9 citations · 15 across the 5 of their papers we have counts for
6 papers
From Large Language Models and Optimization to Decision Optimization CoPilot: A Research Manifesto
Segev Wasserkrug, Leonard Boussioux, Dick den Hertog +4
Significantly simplifying the creation of optimization models for real-world business problems has long been a major goal in applying mathematical optimization more widely to impor…
GC-Flow: A Graph-Based Flow Network for Effective Clustering
Tianchun Wang, Farzaneh Mirzazadeh, Xiang Zhang +1
Graph convolutional networks (GCNs) are \emph{discriminative models} that directly model the class posterior for semi-supervised classification of graph data. Whi…
Alleviating Label Switching with Optimal Transport
Pierre Monteiller, Sebastian Claici, Edward Chien +3
Label switching is a phenomenon arising in mixture model posterior inference that prevents one from meaningfully assessing posterior statistics using standard Monte Carlo procedure…
Hierarchical Optimal Transport for Document Representation
Mikhail Yurochkin, Sebastian Claici, Edward Chien +2
The ability to measure similarity between documents enables intelligent summarization and analysis of large corpora. Past distances between documents suffer from either an inabilit…
BreGMN: scaled-Bregman Generative Modeling Networks
Akash Srivastava, Kristjan Greenewald, Farzaneh Mirzazadeh
The family of f-divergences is ubiquitously applied to generative modeling in order to adapt the distribution of the model to that of the data. Well-definedness of f-divergences, h…
Learning Embeddings into Entropic Wasserstein Spaces
Charlie Frogner, Farzaneh Mirzazadeh, Justin Solomon
Euclidean embeddings of data are fundamentally limited in their ability to capture latent semantic structures, which need not conform to Euclidean spatial assumptions. Here we cons…