Search Results for author:
Found 10 papers, 6 papers with code
Learning to Solve Integer Linear Programs with Davis-Yin Splitting
In many applications, a combinatorial problem must be repeatedly solved with similar, but distinct parameters.
Explainable AI via Learning to Optimize
Indecipherable black boxes are common in machine learning (ML), but applications increasingly require explainable artificial intelligence (XAI).
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Operator Splitting for Learning to Predict Equilibria in Convex Games
Systems of competing agents can often be modeled as games.
Feasibility-based Fixed Point Networks
This is accomplished using feasibility-based fixed point networks (F-FPNs).
Learning to Optimize: A Primer and A Benchmark
It automates the design of an optimization method based on its performance on a set of training problems.
JFB: Jacobian-Free Backpropagation for Implicit Networks
Unlike traditional networks, implicit networks solve a fixed point equation to compute inferences.
Learning A Minimax Optimizer: A Pilot Study
We first present Twin L2O, the first dedicated minimax L2O framework consisting of two LSTMs for updating min and max variables, respectively.
Wasserstein-based Projections with Applications to Inverse Problems
To bridge this gap, we present a new algorithm that takes samples from the manifold of true data as input and outputs an approximation of the projection operator onto this manifold.
Safeguarded Learned Convex Optimization
Our numerical examples show convergence of Safe-L2O algorithms, even when the provided data is not from the distribution of training data.
Universal Safeguarded Learned Convex Optimization with Guaranteed Convergence
Inferences by each network form solution estimates, and networks are trained to optimize these estimates for a particular distribution of data.
