An AI Miracle Malcontent
John Langford’s Machine Learning Theory
by John Langford
1y ago
The stark success of OpenAI’s GPT4 model surprised me shifting my view from “really good autocomplete” (roughly inline with intuitions here) to a dialog agent exhibiting a significant scope of reasoning and intelligence. Some of the MSR folks did a fairly thorough study of capabilities which seems like a good reference. I think of GPT4 as an artificial savant: super-John capable in some language-centric tasks like style and summarization with impressive yet more limited abilities in other domains like spatial and reasoning intelligence. And yet, I’m unhappy with mere acceptance because there i ..read more
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ICML 2021 Invited Speakers — ML for Science
John Langford’s Machine Learning Theory
by Ameet
2y ago
By: Stefanie Jegelka and Ameet Talwalkar (ICML21 Communication Chairs) With ICML 2021 underway, we wanted to briefly highlight the upcoming invited talks. A general theme of the invited talks this year is “machine learning for science.” The Program Chairs (Marina Meila and Tong Zhang) have invited world-renowned scientists from various disciplines to discuss their problems and the corresponding machine learning challenges. By exposing the machine learning community to these fascinating problems, we hope that we can help to further expand the applicability of machine learning to a wide range of ..read more
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ALT Highlights – An Interview with Joelle Pineau
John Langford’s Machine Learning Theory
by GautamKamath
3y ago
Welcome to ALT Highlights, a series of blog posts spotlighting various happenings at the recent conference ALT 2021, including plenary talks, tutorials, trends in learning theory, and more! To reach a broad audience, the series will be disseminated as guest posts on different blogs in machine learning and theoretical computer science. John has been kind enough to host the first post in the series. This initiative is organized by the Learning Theory Alliance, and overseen by Gautam Kamath. All posts in ALT Highlights are indexed on the official Learning Theory Alliance blog. The first post is a ..read more
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What is the Right Response to Employer Misbehavior in Research?
John Langford’s Machine Learning Theory
by John Langford
3y ago
I enjoyed my conversations with Timnit when she was in the MSR-NYC lab, so her situation has been on my mind throughout NeurIPS. Piecing together what happened second-hand is always tricky, but Jeff Dean’s account and Timnit’s agree on a basic outline. Timnit and others wrote a paper for FAccT which was approved for submission by the normal internal review process, then later unapproved. Timnit threatened to leave unless various details about this unapproval were clarified. Google then declared her resigned. The definition of resign makes it clear an employee does it, not an employer. Since th ..read more
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Experiments with the ICML 2020 Peer-Review Process
John Langford’s Machine Learning Theory
by stiv
3y ago
This post is cross-listed on the CMU ML blog. The International Conference on Machine Learning (ICML) is a flagship machine learning conference that in 2020 received 4,990 submissions and managed a pool of 3,931 reviewers and area chairs. Given that the stakes in the review process are high — the careers of researchers are often significantly affected by the publications in top venues — we decided to scrutinize several components of the peer-review process in a series of experiments. Specifically, in conjunction with the ICML 2020 conference, we performed three experiments that target: re ..read more
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HOMER: Provable Exploration in Reinforcement Learning
John Langford’s Machine Learning Theory
by DipendraMisra
4y ago
Last week at ICML 2020, Mikael Henaff, Akshay Krishnamurthy, John Langford and I had a paper on a new reinforcement learning (RL) algorithm that solves three key problems in RL: (i) global exploration, (ii) decoding latent dynamics, and (iii) optimizing a given reward function. Our ICML poster is here. The paper is a bit mathematically heavy in nature so this post is an attempt to distill the key findings. We will also be following up soon with a new codebase release (more on it later). Rich-observation RL landscape Consider the combination lock problem shown below. Th ..read more
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Critical issues in digital contract tracing
John Langford’s Machine Learning Theory
by John Langford
4y ago
I spent the last month becoming a connoisseur of digital contact tracing approaches since this seems like something where I might be able to help. Many other people have been thinking along similar lines (great), but I also see several misconceptions that even smart and deeply involved people are making. For the following a key distinction to understand is between proximity and location approaches. In proximity approaches (such as DP3T, TCN, MIT PACT(*), Apple or one of the UW PACT(*) protocols which I am involved in) smartphones use Bluetooth low energy and possibly ultrasonics to discover ot ..read more
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Updates for the new decade
John Langford’s Machine Learning Theory
by jl
4y ago
This blog has been quiet for the last year. I have quite a bit to write about but found myself often out of time between work at Microsoft, ICML duties, and family life. Nevertheless, I expect to get back to more substantive discussions as I adjust to the new load. In the meantime, I’ve updated the site in various ways: SSL now works, and mail for people registering new accounts should work again. I also setup a twitter account as I’ve often had things left unsaid. I’m not a fan of blog-by-twitter (which seems artificially disjointed), so I expect to use twitter for shorter things and hunch ..read more
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