Spectrally Deconfounded Gradient Boosting

arXiv:2607.09371 · stat.ML, cs.LG · Submitted 2026-07-10 · Read on arXiv

Andrea Nava, Peter Bühlmann, Fabio Sigrist

stat.ML, cs.LG

Submitted: 2026-07-10

Code: https://github.com/fabsig/GPBoost

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

Terminology

• Regularization — 21× in this paper · explained in 3 episodes
• Robustness — 18× in this paper · explained in 6 episodes
• Absorb — 10× in this paper · explained in 1 episode
• Lasso — 7× in this paper · explained in 2 episodes
• Verification — 5× in this paper · explained in 2 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Hyperparameters — 3× in this paper · explained in 2 episodes
• Adam — 2× in this paper · explained in 3 episodes
• Causal Inference — 2× in this paper · explained in 5 episodes
• Distribution Shift — 2× in this paper · explained in 12 episodes
• Feature Importance — 2× in this paper · explained in 2 episodes
• Kernel Ridge Regression — 2× in this paper · explained in 2 episodes
• Latent Variables — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Hilbert Space — 1× in this paper · explained in 2 episodes
• Intermediate Regime — 1× in this paper · explained in 2 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Orthogonal Projection — 1× in this paper · explained in 2 episodes
• Overfitting — 1× in this paper · explained in 2 episodes
• Reproducing Kernel Hilbert Space (RKHS) — 1× in this paper · explained in 7 episodes
• Sparsity — 1× in this paper · explained in 5 episodes
• Spectral Geometry — 1× in this paper · explained in 3 episodes

Sources

• Large sample analysis of the median heuristic
• Laplace Approximations for Mixed-Effects and Gaussian Process Quantile Regression
• Latent confounding in high-dimensional nonlinear models

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