Machine Learning the Future Class

Machine Learning the Future Class This spring, I taught a class on Machine Learning the Future at Cornell Tech covering a number of advanced topics in machine learning including online learning, joint (structured) prediction, active learning, contextual bandit learning, logarithmic time prediction, and parallel learning. Each of these classes was recorded from the laptop via Zoom and I just uploaded the recordings to Youtube. In some ways, this class is a followup to the large scale learning class I taught with Yann LeCun 4 years ago. The videos for that class were taken down(*) so these lectures both update and replace shared subjects as well as having some new subjects. Much of this material is fairly close to research so to assist other machine learning lecturers around the world in digesting the material, I’ve made all the source available as…
Original Post: Machine Learning the Future Class

Fact over Fiction

Politics is a distracting affair which I generally believe it’s best to stay out of if you want to be able to concentrate on research. Nevertheless, the US presidential election looks like something that directly politicizes the idea and process of research by damaging the association of scientists & students, funding for basic research, and creating political censorship. A core question here is: What to do? Today’s March for Science is a good step, but I’m not sure it will change many minds. Unlike most scientists, I grew up in a a county (Linn) which voted overwhelmingly for Trump. As a consequence, I feel like I must translate the mindset a bit. For the median household left behind over my lifetime a march by relatively affluent people protesting the government cutting expenses will not elicit much sympathy. Discussion about the…
Original Post: Fact over Fiction

EWRL and NIPS 2016

EWRL and NIPS 2016 I went to the European Workshop on Reinforcement Learning and NIPS last month and saw several interesting things. At EWRL, I particularly liked the talks from: Remi Munos on off-policy evaluation Mohammad Ghavamzadeh on learning safe policies Emma Brunskill on optimizing biased-but safe estimators (sense a theme?) Sergey Levine on low sample complexity applications of RL in robotics. My talk is here. Overall, this was a well organized workshop with diverse and interesting subjects, with the only caveat being that they had to limit registration At NIPS itself, I found the poster sessions fairly interesting. Allen-Zhu and Hazan had a new notion of a reduction (video). Zhao, Poupart, and Gordon had a new way to learn Sum-Product Networks Ho, Littman, MacGlashan, Cushman, and Austerwell, had a paper on how “Showing” is different from “Doing”. Toulis and…
Original Post: EWRL and NIPS 2016

Vowpal Wabbit version 8.3 and tutorial

Vowpal Wabbit version 8.3 and tutorial I just released Vowpal Wabbit 8.3 and we are planning a tutorial at NIPS Saturday over the lunch break in the ML systems workshop. Please join us if interested. 8.3 should be backwards compatible with all 8.x series. There have been big changes since the last version related to Contextual bandits, particularly w.r.t. the decision service. Learning to search for which we have a paper at NIPS. Logarithmic time multiclass classification.
Original Post: Vowpal Wabbit version 8.3 and tutorial

ICML 2016 videos and statistics

ICML 2016 videos and statistics The ICML 2016 videos are out. I also wanted to share some statistics from registration that might be of general interest. The total number of people attending: 3103. Industry: 47% University: 46% Male: 83% Female: 14% Local (NY, NJ, or CT): 27% North America: 70% Europe: 18% Asia: 9% Middle East: 2% Remainder: <1% including…
Original Post: ICML 2016 videos and statistics

ICML 2016 was awesome

I had a fantastic time at ICML 2016— I learned a great deal. There was far more good stuff than I could see, and it was exciting to catch up on recent advances.David Silver gave one of the best tutorials I’ve seen on his group’s recent work in “deep” reinforcement learning. I learned about a few new techniques, including the…
Original Post: ICML 2016 was awesome

The Multiworld Testing Decision Service

The Multiworld Testing Decision Service We made a tool that you can use. It is the first general purpose reinforcement-based learning system Reinforcement learning is much discussed these days with successes like AlphaGo. Wouldn’t it be great if Reinforcement Learning algorithms could easily be used to solve all reinforcement learning problems? But there is a well-known problem: It’s very easy…
Original Post: The Multiworld Testing Decision Service

An ICML unworkshop

An ICML unworkshop Following up on an interesting suggestion, we are creating a “Birds of a Feather Unworkshop” with a leftover room (Duffy/Columbia) on Thursday and Friday during the workshops. People interested in ad-hoc topics can post a time and place to meet and discuss. Details are here a little ways down.
Original Post: An ICML unworkshop

The ICML 2016 Space Fight

The space problem started long ago. At ICML last year and the year before the amount of capacity that needed to fit everyone on any single day was about 1500. My advice was to expect 2000 and have capacity for 2500 because “New York” and “Machine Learning”. Was history right? Or New York and buzz? I was not involved in…
Original Post: The ICML 2016 Space Fight