Vowpal Wabbit 8.5.0 & NIPS tutorial Yesterday, I tagged VW version 8.5.0 which has many interactive learning improvements (both contextual bandit and active learning), better support for sparse models, and a new baseline reduction which I’m considering making a part of the default update rule. If you want to know the details, we’ll be doing a mini-tutorial during the Friday lunch break at the Extreme Classification workshop at NIPS. Please join us if interested.
Original Post: Vowpal Wabbit 8.5.0 & NIPS tutorial
The Real World Interactive Learning Tutorial Alekh and I have been polishin the Real World Interactive Learning tutorial for ICML 2017 on Sunday. This tutorial should be of pretty wide interest. For data scientists, we are crossing a threshold into easy use of interactive learning while for researchers interactive learning is plausibly the most important frontier of understanding. Great progress on both the theory and especially on practical systems has been made since an earlier NIPS 2013 tutorial. Please join us if you are interested
Original Post: The Real World Interactive Learning Tutorial
ICML is changing its constitution Andrew McCallum has been leading an initiative to update the bylaws of IMLS, the organization which runs ICML. I expect most people aren’t interested in such details. However, the bylaws change rarely and can have an impact over a long period of time so they do have some real importance. I’d like to hear comment from anyone with a particular interest before this year’s ICML. In my opinion, the most important aspect of the bylaws is the at-large election of members of the board which is preserved. Most of the changes between the old and new versions are aimed at better defining roles, committees, etc… to leave IMLS/ICML better organized. Anyways, please comment if you have a concern or thoughts.
Original Post: ICML is changing its constitution
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
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
The Decision Service is Hiring The Decision Service is a first-in-the-world project making tractable reinforcement learning easily used by developers everywhere. We are hiring for devel opers, data scientist, and a product manager. Please consider joining us to do something interesting this life
Original Post: The Decision Service is Hiring
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 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 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
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