When the bubble bursts…

When the bubble bursts… Consider the following facts: NIPS submission are up 50% this year to ~4800 papers. There is significant evidence that the process of reviewing papers in machine learning is creaking under several years of exponentiating growth. Public figures often overclaim the state of AI. Money rains from the sky on ambitious startups with a good story. Apparently, we now even have a fake conference website (https://nips.cc/ is the real one for NIPS). We are clearly not in a steady-state situation. Is this a bubble or a revolution? The answer surely includes a bit of revolution—the fields of vision and speech recognition have been turned over by great empirical successes created by deep neural architectures and more generally machine learning has found plentiful real-world uses. At the same time, I find it hard to believe that we aren’t…
Original Post: When the bubble bursts…

ICML Board and Reviewer profiles

ICML Board and Reviewer profiles The outcome of the election for the IMLS (which runs ICML) adds Emma Brunskill, Kamalika Chaudhuri, and Hugo Larochelle to the board. The current members of the board (and the reason for board membership) are: President Elect is a 2-year position with little responsibility, but I decided to look into two things. One is the website which seems relatively difficult to navigate. Ideas for how to improvement are welcome. The other is creating a longitudinal reviewer profile. I keenly remember the day after reviews were due when I was program chair (in 2012) which left a panic-inducing number of unfinished reviews. To help with this, I’m planning to create a profile of reviewers which program chairs can refer to in making decisions about who to ask to review. There are a number of ways to…
Original Post: ICML Board and Reviewer profiles

Pervasive Simulator Misuse with Reinforcement Learning

Pervasive Simulator Misuse with Reinforcement Learning The surge of interest in reinforcement learning is great fun, but I often see confused choices in applying RL algorithms to solve problems. There are two purposes for which you might use a world simulator in reinforcement learning: Reinforcement Learning Research: You might be interested in creating reinforcement learning algorithms for the real world and use the simulator as a cheap alternative to actual real-world application. Problem Solving: You want to find a good policy solving a problem for which you have a good simulator. In the first instance I have no problem, but in the second instance, I’m seeing many head-scratcher choices. A reinforcement learning algorithm engaging in policy improvement from a continuous stream of experience needs to solve an opportunity-cost problem. (The RL lingo for opportunity-cost is “advantage”.) Thinking about this in…
Original Post: Pervasive Simulator Misuse with Reinforcement Learning

Vowpal Wabbit 8.5.0 & NIPS tutorial

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

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

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

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