Generalized Neural Distributional Regression

arXiv:2607.14122 · stat.ML, cs.LG, math.ST, stat.AP, stat.CO, stat.ME · Submitted 2026-06-16 · Read on arXiv

Natan Hilario da Silva, Vicente Garibay Cancho, Adriano Kamimura Suzuki

stat.ML, cs.LG, math.ST, stat.AP, stat.CO, stat.ME

Submitted: 2026-06-16

Comments: 51 pages, 10 figures, 5 tables

Code: https://github.com/nhilariosilva/thetaflow

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Calibration — 10× in this paper · explained in 9 episodes
• Uncertainty Quantification — 9× in this paper · explained in 9 episodes
• Feature Extraction — 8× in this paper · explained in 3 episodes
• Generalized Neural Distributional Regression — 7× in this paper
• Fisher Information — 6× in this paper · explained in 4 episodes
• Overfitting — 6× in this paper · explained in 2 episodes
• Regularization — 5× in this paper · explained in 3 episodes
• Asymptotic Normality — 4× in this paper · explained in 2 episodes
• Fisher Information Matrix — 4× in this paper
• Interpretability — 4× in this paper · explained in 15 episodes
• Adam — 3× in this paper · explained in 3 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Hyperparameters — 3× in this paper · explained in 2 episodes
• Loss Function — 3× in this paper · explained in 2 episodes
• Causal Inference — 2× in this paper · explained in 5 episodes
• Dimensionality Reduction — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Generalization — 2× in this paper · explained in 14 episodes
• Latent Representation — 2× in this paper · explained in 2 episodes
• Latent Representations — 2× in this paper · explained in 3 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Parameter Identifiability — 2× in this paper · explained in 2 episodes
• Parameterization — 2× in this paper · explained in 2 episodes
• Time Series Forecasting — 2× in this paper · explained in 4 episodes
• Vector Generalized Linear Model — 2× in this paper
• Accuracy — 1× in this paper · explained in 1 episode
• Bayesian Inference — 1× in this paper · explained in 6 episodes
• Composition — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Convolutional Neural Networks (CNNs) — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Curse of Dimensionality — 1× in this paper · explained in 6 episodes
• Diagnostic Tools — 1× in this paper · explained in 2 episodes
• Empirical Coverage — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Generator — 1× in this paper · explained in 2 episodes
• Heteroscedasticity — 1× in this paper · explained in 2 episodes
• Hybrid Architecture — 1× in this paper · explained in 2 episodes
• Inflation — 1× in this paper · explained in 2 episodes
• Linear Complexity — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 3 episodes
• Score Function — 1× in this paper · explained in 4 episodes
• Transfer Learning — 1× in this paper · explained in 16 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Transformer Architecture — 1× in this paper · explained in 4 episodes
• Uncertainty Quantification (UQ) — 1× in this paper · explained in 3 episodes
• Vector Representations — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes
• Weight Decay — 1× in this paper · explained in 2 episodes

Sources

• Adam: A Method for Stochastic Optimization
• Decoupled Weight Decay Regularization
• Neural Mixture Distributional Regression

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