Event
Microsoft @ PPoPP/HPCA/CGO 2019
Microsoft is excited to be part of the PPoPP/HPCA/CGO 2019 co-located conferences and is a Gold sponsor of PPoPP 2019 and HPCA 2019. Stop by our booth to learn about our latest research and find…
Algorithms
Publication
Polarization Through the Lens of Learning Theory
Microsoft Research Blog
What are the biases in my data?
One challenge with AI algorithmic fairness is that one usually has to know the potential group(s) that an algorithm might discriminate against in the first place. However, in joint work with Maria De-Arteaga, Nathaniel Swinger,…
Podcast
Competing in the X Games of machine learning with Dr. Manik Varma
Episode 63, February 13, 2019 – Dr. Varma tells us all about extreme classification (including where in the world you might actually run into 10 or 100 million options), reveals how his Parabel and Slice…
Microsoft Research Blog
Everything you always wanted to know about extreme classification (but were afraid to ask)
Varma’s team published a paper that exploded the number of choices that could be considered from a search engine from five thousand to ten million. This changed the nature of the game and led to…
Video
Northwest Database Society Annual Meeting – Session 3
Northwest Database Society Annual Meeting Session 3: Open-Source Technologies for Streaming and State Management, Improving Optimistic Concurrency Control Through Transaction Batching and Operation Reordering, Towards a Learning Optimizer for Shared Clouds, and more. Agenda: 1:15…
Video
Northwest Database Society Annual Meeting – Session 2
Northwest Database Society Annual Meeting Session 2: Urban Data Systems, Democratize Data Preparation for AI, CutTheTail, Integrity Constraints Revisited: From Exact to Approximate Implication, Automating Machine Learning Model Building with Big Clinical Data and more.…
Video
Northwest Database Society Annual Meeting: Explainable Artificial Intelligence in Precision Medicine
Modern machine learning models can accurately predict patient progress and outcomes, however, they are not interpretable in the sense that they do not explain why selected features make sense or why a particular prediction result…
