NPBoost: Neural Processes with Gradient-Boosted Fixed Effects

arXiv:2609.28122 · stat.ML, cs.LG · Submitted 2026-09-23 · Read on arXiv

stat.ML, cs.LG

Submitted: 2026-09-23

Updated: 2026-09-23

Code: https://github.com/ken-r/npboost

Project page: http://yanndubs.github.io/Neural-Process-Family

Terminology

• Meta-learning — 13× in this paper · explained in 2 episodes
• Core — 10× in this paper · explained in 2 episodes
• Calibration — 5× in this paper · explained in 7 episodes
• Adam — 4× in this paper · explained in 2 episodes
• Aggregation — 4× in this paper · explained in 1 episode
• Benchmark — 4× in this paper · explained in 2 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Foundation Model — 3× in this paper · explained in 3 episodes
• Generative AI — 3× in this paper · explained in 2 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Bayesian Inference — 2× in this paper · explained in 5 episodes
• Empirical Coverage — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Activation Function — 1× in this paper · explained in 1 episode
• Additive Decomposition — 1× in this paper · explained in 1 episode
• Alternation — 1× in this paper · explained in 1 episode
• Attentive Neural Process — 1× in this paper
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 3 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Longitudinal Data — 1× in this paper · explained in 2 episodes
• Mean Absolute Calibration Error — 1× in this paper
• Representation Learning — 1× in this paper · explained in 2 episodes
• Zero-Shot Generalization — 1× in this paper · explained in 5 episodes

Sources

• Robust Representation Learning through Explicit Environment Modeling
• Recurrent Neural Processes

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