DAVID P. HELMBOLD
Professor, Computer Science
Computer Science Department
E2 Building room 345B
University of California
Santa Cruz, CA 95064
David Helmbold received PhD in Computer Science from Stanford University
in 1987 where
he worked on parallel algorithms and the debugging of parallel programs.
He joined the Computer Science Department at UC Santa Cruz where
he has been a faculty member for over 25 years.
At Santa Cruz his research interests shifted to theoretical Machine Learning,
with an emphasis on boosting methods and on-line learning algorithms.
He is a long-standing member of the computational learning theory community,
having hosted the COLT conference and served on the COLT steering committee.
David Helmbold's current research centers around machine learning
and computational learning theory. In addition to theoretical work,
he has applied learning algorithms to practical problems such as
determining when to spin down a disk drive in a portable computer
to save power.
David P. Helmbold and Philip M. Long
New Bounds for Learning Intervals with
Implications for Semi-Supervised Learning
Although labeled training data can be expensive, often there is
a source of cheap unlabeled examples.
This paper shows that access to unlabeled examples
provably helps even when the distribution over examples is adversarial and
the classes are not well-separated.
Our results include the first progress in almost 20 years on
determining the exact number of examples required to obtain a
given error guarantee when learning intervals.
James Pettit and David P. Helmbold
Evolutionary Learning of Policies for MCTS Simulations
Monte-Carlo Tree Search (MCTS) is a recent technique for evaluating moves
and positions in abstract games like Go and Hex.
MCTS dynamically grows a partial search-tree while performing a
large number of artificial play-outs to evaluate moves.
These artificial play-outs first traverse to a leaf in
the partial search tree and then use
a simple (usually randomized) policy to complete the game and obtain
feedback about the goodness of the leaf.
Surprisingly, using better or stronger policies to complete the playouts
often causes the MCTS system to perform worse.
In the above paper we present an evolutionary approach for discovering
simple randomized strategies that improve the quality of the
overall MCTS system.
David P. Helmbold and Philip M. Long
On the Necessity of Irrelevant Variables
in ICML 2011 and the long version
in JMLR 13 (2012).
When creating a classifier, a natural inclination is to only use variables
that are obviously relevant since irrelevant variables typically decrease
the accuracy of a classifier.
Some researchers use the goodness of a predictive model as support for
the relevance of the used variables.
The above papers show that the harm done by irrelevant variables can be much
less than the benefit from relevant variables.
Therefore it can be advantageous to continue adding variables to the model,
even as their prospects for being relevant fade away.
Manfred K. Warmuth, Wouter M. Koolen, and David P. Helmbold
Combining Initial Segments of Lists in ALT 2011.
Damian Eads, Edward Rosten, and David Helmbold.
Learning object location predictors with boosting and
grammer-guided feature extraction in Proceedings of the British
Machine Vision Conference (BMVC), September 2009.
David Helmbold and Aleatha Parker-Wood.
All-moves-as-first heuristics in monte-carlo go in Hamid R.
Arabnia, David de la Fuente, and Jose A. Olivas, editors, Proceedings of
the 2009 International Conference on Artificial Intelligence, pages
605--610. WorldComp, July 2009.
David Helmbold and Manfred K. Warmuth.
Learning Permutations with exponential weights
in Journal of Machine Learning Research, vol. 10, pages 1687--1718, July 2009.
(The conference version appeared in COLT 2007.)
Additional Selected Publications
Here is a list containing many of my other publications. It includes
work in the areas of Object Detection and Terrain classification, Computer Go, Boosting, On-line learning,
Other learning theory, and Learning Applications, as well as some older work on Debugging of Parallel Programs and
UCSC hosted a machine learning summer school at Santa Cruz in 2012.
Last modified March 12, 2014 (05:11:39 PM).