4 code implementations • 23 Sep 2022 • Chandan Singh, Armin Askari, Rich Caruana, Jianfeng Gao
Recent large language models (LLMs) have demonstrated remarkable prediction performance for a growing array of tasks.
1 code implementation • 15 Jun 2020 • Armin Askari, Quentin Rebjock, Alexandre d'Aspremont, Laurent El Ghaoui
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.
no code implementations • 17 Aug 2019 • Laurent El Ghaoui, Fangda Gu, Bertrand Travacca, Armin Askari, Alicia Y. Tsai
Implicit deep learning prediction rules generalize the recursive rules of feedforward neural networks.
no code implementations • 23 May 2019 • Armin Askari, Alexandre d'Aspremont, Laurent El Ghaoui
We propose a sparse version of naive Bayes, which can be used for feature selection.
1 code implementation • 20 Nov 2018 • Fangda Gu, Armin Askari, Laurent El Ghaoui
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.
2 code implementations • 6 Nov 2018 • Gary Cheng, Armin Askari, Kannan Ramchandran, Laurent El Ghaoui
In this paper, we consider the problem of selecting representatives from a data set for arbitrary supervised/unsupervised learning tasks.
no code implementations • 18 Jun 2018 • Armin Askari, Forest Yang, Laurent El Ghaoui
Outlier detection methods have become increasingly relevant in recent years due to increased security concerns and because of its vast application to different fields.
no code implementations • 3 May 2018 • Armin Askari, Geoffrey Negiar, Rajiv Sambharya, Laurent El Ghaoui
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.