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09/19/2023
A Configurable Library for Generating and Manipulating Maze Datasets
Understanding how machine learning models respond to distributional shif...
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01/31/2023
Faster Predict-and-Optimize with Three-Operator Splitting
In many practical settings, a combinatorial problem must be repeatedly s...
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11/30/2022
Taming Hyperparameter Tuning in Continuous Normalizing Flows Using the JKO Scheme
A normalizing flow (NF) is a mapping that transforms a chosen probabilit...
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04/29/2022
Explainable AI via Learning to Optimize
Indecipherable black boxes are common in machine learning (ML), but appl...
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04/11/2022
A numerical algorithm for inverse problem from partial boundary measurement arising from mean field game problem
In this work, we consider a novel inverse problem in mean-field games (M...
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02/25/2022
Random Features for High-Dimensional Nonlocal Mean-Field Games
We propose an efficient solution approach for high-dimensional nonlocal ...
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09/02/2021
Adaptive Uncertainty-Weighted ADMM for Distributed Optimization
We present AUQ-ADMM, an adaptive uncertainty-weighted consensus ADMM met...
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06/02/2021
Learn to Predict Equilibria via Fixed Point Networks
Systems of interacting agents can often be modeled as contextual games, ...
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04/29/2021
Feasibility-based Fixed Point Networks
Inverse problems consist of recovering a signal from a collection of noi...
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03/23/2021
Fixed Point Networks: Implicit Depth Models with Jacobian-Free Backprop
A growing trend in deep learning replaces fixed depth models by approxim...
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08/05/2020
Projecting to Manifolds via Unsupervised Learning
We present a new framework, called adversarial projections, for solving ...
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05/29/2020
OT-Flow: Fast and Accurate Continuous Normalizing Flows via Optimal Transport
A normalizing flow is an invertible mapping between an arbitrary probabi...
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05/27/2020
PNKH-B: A Projected Newton-Krylov Method for Large-Scale Bound-Constrained Optimization
We present PNKH-B, a projected Newton-Krylov method with a low-rank appr...
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01/27/2019