Microsoft Technology Licensing, LLC

Mengting Wan

Longqi Yang

Jaime Brooks Teevan

Brent Jaron Hecht

Scott Joseph Counts

Reid Andersen

Chirag Deepakkumar Shah

Sujay Kumar Jauhar

Sathish Kumar Manivannan

Xiaochuan Ni

Kiran Tomlinson

Cao Lu

Siddharth Suri

Jennifer Lynay Neville

Georg Ludwig Wilhelm Buscher

Jing Ma

Tara Lynn Safavi

Various embodiments discussed herein relate to prompting a model, such as a Large Language Model (LLM), to ingest natural language clustering instructions and generate corresponding natural language clustering information, such as a cluster description and/or a cluster label without the need to generate any numeric text embeddings.

Dataset clustering via language model prompts

An enterprise system server, a computer-readable storage medium, and a method for targeted training of inductive multi-organization recommendation models for enterprise applications are described herein. The method includes receiving enterprise application data from remote organization computing systems executing the enterprise application, training per-organization recommendation models for a subset of the organizations, and validating each per-organization recommendation model on enterprise application data corresponding to one or more other organizations. The method also includes calculating a transferability metric for each per-organization recommendation model based on results obtained during validation, determining a specified number of organizations including the best-transferring per-organization recommendation models based on the calculated transferability metrics, and training an inductive multi-organization recommendation model using the enterprise application data from the specified number of organizations. The method further includes utilizing the trained inductive multi-organization recommendation model to provide user recommendations to the remote organization computing systems during execution of the enterprise application.

Targeted training of inductive multi-organization recommendation models for enterprise applications

A computing system, computer-readable storage medium, and method for individual treatment effect (ITE) estimation under high-order interference in hypergraphs are described herein. The method includes accessing, via a processor, a hypergraph dataset including multi-way interactions among nodes within each hyperedge of a corresponding hypergraph, where the hypergraph dataset corresponds to a treatment assignment for each node. The method includes performing representation learning on the hypergraph dataset to control for confounders corresponding to features of each node and to learn a confounder representation for each node. The method also includes modeling a high-order interference representation for each node by propagating the learned confounder representation and the treatment assignment for each node through a hypergraph neural network. The method further includes estimating the ITE for each node under the treatment assignment based on the learned confounder representation and the modeled high-order interference representation for each node.

Individual treatment effect estimation under high-order interference in hypergraphs

Growing community of inventors

Bellevue, WA, United States of America

Mengting Wan