A library for differentiable nonlinear optimization
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Updated
Aug 21, 2024 - Python
A library for differentiable nonlinear optimization
Bilevel Optimization Library in Python for Multi-Task and Meta Learning
TorchOpt is an efficient library for differentiable optimization built upon PyTorch.
Betty: an automatic differentiation library for generalized meta-learning and multilevel optimization
An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming (ICML'21)
Example code for paper "Bilevel Optimization: Nonasymptotic Analysis and Faster Algorithms"
MetaStyle: Three-Way Trade-Off Among Speed, Flexibility, and Quality in Neural Style Transfer
PyTorch implementation of "STNs" and "Delta-STNs".
Benchmark for bi-level optimization solvers
Example Code for paper "Provably Faster Algorithms for Bilevel Optimization"
Deep Bilevel Learning. In ECCV, 2018.
Formulations for the robust Resource-Constrained Project Scheduling Problem (RCPSP) using Pyomo modelling.
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.
RECKONING is a bi-level learning algorithm that improves language models' reasoning ability by folding contextual knowledge into parametric knowledge through back-propagation.
Code base for SICNav: Safe and Interactive Crowd Navigation using Model Predictive Control and Bilevel Optimization
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
Solver for Bilevel Parameter Learning using a MPCC reformulation
Code accompanying the paper "Heuristic Methods for Mixed-Integer, Linear, and Gamma-Robust Bilevel Problems" (with Ivana Ljubic and Martin Schmidt)
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