Algorithms
Optimization with Uncertainty
Classical algorithms (exact/ approximation) work with an input which is entirely specified up front. While this offline model is useful for static optimization problems, there are several domains which need algorithms to make decisions with…
A Survey on Embedding Dynamic Graphs
Keynote: Extreme classification for dense retrieval and personalized recommendation
Extreme classification is a new research area pioneered by scientists at Microsoft dealing with classification problems involving millions, or even billions, of categories. In this keynote, partner researcher Manik Varma demonstrates how extreme classification can…
Research talk: Approximate nearest neighbor search systems at scale
Building deep learning-based search and recommendation systems at internet scale requires a complete redesign of the search index. Key to this redesign is a fast, accurate, and cost-efficient indexing system for approximate nearest neighbor search.…
Panel: The future of search and recommendation: Beyond web search
The increasing ability to learn representations of text, code, and even medical chemical compounds is changing what and how people search in every domain. In this panel, we bring together experts from industry and academia…
Keynote: Universal search and recommendation
Search and recommendation are at the heart of how information workers deal with information overload. Recent advances and trends in science show us that search and recommendation will be transformed in the coming years. One…
