Optimizing Machine Learning Programs – Machine Learning (Theory)
Machine learning is often computationally bounded which implies that the ability to write fast code becomes important if you ever want to implement a machine learning algorithm. Basic tactical optimizations are covered well elsewhere, but I haven’t seen a reasonable guide to higher level optimizations, which are the most important in my experience. Here are some of the higher level optimizations I
The Ideal Large Scale Learning Class – Machine Learning (Theory)
At NIPS, Andrew Ng asked me what should be in a large scale learning class. After some discussion with him and Nando and mulling it over a bit, these are the topics that I think should be covered. There are many different kinds of scaling. Scaling in examples This is the most basic kind of scaling. Online Gradient Descent This is an old algorithm—I’m not sure if anyone can be credited with it in p
What does Watson mean? – Machine Learning (Theory)
Watson convincingly beat the best champion Jeopardy! players. The apparent significance of this varies hugely, depending on your background knowledge about the related machine learning, NLP, and search technology. For a random person, this might seem evidence of serious machine intelligence, while for people working on the system itself, it probably seems like a reasonably good assemblage of exist
Vowpal Wabbit (Fast Learning)
This is a project started at Yahoo! Research and continuing at Microsoft Research to design a fast, scalable, useful learning algorithm. VW is the essence of speed in machine learning, able to learn from terafeature datasets with ease. Via parallel learning, it can exceed the throughput of any single machine network interface when doing linear learning, a first amongst learning algorithms. We prim
NIPS 2010 – Machine Learning (Theory)
I enjoyed attending NIPS this year, with several things interesting me. For the conference itself: Peter Welinder, Steve Branson, Serge Belongie, and Pietro Perona, The Multidimensional Wisdom of Crowds. This paper is about using mechanical turk to get label information, with results superior to a majority vote approach. David McAllester, Tamir Hazan, and Joseph Keshet Direct Loss Minimization for
Machine Learning (Theory) ? How is Compressed Sensing going to change Machine Learning ?
How is Compressed Sensing going to change Machine Learning ? Compressed Sensing (CS) is a new framework developed by Emmanuel Candes, Terry Tao and David Donoho. To summarize, if you acquire a signal in some basis that is incoherent with the basis in which you know the signal to be sparse in, it is very likely you will be able to reconstruct the signal from these incoherent projections. Terry Tao,
Interesting Papers at SODA 2009 – Machine Learning (Theory)
Several talks seem potentially interesting to ML folks at this year’s SODA. Maria-Florina Balcan, Avrim Blum, and Anupam Gupta, Approximate Clustering without the Approximation. This paper gives reasonable algorithms with provable approximation guarantees for k-median and other notions of clustering. It’s conceptually interesting, because it’s the second example I’ve seen where NP hardness is subv
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Vowpal Wabbit, version 5.0, and the second heresy – Machine Learning (Theory)