pursueorigin

AdvancedOptML

CS 7301: Spring 2021 Course on Advanced Topics in Optimization in Machine Learning

awesome-beamer

Creating presentation slides by using Beamer in LaTeX.

BNS-GCN

[MLSys 2022] "BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling" by Cheng Wan, Youjie Li, Ang Li, Nam Sung Kim, Yingyan Lin

BROADCAST

Byzantine-Federeated-RL

CFLP

Author: Tong Zhao (tzhao2@nd.edu). Counterfactual Graph Learning for Link Prediction

CGIPool

CGS

The official implementation of Convergent Graph Solvers (CGS).

chop

CHOP: An optimization library based on PyTorch, with applications to adversarial examples.

cornell-cs5785-applied-ml

Teaching materials for the applied machine learning course at Cornell Tech

CPF

The official code of WWW2021 paper: Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework

Dec-TD-GT

The code of paper "DECENTRALIZED TD(0) WITH GRADIENT TRACKING"

dpcmu.github.io

Course webpage

FedScale

FedScale: Benchmarking Model and System Performance of Federated Learning

GMT

Official Code Repository for the paper "Accurate Learning of Graph Representations with Graph Multiset Pooling" (ICLR 2021).

graph-barlow-twins

The official implementation of the Graph Barlow Twins method with the experimental pipeline

graph_augmentation

GraphLoG

Implementation of Self-supervised Graph-level Representation Learning with Local and Global Structure (ICML 2021).

HFW

Code of NeurIPS 2021 paper: Heavy Ball Momentum for Conditional Gradient

IGNN

Implicit Graph Neural Networks

MD-PGT

Repository for MDPGT

multi-center-fed-learning

fully ready experiments

Optimization-Mavericks

This repository provides a unified framework to perform Optimization experiments across Stochastic, Mini-Batch, Decentralized and Federated Setting.

PipeGCN

[ICLR 2022] "PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication" by Cheng Wan, Youjie Li, Cameron R. Wolfe, Anastasios Kyrillidis, Nam Sung Kim, Yingyan Lin

PTDNet

Learning to Drop: Robust Graph Neural Network via Topological Denoising & Robust Graph Representation Learning via Neural Sparsification

pyg_autoscale

Implementation of "GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings" in PyTorch

SGMRL

SG-MRL codebase based on ProMP

stocBiO

Example code for paper "Bilevel Optimization: Nonasymptotic Analysis and Faster Algorithms"

variance_reduced_neural_networks

Implementation of SVRG and SAGA optimization algorithms for deep learning topics.

WEGL

The implementation code for our paper Wasserstein Embedding for Graph Learning (WEGL).