While Infinia ML is focused on making business impact in machine learning, our commitment to academic research keeps us on technology’s cutting edge. Our team of machine learning experts have earned 9 PhDs and authored over 600 published papers.

These are the papers published under the Infinia ML name:


Fused Gromov-Wasserstein Alignment for Hawkes Processes

NeurIPS Workshop on Learning with Temporal Point Processes


Improving Zero-Shot Learning via Optimal Transport

NeurIPS Workshop on Optimal Transport for Machine Learning


Graph-Driven Generative Models for Heterogeneous Multi-Task Learning

NeurIPS Workshop on Graph Representation Learning


Adversarial Self-Paced Learning for Mixture Models of Hawkes Processes

ICML Workshop on Time Series


Generalized Simple Word Embedding Model And Its Application To Text Classification With Automatic Tuning of Term Frequency and Inverse Document Frequency  

KDD AutoML Workshop on Automation in Machine Learning


Modeling and Applications for Temporal Point Processes

KDD tutorial


Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching

Conference on Neural Information Processing System (NeurIPS), 2019

Five Lessons for Applying Machine Learning

Research-Technology Management

Gromov-Wasserstein Learning for Graph Matching and Node Embedding

International Conference on Machine Learning (ICML) 2019

Personalized Fashion Recommendation with Visual Explanations based on Multi-model Attention Network

Special Interest Group on Information Retrieval (SIGIR) 2019

Single-Image Rain Removal via Multi-Scale Cascading Image Generation

International Conference on Image Processing (ICIP) 2019


Topic-Guided Variational Autoencoders for Text Generation

North American Chapter of the Association for Computational Linguistics (NAACL) 2019

Greedy Orthogonal Pivoting for Non-Negative Matrix Factorization

International Conference on Machine Learning  (ICML) 2019


PoPPy: A Point Process Toolbox Based on PyTorch

Distilled Wasserstein Learning for Word Embedding and Topic Modeling

Conference on Neural Information Processing System (NIPS), 2018

Acceptance Rate: ~22%

Quaternion Convolutional Neural Networks

The European Conference on Computer Vision (ECCV), 2018

Acceptance Rate: ~25%

Predicting Smoking Events with a Time-Varying Semi-Parametric Hawkes Process Model

The Conference on Machine Learning for Healthcare, 2018

Learning Registered Point Processes from Idiosyncratic Observations

The International Conference on Machine Learning (ICML), 2018

Acceptance Rate: 25%

Flexible Network Binarization with Layer-wise Priority

The International Conference on Image Processing (ICIP), 2018.

Online Continuous-Time Tensor Factorization Based on Pairwise Interactive Point Processes

The International Joint Conference on Artificial Intelligence (IJCAI-ECAI), 2018

Oral, Acceptance Rate: 20.4%

Learning an Inverse Tone Mapping Network with a Generative Adversarial Regularizer

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018

Benefits from Superposed Hawkes Processes

The 21st International Conference on Artificial Intelligence and Statistics (AISTATS), 2018

Acceptance Rate: 33.2%