Microsoft Research | Machine Learning for Combinatorial Optimization: Some Empirical Studies @MicrosoftResearch | Uploaded December 2022 | Updated October 2024, 1 week ago.
2022 Data-driven Optimization Workshop: Machine Learning for Combinatorial Optimization: Some Empirical Studies
Speaker: Junchi Yan, Shanghai Jiao Tong University
In this talk, I will present our lab’s recent progress and empirical results including some results on EDA, on machine learning for combinatorial optimiation, which has been an emerging topic in both communities of machine learning and operational research. I will also discuss the potential future directions for this exciting area.
2022 Data-driven Optimization Workshop: Machine Learning for Combinatorial Optimization: Some Empirical Studies
Speaker: Junchi Yan, Shanghai Jiao Tong University
In this talk, I will present our lab’s recent progress and empirical results including some results on EDA, on machine learning for combinatorial optimiation, which has been an emerging topic in both communities of machine learning and operational research. I will also discuss the potential future directions for this exciting area.
![Podcast | Collaborators: Teachable AI with Cecily Morrison & Karolina Pakėnaitė [ASL interpretation]](https://i.ytimg.com/vi/e2Kd0O8Wr-8/hqdefault.jpg "Podcast | Collaborators: Teachable AI with Cecily Morrison & Karolina Pakėnaitė [ASL interpretation]
The Microsoft Research Podcast series Collaborators explores the relationships behind the projects, products, and services being pursued and delivered by researchers at Microsoft and the diverse range of people they’re teaming up with.
In this episode, host Gretchen Huizinga speaks with Cecily Morrison, MBE, a Senior Principal Research Manager at Microsoft Research, and Karolina Pakėnaitė, a PhD student and member of the citizen design team working with Morrison on the research project Find My Things. An AI phone application designed to help people who are blind or have low vision locate their personal items, Find My Things is an example of a broader research approach known as Teachable AI. Morrison and Pakėnaitė explore the Teachable AI goal of empowering people to make AI experiences work for them and discuss how “designing for one” leads to innovative solutions and what they learned about optimizing these systems for real-world use (spoiler: it’s not necessarily more or higher-quality data).
For audio of the episode, a full transcript, and other resources, visit the episode landing page: https://www.microsoft.com/en-us/research/podcast/collaborators-teachable-ai-with-cecily-morrison-and-karolina-pakenaite/")
