Julia library for the Network Pricing Problem (NPP)
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Updated
May 11, 2023 - Julia
Julia library for the Network Pricing Problem (NPP)
This repository contains the source code used in the computational experiments of the paper: Learning to Solve Bilevel Programs with Binary Tender (ICLR 2024, available on OpenReview.net).
Nonsmooth Bilevel Parameter Learning of Imaging Variational Models
Code repository for the paper: A Catalog of Formulations for the Network Pricing Problem.
BROT is a self-adaptation algorithm that trade offs traffic mitigation and shortest path length on transportation networks
Solver for Bilevel Parameter Learning using a MPCC reformulation
RECKONING is a bi-level learning algorithm that improves language models' reasoning ability by folding contextual knowledge into parametric knowledge through back-propagation.
Ph.D daily work log
Nonsmooth Bilevel Parameter Learning for Image Denoising
Code accompanying the paper "Heuristic Methods for Mixed-Integer, Linear, and Gamma-Robust Bilevel Problems" (with Ivana Ljubic and Martin Schmidt)
Instances of aircraft deconfliction problem via speed regulation in 3 dimensions.
A collection of programs used to generate test cases for the main solver of a research project of mine involving a trilevel network interdiction game on an interdependent network.
A Julia package for adaptive proximal gradient for convex bilevel optimization
A program for generating computational results for a research project of mine involving a trilevel network interdiction game on an interdependent network.
Thousands of bibliographic references on bi-level optimization.
COMBINATORIAL OPTIMIZATION & NETWORK ANALYSIS - AUT- Professor: FARNAZ HOOSHMAND KHALIGH
In this repository, we implement Targeted Meta-Learning (or Targeted Data-driven Regularization) architecture for training machine learning models with biased data.
In this repository, we implement Targeted Meta-Learning (or Targeted Data-driven Regularization) architecture for training machine learning models with biased data.
MetaOpt: Towards efficient heuristic design with quantifiable and confident performance
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