13 citations · 36 across the 5 of their papers we have counts for
5 papers
CLOVER : Probabilistic Forecasting with Coherent Learning Objective Reparameterization
Kin G. Olivares, Geoffrey Négiar, Ruijun Ma +3
Obtaining accurate probabilistic forecasts is an operational challenge in many applications, such as energy management, climate forecasting, supply chain planning, and resource all…
Learning differentiable solvers for systems with hard constraints
Geoffrey Négiar, Michael W. Mahoney, Aditi S. Krishnapriyan
We introduce a practical method to enforce partial differential equation (PDE) constraints for functions defined by neural networks (NNs), with a high degree of accuracy and up to…
Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization
Geoffrey Négiar, Gideon Dresdner, Alicia Tsai +4
We propose a novel Stochastic Frank-Wolfe (a.k.a. conditional gradient) algorithm for constrained smooth finite-sum minimization with a generalized linear prediction/structure. Thi…
Lifted Neural Networks
Armin Askari, Geoffrey Negiar, Rajiv Sambharya +1
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. Our approach i…
Linearly Convergent Frank-Wolfe with Backtracking Line-Search
Fabian Pedregosa, Geoffrey Negiar, Armin Askari +1
Structured constraints in Machine Learning have recently brought the Frank-Wolfe (FW) family of algorithms back in the spotlight. While the classical FW algorithm has poor local co…