DADIR: Density-Aware Data-level Imbalanced Regression Framework

arXiv:2607.17178 · cs.LG, cs.AI · Submitted 2026-07-19 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-07-19

Updated: 2026-07-19

Terminology

• Clustering — 25× in this paper · explained in 2 episodes
• Latent Space — 22× in this paper · explained in 7 episodes
• Adaptive Partitioning — 18× in this paper · explained in 1 episode
• Density-Aware Adaptive Partitioning — 16× in this paper
• Latent Representation — 16× in this paper · explained in 2 episodes
• Ablation — 12× in this paper · explained in 1 episode
• Benchmark — 11× in this paper · explained in 2 episodes
• Representation Learning — 11× in this paper · explained in 2 episodes
• Accuracy — 9× in this paper · explained in 1 episode
• Latent Representations — 9× in this paper · explained in 3 episodes
• Data Augmentation — 8× in this paper · explained in 7 episodes
• Generative Model — 8× in this paper · explained in 2 episodes
• Generative Models — 8× in this paper · explained in 2 episodes
• Kernel Density Estimation — 8× in this paper
• Robustness — 8× in this paper · explained in 5 episodes
• Consistency — 6× in this paper · explained in 3 episodes
• Generalization — 6× in this paper · explained in 13 episodes
• Predictive Modeling — 4× in this paper · explained in 2 episodes
• Regularization — 4× in this paper · explained in 3 episodes
• Support Vector Regression — 4× in this paper
• Ablation study — 3× in this paper · explained in 1 episode
• Computational overhead — 3× in this paper · explained in 2 episodes
• Core — 3× in this paper · explained in 2 episodes
• Feature Distribution Smoothing — 3× in this paper
• Label Distribution Smoothing — 3× in this paper
• Linear Regression — 3× in this paper
• Adam — 2× in this paper · explained in 2 episodes
• Interpretability — 2× in this paper · explained in 15 episodes
• K-Nearest Neighbors — 2× in this paper
• Loss Function — 2× in this paper · explained in 2 episodes
• Model-Agnostic — 2× in this paper · explained in 2 episodes
• Pruning — 2× in this paper · explained in 4 episodes
• Sparsity — 2× in this paper · explained in 4 episodes
• Support Vector Regression (SVR) — 2× in this paper · explained in 2 episodes
• Unified Framework — 2× in this paper · explained in 5 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Abstraction — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Concept Drift — 1× in this paper · explained in 4 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Ensemble Learning — 1× in this paper · explained in 2 episodes
• Error Distribution Smoothing — 1× in this paper
• Fidelity — 1× in this paper · explained in 2 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Generative Adversarial Networks (GANs) — 1× in this paper · explained in 3 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Implicit Reweighting with Data Augmentation — 1× in this paper
• Information Leakage — 1× in this paper · explained in 2 episodes
• KL Regularization — 1× in this paper · explained in 2 episodes
• Mean Absolute Error — 1× in this paper
• Mode Collapse — 1× in this paper · explained in 2 episodes
• Multi-Layer Perceptron — 1× in this paper
• Redundancy — 1× in this paper · explained in 2 episodes
• Root Mean Squared Error — 1× in this paper
• Synthetic Data Generation — 1× in this paper · explained in 5 episodes
• Uncertainty Quantification — 1× in this paper · explained in 8 episodes

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