(c.1991)
Andrew Kachites McCallum
This home page is old and out of date.
As of 1996 I have moved to Carnegie Mellon University.
My new home page is at
http://www.cs.cmu.edu/~mccallum.
Some Publications
- Mark Craven, Dayne Freitag, Andrew McCallum, Tom Mitchell, Kamal
Nigam, Choon Quek,
Learning to Extract Symbolic Knowledge from the World Wide Web,
Internal Report, School of Computer Science, CMU, January 1997.
- McCallum, R. Andrew,
Efficient Exploration in Reinforcement Learning with Hidden State,
AAAI Fall Symposium on "Model-directed Autonomous Systems", 1997.
- McCallum, R. Andrew,
Learning to Use Selective Attention and Short-Term Memory in Sequential
Tasks,
in From Animals to Animats,
Fourth International Conference on Simulation of Adaptive Behavior,
(SAB'96). Cape Cod, Massachusetts. September, 1996.
- McCallum, Andrew K.,
Reinforcement Learning with Selective Perception and Hidden State,
PhD. thesis. December, 1995.
- McCallum, R. Andrew,
Instance-Based Utile Distinctions for Reinforcement Learning,
The Proceedings of the Twelfth International Machine Learning
Conference (ML'95), Lake Tahoe, CA, 1995.
- McCallum, R. Andrew,
Hidden State and Reinforcement Learning with Instance-Based State
Identification,
IEEE Transations on Systems, Man and Cybernetics
(Special issue on Robot Learning), to appear, 1996.
- McCallum, R. Andrew,
Utile Suffix Memory for Reinforcement Learning with Hidden State,
URCS Tech Report 549, under revision, 1994.
- McCallum, R. Andrew,
Instance-Based State Identification for Reinforcement Learning,
Advances in Neural Information Processing Systems (NIPS 7), 1995.
- McCallum, R. Andrew,
First Results with Instance-Based State Identification for Reinforcement Learning,
URCS Tech Report 502, 1994.
- McCallum, R. Andrew,
Reduced Training Time for Reinforcement Learning with Hidden State,
The Proceedings of the Eleventh International Machine Learning
Workshop (Robot Learning), New Brunswick, NJ, 1994.
- McCallum, R. Andrew,
Short-Term Memory in Visual Routines for `Off-Road Car Chasing',
Working Notes of AAAI Spring Symposium Series, "Toward Physical
Interaction and Manipulation", Stanford University, March 21-23, 1994.
- McCallum, R. Andrew,
Overcoming Incomplete Perception with Utile Distinction Memory,
The Proceedings of the Tenth International Machine Learning
Conference (ML'93), Amherst, MA, 1993.
- McCallum, R. Andrew,
Learning with Incomplete Selective Perception,
Thesis Proposal, URCS Tech Report 453, 1993.
- Garrett, Scott, Bianchini, Kontothanassis, McCallum, Thomas, Wisniewski
and Luk,
Linking Shared Segments,
Winter USENIX, San Diego, CA, 1993.
- McCallum, R. Andrew,
First Results with Utile Distinction Memory for Reinforcement Learning,
URCS Tech Report 446, 1992.
- McCallum, R. Andrew,
Using Transitional Proximity for Faster Reinforcement Learning,
The Proceedings of the Ninth International Machine Learning
Conference (ML'92), Aberdeen, Scotland, 1992.
- Garrett, Bianchini, Kontothanassis, McCallum, Thomas, Wisniewski
and Scott,
Dynamic Sharing and Backward Compatibility on 64-Bit Machines,
URCS Tech Report 418, 1992.
- McCallum, R. Andrew, and Spackman, Kent A.,
Using Genetic Algorithms to Learn Disjunctive Rules from Examples,
The Proceedings of the Seventh International Machine Learning
Conference (ML'90), Austin, Texas, 1990.
To contact me:
email
mccallum@cs.rochester.edu
phone: (716) 275-2527 (office)
(716) 275-1372 (robot/VR lab)
(716) 275-5377 (software lab)
(716) 275-1083 (speech lab)
USMail: Department of Computer Science
University of Rochester
Rochester, NY 14627
Some Hacking
- I am cheif maintainer of the GNUstep project,
the Free Software Foundation's effort to implement NeXT Computer Inc.'s
OpenStep standard.
- I hack on
GNU Objective C Class Library.
- I also hack on libguileobjc,
an interface between GNU Guile and Objective C.
- I also hack on persia, a toolkit for building virtual
reality environments on our SGI Onyx RealityEngine2. The kit is based
on SGI's Performer library and ELK Scheme.
- I also hack on rlkit, a software library that makes it
easy to test various reinforcement learning algorithms in different
environments with different sensory-motor systems. It's implemented
in Objective-C and Guile.
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