Search Results for author:
Found 8 papers, 4 papers with code
Augmenting Interpretable Models with LLMs during Training
Recent large language models (LLMs) have demonstrated remarkable prediction performance for a growing array of tasks.
FANOK: Knockoffs in Linear Time
We describe a series of algorithms that efficiently implement Gaussian model-X knockoffs to control the false discovery rate on large scale feature selection problems.
Implicit Deep Learning
Implicit deep learning prediction rules generalize the recursive rules of feedforward neural networks.
Naive Feature Selection: Sparsity in Naive Bayes
We propose a sparse version of naive Bayes, which can be used for feature selection.
Fenchel Lifted Networks: A Lagrange Relaxation of Neural Network Training
In this paper, we introduce a new class of lifted models, Fenchel lifted networks, that enjoy the same benefits as previous lifted models, without suffering a degradation in performance over classical networks.
Greedy Frank-Wolfe Algorithm for Exemplar Selection
In this paper, we consider the problem of selecting representatives from a data set for arbitrary supervised/unsupervised learning tasks.
Kernel-based Outlier Detection using the Inverse Christoffel Function
Outlier detection methods have become increasingly relevant in recent years due to increased security concerns and because of its vast application to different fields.
Lifted Neural Networks
We describe a novel family of models of multi- layer feedforward neural networks in which the activation functions are encoded via penalties in the training problem.
