Machine Learning (Theory)


Interesting papers at ICML 2014

This year’s ICML had several papers which I want to read through more carefully and understand better.

  1. Chun-Liang Li, Hsuan-Tien Lin, Condensed Filter Tree for Cost-Sensitive Multi-Label Classification. Several tricks accumulate to give a new approach for addressing cost sensitive multilabel classification.
  2. Nikos Karampatziakis and Paul Mineiro, Discriminative Features via Generalized Eigenvectors. An efficient, effective eigenvalue solution for supervised learning yields compelling nonlinear performance on several datasets.
  3. Nir Ailon, Zohar Karnin, Thorsten Joachims, Reducing Dueling Bandits to Cardinal Bandits. An effective method for reducing dueling bandits to normal bandits that extends to contextual situations.
  4. Pedro Pinheiro, Ronan Collobert, Recurrent Convolutional Neural Networks for Scene Labeling. Image parsing remains a challenge, and this is plausibly a step forward.
  5. Cicero Dos Santos, Bianca Zadrozny, Learning Character-level Representations for Part-of-Speech Tagging. Word morphology is clearly useful information, and yet almost all ML-for-NLP applications ignore it or hard-code it (by stemming).
  6. Alekh Agarwal, Daniel Hsu, Satyen Kale, John Langford, Lihong Li, Robert Schapire, Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits. Statistically efficient interactive learning is now computationally feasible. I wish this one had been done in time for the NIPS tutorial :-)
  7. David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, Martin Riedmiller, Deterministic Policy Gradient Algorithms. A reduction in variance from working out the deterministic limit of policy gradient make policy gradient approaches look much more attractive.

Edit: added one that I forgot.


The New York ML Symposium, take 2

The 201314 is New York Machine Learning Symposium is finally happening on March 28th at the New York Academy of Science. Every invited speaker interests me personally. They are:

We’ve been somewhat disorganized in advertising this. As a consequence, anyone who has not submitted an abstract but would like to do so may send one directly to me ( title NYASMLS) by Friday March 14. I will forward them to the rest of the committee for consideration.


Metacademy: a package manager for knowledge

In recent years, there’s been an explosion of free educational resources that make high-level knowledge and skills accessible to an ever-wider group of people. In your own field, you probably have a good idea of where to look for the answer to any particular question. But outside your areas of expertise, sifting through textbooks, Wikipedia articles, research papers, and online lectures can be bewildering (unless you’re fortunate enough to have a knowledgeable colleague to consult). What are the key concepts in the field, how do they relate to each other, which ones should you learn, and where should you learn them?

Courses are a major vehicle for packaging educational materials for a broad audience. The trouble is that they’re typically meant to be consumed linearly, regardless of your specific background or goals. Also, unless thousands of other people have had the same background and learning goals, there may not even be a course that fits your needs. Recently, we (Roger Grosse and Colorado Reed) have been working on Metacademy, an open-source project to make the structure of a field more explicit and help students formulate personal learning plans.

Metacademy is built around an interconnected web of concepts, each one annotated with a short description, a set of learning goals, a (very rough) time estimate, and pointers to learning resources. The concepts are arranged in a prerequisite graph, which is used to generate a learning plan for a concept. In this way, Metacademy serves as a sort of “package manager for knowledge.”

Currently, most of our content is related to machine learning and probabilistic AI; for instance, here are the learning plan and graph for deep belief nets.

the learning plan for deep belief nets part of the learning graph for deep belief nets

Metacademy also has wiki-like documents called roadmaps, which briefly overview key concepts in a field and explain why you might want to learn about them; here’s one we wrote for Bayesian machine learning.

Many ingredients of Metacademy are drawn from pre-existing systems, including Khan Academy,, Connexions, and many intelligent tutoring systems. We’re not trying to be the first to do any particular thing; rather, we’re trying to build a tool that we personally wanted to exist, and we hope others will find it useful as well.

Granted, if you’re reading this blog, you probably have a decent grasp of most of the concepts we’ve annotated. So how can Metacademy help you? If you’re teaching an applied course and don’t want to re-explain Gibbs sampling, you can simply point your students to the concept on Metacademy. Or, if you’re writing a textbook or teaching a MOOC, Metacademy can help potential students find their way there. Don’t worry about self-promotion: if you’ve written something you think people will find useful, feel free to add a pointer!

We are hoping to expand the content beyond machine learning, and we welcome contributions. You can create a roadmap to help people find their way around a field. We are currently working on a GUI for editing the concepts and the graph connecting them (our current system is based on Github pull requests), and we’ll send an email to our registered users once this system is online. If you find Metacademy useful or want to contribute, let us know at feedback _at_ metacademy _dot_ org.


NIPS tutorials and Vowpal Wabbit 7.4

At NIPS I’m giving a tutorial on Learning to Interact. In essence this is about dealing with causality in a contextual bandit framework. Relative to previous tutorials, I’ll be covering several new results that changed my understanding of the nature of the problem. Note that Judea Pearl and Elias Bareinboim have a tutorial on causality. This might appear similar, but is quite different in practice. Pearl and Bareinboim’s tutorial will be about the general concepts while mine will be about total mastery of the simplest nontrivial case, including code. Luckily, they have the right order. I recommend going to both :-)

I also just released version 7.4 of Vowpal Wabbit. When I was a frustrated learning theorist, I did not understand why people were not using learning reductions to solve problems. I’ve been slowly discovering why with VW, and addressing the issues. One of the issues is that machine learning itself was not automatic enough, while another is that creating a very low overhead process for doing learning reductions is vitally important. These have been addressed well enough that we are starting to see compelling results. Various changes:

  • The internal learning reduction interface has been substantially improved. It’s now pretty easy to write new learning reduction. provides a good example. This is a very simple reduction which just binarizes the prediction. More improvements are coming, but this is good enough that other people have started contributing reductions.
  • Zhen Qin had a very productive internship with Vaclav Petricek at eharmony resulting in several systemic modifications and some new reductions, including:
    1. A direct hash inversion implementation for use in debugging.
    2. A holdout system which takes over for progressive validation when multiple passes over data are used. This keeps the printouts ‘honest’.
    3. An online bootstrap mechanism system which efficiently provides some understanding of prediction variations and which can sometimes effectively trade computational time for increased accuracy via ensembling. This will be discussed at the biglearn workshop at NIPS.
    4. A top-k reduction which chooses the top-k of any set of base instances.
  • Hal Daume has a new implementation of Searn (and Dagger, the codes are unified) which makes structured prediction solutions far more natural. He has optimized this quite thoroughly (exercising the reduction stack in the process), resulting in this pretty graph.
    part of speech tagging time accuracy tradeoffs
    Here, CRF++ is commonly used conditional random field code, SVMstruct is an SVM-style approach to classification, and CRF SGD is an online learning CRF approach. All of these methods use the same features. Fully optimized code is typically rough, but this one is less than 100 lines.

I’m trying to put together a tutorial on these things at NIPS during the workshop break on the 9th and will add details as that resolves for those interested enough to skip out on skiing :-)

Edit: The VW tutorial will take place during the break at the big learning workshop from 1:30pm – 3pm at Harveys Emerald Bay B.


Ben Taskar is gone

Tags: Announcements,Machine Learning jl@ 12:13 pm

I was not as personally close to Ben as Sam, but the level of tragedy is similar and I can’t help but be greatly saddened by the loss.

Various news stories have coverage, but the synopsis is that he had a heart attack on Sunday and is survived by his wife Anat and daughter Aviv. There is discussion of creating a memorial fund for them, which I hope comes to fruition, and plan to contribute to.

I will remember Ben as someone who thought carefully and comprehensively about new ways to do things, then fought hard and successfully for what he believed in. It is an ideal we strive for, that Ben accomplished.

Edit: donations go here, and more information is here.

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