Severity Levels
by: Yi, Wonjun, et al.
Published: (2024)

Markov Chain Decoders Overcome the Heavy-Tail Limitations of Lipschitz Generative Models
by: Ziani, Abdelhakim, et al.
Published: (2026)

Error-Driven Prompt Optimization for Arithmetic Reasoning
by: Pándy, Árpád, et al.
Published: (2025)

Margin-calibrated Classifier Guidance for Property-driven Synthesis Planning
by: Laabid, Najwa, et al.
Published: (2026)

Large language models as uncertainty-calibrated optimizers for experimental discovery
by: Ranković, Bojana, et al.
Published: (2025)

How Inverse Conditional Flows Can Serve as a Substitute for Distributional Regression
by: Kook, Lucas, et al.
Published: (2024)

Sufficient Invariant Learning for Distribution Shift
by: Kim, Taero, et al.
Published: (2022)

The Resurrection of the ReLU
by: Horuz, Coşku Can, et al.
Published: (2025)

SuReNav: Superpixel Graph-based Constraint Relaxation for Navigation in Over-constrained Environments
by: Koh, Keonyoung, et al.
Published: (2026)

Trust the uncertain teacher: distilling dark knowledge via calibrated uncertainty
by: Kim, Jeonghyun, et al.
Published: (2026)

Nonparametric Modern Hopfield Models
by: Hu, Jerry Yao-Chieh, et al.
Published: (2024)

A calibration test for evaluating set-based epistemic uncertainty representations
by: Jürgens, Mira, et al.
Published: (2025)