We strongly believe in open source and giving to our community. We work directly with researchers in academia and seek out new perspectives with our intern and fellowship programs. We generalize our solutions and release them to the world as open source projects. We host discussions and publish our results.


Internet Commerce and Software Agents: Cases, Technologies and Opportunities. Rahman, S.M. and Bignall, R. eds. Idea Group. 2001

Distributed Recommender Systems: New Opportunities for Internet Commerce

Badrul Sarwar, Joseph Konstan, John Riedl, Badrul Sarwar, Joseph Konstan, John Riedl

No Information

ICML Workshop on Machine Learning of Spatial Knowledge, July 2, 2000, Stanford University

Learning and Planning with Hierarchical Stochastic Models for Robot Navigation

Georgios Theocharous, Khashayar Rohanimanesh, Sridhar Mahadevan, Georgios Theocharous, Khashayar Rohanimanesh, Sridhar Mahadevan

We propose and investigate a method for hierarchical learning and planning in partially observable environments using the framework of Hierarchical Hidden Markov Models (HHMMs).

Our main goal is to use hierarchical modeling as a basis for exploring more efficient learning and planning algorithms. As a case study we focus on indoor robot navigation problem and will show how this framework can be used to learn a hierarchy of maps of the environment at different levels of spatial abstraction.

We train different families of HHMMs for a real corridor environment and compare them with the standard HMM representation of the same environment. We find significant bene ts to using HHMMs in terms of the fit of the model to the training data, localization of the robot, and the ability to infer the structure of the environment. We also introduce the idea of model reuse that can be used to combine already learned models into a larger model

International Conference on Knowledge Discovery and Data Mining (KDD-2000), August 2000

Alpha seeding for support vector machines

Dennis DeCoste, K. Wagstaff

A key practical obstacle in applying support vector machines to many large-scale data mining tasks is that SVM’s generally scale quadratically (or worse) in the number of examples or support vectors.

This complexity is further compounded when a specific SVM training is but one of many, such as in Leave-One-Out-Cross-Validation (LOOCV) for determining optimal SVM kernel parameters or as in wrapper-based feature selection. In this paper we explore new techniques for reducing the amortized cost of each such SVM training, by seeding successive SVM trainings with the results of previous similar trainings.

Computer Vision and Pattern Recognition (CVPR-2000), June 2000

Distortion-invariant recognition via jittered queries

Dennis DeCoste, M.C. Burl

This paper presents a new approach for achieving distortion-invariant recognition and classification. A test example to be classified is viewed as a query intended to find similar examples in the training set (or class models derived from the training set). The key idea is that instead of querying with a single pattern, we construct a more robust query, based on the family of patterns formed by distorting the test example.

Although query execution is slower than if the invariances were successfully pre-compiled during training, there are significant advantages in several contexts:

(i) providing invariances in memory-based learning,

(ii) in model selection, where reducing training time at the expense of test time is a desirable trade-off, and

(iii) in enabling robust, ad hoc searches based on a single example. Preliminary tests for memory-based learning on the NIST handwritten digit database with a limited set of shearing and translation distortions produced an error rate of 1.35%.

April 14, 2000, Breckenridge, Colorado

Hierarchical Map Learning for Robot Navigation, AIPS Workshop on Decision-Theoretic Planning

Charles Sutton, Khashayar Rohanimanesh, Andrew McCallum, Charles Sutton, Khashayar Rohanimanesh, Andrew McCallum

No Information

Journal of Computer Mediated Communication, JCMC 4 (4), 1999

Distributed Research Teams: Meeting Asynchronously in Virtual Space

Lai Adams, Lori Toomey, Elizabeth Churchill
As computer networks improve, more social and work interactions are carried out “virtually” by geographically separated group members. In this paper we discuss the design of a tool, PAVE, to support remote work interactions among colleagues in different time zones.
PAVE extends a 2D graphical MOO and supports synchronous and asynchronous interactions.
PAVE logs and indexes activities in the space. This capture facility enables playback and augmentation of meeting interactions by non-collocated group members. Thus, members can participate asynchronously in meetings they could not attend in real time, not just review them.
AAAI 1999 Conference. 1999

Combining Collaborative Filtering with Personal Agents for Better Recommendations

Nathan Good, Ben Schafer, Joseph Konstan, Al Borchers, Badrul Sarwar, Jon Herlocker, John Riedl, Nathan Good, Ben Schafer, Joseph Konstan, Al Borchers, Badrul Sarwar, Jon Herlocker, John Riedl

No Information

Quarterly Journal of Experimental Psychology, 51A(1), 1-17, 1998

Selection through rejection: Reconsidering the invariant learning paradigm

Elizabeth Churchill, David Gilmore

Two experiments are reported that investigate the nature of selections in the McGeorge and Burton (1990) invariant learning paradigm. McGeorge and Burton suggest that subjects implicitly acquire abstract knowledge of an invariant feature (usually the presence of the digit “3”) in a set of 30 stimuli.

McGeorge and Burton's analysis has recently been challenged by Cock, Berry, and Gaffan (1994) and by Wright and Burton (1995). In this paper, we demonstrate that performance is based on knowledge of other aspects of the learning set besides the invariant digit, but that this knowledge is still implicit.

Altering the nature of the learning stimuli to highlight these co-varying features enhances the effects and increases the reporting of explicit knowledge. Our results indicate that performance within this paradigm is more easily characterized as rejection of salient negatives than selection of positive instances, but that salience is not based simply on similarity.