Programming Languages for Machine Learning Implementations – Machine Learning (Theory)
Machine learning algorithms have a much better chance of being widely adopted if they are implemented in some easy-to-use code. There are several important concerns associated with machine learning which stress programming languages on the ease-of-use vs. speed frontier. Speed The rate at which data sources are growing seems to be outstripping the rate at which computational power is growing, so i
Machine Learning (Theory) – Machine learning and learning theory research
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-c
The Efficient Robust Conditional Probability Estimation Problem – Machine Learning (Theory)
The Efficient Robust Conditional Probability Estimation Problem I’m offering a reward of $1000 for a solution to this problem. This joins the cross validation problem which I’m offering a $500 reward for. I believe both of these problems are hard but plausibly solvable, and plausibly with a solution of substantial practical value. While it’s unlikely these rewards are worth your time on an hourly
Netflix finishes (and starts) – Machine Learning (Theory)
I attended the Netflix prize ceremony this morning. The press conference part is covered fine elsewhere, with the basic outcome being that BellKor’s Pragmatic Chaos won over The Ensemble by 15-20 minutes, because they were tied in performance on the ultimate holdout set. I’m sure the individual participants will have many chances to speak about the solution. One of these is Bell at the NYAS ML sym
Another 10-year paper in Machine Learning – Machine Learning (Theory)
When I was thinking about the best “10 year paper” for ICML, I also took a look at a few other conferences. Here is one from 10 years ago that interested me: David McAllester PAC-Bayesian Model Averaging, COLT 1999. 2001 Journal Draft. Prior to this paper, the only mechanism known for controlling or estimating the necessary sample complexity for learning over continuously parameterized predictors
Interesting papers at KDD – Machine Learning (Theory)
I attended KDD this year. The conference has always had a strong grounding in what works based on the KDDcup, but it has developed a halo of workshops on various subjects. It seems that KDD has become a place where the economy meets machine learning in a stronger sense than many other conferences. There were several papers that other people might like to take a look at. Yehuda Koren Collaborative
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KDD 2010 – Machine Learning (Theory)
Deadline Season, 2010 – Machine Learning (Theory)
Top graduates this season – Machine Learning (Theory)
Interesting things at NIPS 2009 – Machine Learning (Theory)
NIPS workshops – Machine Learning (Theory)