Statistical Learning
Scalable Interpretability via Polynomials
Our approach, titled Scalable Polynomial Additive Models (SP AM) is effortlessly scalable and models all higher-order feature interactions without a combinatorial parameter explosion. SP AM outperforms all current interpretable approaches, and matches DNN/XGBoost performance on a series of real-world benchmarks with up to hundreds of thousands of features.
ee74a6ade401e200985e2421b20bbae4-Paper-Conference.pdf
Our main technical contribution is the rigorous analysis of a Bayes estimator and of an approximate message passing (AMP) algorithm, both of which incorrectly assume a Gaussian setup.
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification
Meta-learning is based on the idea of learning-how-to-learn by improving the algorithm itself (Schmidhuber, 1987; Hospedales et al., 2020).
Finding Second-Order Stationary Points in Nonconvex-Strongly-Concave Minimax Optimization
P ( x) could be nonconvex.
SecureFedYJ: a safe feature Gaussianization protocol for Federated Learning
The Y eo-Johnson (YJ) transformation is a standard parametrized per-feature unidimensional transformation often used to Gaussianize features in machine learning.
Nonparametric_Uncertainty_Quantification_for_Single_Deterministic_Neural_Network (1)
This paper proposes a fast and scalable method for uncertainty quantification of machine learning models' predictions.