additive model

Decorrelated Local Linear Estimator: Inference for Non-linear Effects in High-dimensional Additive Models JMLR 2026 ADHAM: Additive Deep Hazard Analysis Mixtures for Interpretable Survival Regression MLHC 2025 Boosting Causal Additive Models JMLR 2025 Learning Joint Interventional Effects from Single-Variable Interventions in Additive Models ICML 2025 Quantile Additive Trend Filtering AISTATS 2025 A Functional Extension of Semi-Structured Networks NeurIPS 2024 Are Logistic Models Really Interpretable? IJCAI 2024 Learning sum of diverse features: computational hardness and efficient gradient-based training for ridge combinations COLT 2024 More Efficient Estimation of Multivariate Additive Models Based on Tensor Decomposition and Penalization JMLR 2024 Orthogonal Gradient Boosting for Simpler Additive Rule Ensembles AISTATS 2024 Unsupervised Tree Boosting for Learning Probability Distributions JMLR 2024 Additive Causal Bandits with Unknown Graph ICML 2023 Causal Discovery with Score Matching on Additive Models with Arbitrary Noise CLeaR 2023 NA^2Q: Neural Attention Additive Model for Interpretable Multi-Agent Q-Learning ICML 2023 Statistical Learning under Heterogeneous Distribution Shift ICML 2023 Tilted Sparse Additive Models ICML 2023 A cautionary tale on fitting decision trees to data from additive models: generalization lower bounds AISTATS 2022 Additive Gaussian Processes Revisited ICML 2022 Additive Nonlinear Quantile Regression in Ultra-high Dimension JMLR 2022 Fast Sparse Classification for Generalized Linear and Additive Models AISTATS 2022 High-dimensional Additive Gaussian Processes under Monotonicity Constraints NeurIPS 2022 Sparse Additive Gaussian Process Regression JMLR 2022 Sparse Interaction Additive Networks via Feature Interaction Detection and Sparse Selection NeurIPS 2022 High-Dimensional Bayesian Optimization via Tree-Structured Additive Models AAAI 2021 Sparse Tensor Additive Regression JMLR 2021