Date 
Readings 

2009, August 25 
 J. Peters, S. Vijayakumar, and S. Schaal
(2003). Reinforcement Learning
for Humanoid Robotics. ICHR.

Hey, it's about, um, Reinforcement Learning for Humanoid Robotics.



2009, August 18 
 J. Zico Kolter and Andrew Y. Ng (2009). NearBayesian Exploration in Polynomial Time. ICML.

A regularization framework for LSTD. Mmmmm.



2009, August 17 
We return after a yearlong break
The Theme is Reinforcement Learning





2008, March 26 
 Beygelzimer, Kakade, Langford (2006). Cover Trees for Nearest Neighbor. ICML.

...back to ICML
The interested reader may also look at: http://hunch.net/~jl/projects/cover_tree/cover_tree.html



2008, March 5 
 Moghaddam, Weiss, Avidan (2006). Spectral Bounds for Sparse PCA: Exact and
Greedy Bounds. NIPS

...back to NIPS.



2008, February 27 
 Zhou, Hastie and Tibshirani (2006^{*}). Sparse Principal Component
Analysis. JCGS 15(2).

Continuing our series of papers that look longer than they are.
^{*}Year of publication... actually written in
2004, apparently.



2008, February 20 
 Tibshirani (2003^{*}). A Simple Explanation of the Lasso and
Least Angle Regression. Stolen from: http://wwwstat.stanford.edu/~tibs/lasso/simple.html

Zou and Hastie (2005). Regularization and Variable Selection Via
the Elastic Net. J.R. Statist Soc. B.

One very short explanation of Lasso and then some elastic nets.
^{*}Well, according to the way back machine, at any rate.



2008, February 13 

Tibshirani (1996). Regression Shrinkage and Selection via the
Lasso. Journal of the Royal Statistical Society

It's 23 pages, but they're small pages.



2008, February 6 

Bishop (2006). Chapter 6: Kernel Methods. Pattern Recognition
and Machine Learning




2008, January 30 

Weston, Mukherjee, Chapelle, Pontil, Poggio, Vapnik (2008). Feature Selection for SVMs. NIPS.




2008, January 23 

Siddiqi, Boots, Gordon (2008). A Constraint Generation Approach
to Learning Stable Linear Dynamical Systems. NIPS.




2008, January 16 

Gashler, Ventura, and Martinez (2008). Iterative Nonlinear
Dimensionality Reduction by Manifold Sculpting. NIPS.




2008, January 9 

Cayton and Dasgupta (2008). A Learning Framework for Nearest
Neighbor Search. NIPS.

Our first paper of the new term. This begins our series on new and
popular algorithms / frameworks explored at NIPS 2007.





2007, October 17 

Minnen, Isbell, Essa and Starner (2007). Discovering Multivariate Motifs
using Subsequence Density Estimation and Greedy Mixture Learning. AAAI.

More on activity discovery. We may spend some time on [2] from last
week as well.



2007, October 10 

Keogh, Lin and Truppel (2003). Clustering of Time Series
Subsequences is Meaningless: Implications for Previous and Future
Research. ICDM.

Chiu, Keogh and Lonardi (2003). Probabilistic Discovery of Time Series
Motifs. SIGKDD.

Time to switch topics: activity discovery and recognition. These are
good starter papers (we read them about three years ago) that get us
started into one particular line of research.



2007, October 3 
 Marthi, Russell, Latham, Guestrin (2005). Concurrent Hierarchical Reinforcement
Learning. IJCAI.


Continuing this thread (as it were)....



2007, September 26 
 Bhat, Isbell, Mateas (2006). On the Difficulty of Modular Reinforcement Learning
for RealWorld Partial Programming. AAAI.


Short followup on last week's paper. Bring [1] and [2] from last week
as well.



2007, September 19 
 S. Russell and A. Zimdars (2003). QDecomposition for Reinforcement
Learning Agents. ICML03.
2003.
 N. Sprague and D. Ballard (2003). MultipleGoal
Reinforcement Learning with Modular Sarsa(0). ICML.


Oldies but goodies, moving in the multiagent RL problems space.
[1] is our paper, but [2] give s a very nice overview and is much shorter.



2007, September 12 
 Zhang, Aberdeen, Vishwanathan (2007). Conditional
Random Fields for
MultiAgent
Reinforcement Learning.
ICML.
Continuing the theme of papers from this year's ICML.



2006, September 5 
no ISRG



2007, August 29 
 Salakhutdinov, Mnih, and Hinton (2007). Restricted
Boltzmann Machines for
Collaborative Filtering.
ICML.
Continuing the theme of papers from this year's ICML.



2007, August 22 
 Zhang, Xue, Sun, Guo, and Lu (2007). Optimal
Dimensionality of Metric
Space for Classification.
ICML.
It is the beginning of a new term! We begin with papers from this
year's ICML.





2007, April 11 
 Choudhry and Basu (2004). Modeling
Conversational Dynamics
as a MixedMemory Markov Process.
NIPS.
You know what we need? Machine learning papers applied to human activities.



2007, April 4 
 Raj, Smaragdis, Shashanka (2006). Latent Dirichlet Decomposition for Single Channel Speaker Spearation.
ICASSP.
You know what we need? Latent Dirichlet decompositions



2007, March 28 
 Wang, Pentney, Papescu, Choudhury, and Philipose (2007). Common Sense Based Joint Training.
IJCAI.
You know what we need? Machine learning papers applied to human activities.



2007, March 21 
Spring Break



2007, March 14 
 Shoham, Powers, and Grenager (2003).MultiAgent
Reinforcement Learning: A
Critical Survey.
Stanford Technical Report.
We're back!



2007, February 14 
 Thomaz and Breazeal (2006).Reinforcement Learning with Human Teachers: Evidence of feedback and guidance with implications for learning performance. AAAI.
 Thomaz, Hoffman and Breazeal (2006).Reinforcement Learning with Human Teachers: Understanding how people want to teach robots.. ROMAN.
Machine Learning. Humans. Robots. Sheep. Wolves.



2007, Feburary 7 
too many paper deadlines this week



2007, January 31 
 Magerko and Laird (2003).Building
an Interactive Drama Architecture. TIDSE.
Hm.



2007, January 24 
 Ho and Pepyne (2001). Simple Explanation of
the No Free Lunch Theorem of Optimization. IEEE D&C.
 Wolpert and Macready (1997). No Free Lunch Theorems for
Optimization. IEEE Transactions.
We've decided that [2] is worth looking at.



2007, January 17 
 Ho and Pepyne (2001). Simple Explanation of
the No Free Lunch Theorem of Optimization. IEEE D&C.
 Wolpert and Macready (1997). No Free Lunch Theorems for
Optimization. IEEE Transactions.
Well, you wanted it and we've got it: No Free Lunch. [1] is a shorter
explanation of [2]. We're reading [1], but [2] may have be worth
looking over.



2007, January 10 
 Kearns, Mansour, Ng, Ron (1995). An Experimental and Theoretical
Comparison of Model Selection
Methods. COLT.
 Kearns, Mansour, Ng, Ron (1997). An Experimental and Theoretical
Comparison of Model Selection
Methods. Machine Learning 27.
A nice way to start off the term. We are reading [1]; however, the
extremely interested reader may want to read [2], the longer (now with
over 33% more notation!) journal article.




2006, November 29 
 James, Singh and Littman (2004). Planning with Predictive State
Representations. ICMLA.
So... can we plan with these things?



2006, November 15 
 James and Singh (2004). Learning and Discovery of Predictive
State Representations in Dynamical Systems with Reset. ICML.
So... can we learn the things?



2006, November 8 
 Singh, James and Rudary (2004). Predictive State Representations: A
New Theory for Modeling Dynamical Systems . UAI.
So we begin a few weeks on predictive state representations. Plus: My voice is back!



2006, November 1 
 Balkcom and Mason (2004). Introducing robotic origami folding. ICRA.
This should get us ready for this week's RIM.



2006, October 18 
 D. Roberts, M. Nelson, C. Isbell, M. Mateas, M. Littman (2006). Targeting Specific Distributions of
Trajectories in MDPs. AAMAS.



2006, October 11 
 White, Holloway (2006). Resolvability for Imprecise Multiattribute Alternative Selection.



2006, October 4 
no ISRG



2006, September 27 
 Chajewska, Koller, Parr (2000).
Making Rational Decisions Using
Adaptive Utility Elicitation. AAAI.



2006, September 20 
 Isbell, Kearns, Singh, Shelton, Stone, and Kormann (2006). Cobot in LambdaMOO: An Adaptive Social Statistics
Agent. Autonmous Agents and MultiAgent Systems.



2006, September 13 
 Aha, Molineaux, and Ponsen (2005). Learning to Win: Casebased plan selection
in a realtime strategy game. CCBR.
Finish the Aha paper.



2006, September 6 
 Mateas and Stern (2003). Facade: An Experiment in Building a
FullyRealized Interactive Drama. Game Developer's Conference.
 Aha, Molineaux, and Ponsen (2005). Learning to Win: Casebased plan selection
in a realtime strategy game. CCBR.
We will be reading two papers in the space of interesting games with
AI. Read the first paper and the first two sections of the
second. Next week we will finish the second paper and look at another
AI/ML approach to building players of games.



2006, August 30 
 Blekas, Fotiadis, and Likas (2003). Greedy mixture learning for multiple motif discovery in biological sequences. Bioinformatics.



2006, August 23 
plans for the term





2006, April 17 
qualifer exams today, so no ISRG



2006, April 10 
 E. Altendorf, A. Restificar, T. Dietterich (2005). Learning from Sparse Data by
Exploiting Monotonicity Constraints. UAI.


ISRG will be held in TSRB 133 Monday at 1pm instead of in the usual
place. Angelo Restificar
will be giving a talk. The paper is background reading.



2006, April 3 
 D. Jensen and J. Neville (2002). Linkage and Autocorrelation
Cause Feature Selection Bias in Relational Learning. ICML.
 J. Neville, D. Jensen, L. Friedland and M. Hay (2003). Learning Relational
Probability Trees. SIGKDD.





2006, March 27 
 J. Neville and D. Jensen (2004). Dependency Networks for
Relational Data. ICDM.





2006, March 20 
Spring Break



2006, March 6 
 C. Yu, D. Ballard and R. Aslin (2003). The Role of Embodied Intention in Early Lexical Acquisition. Gog Sci 2003.





2006, February 27 
 Cohen, Oates, Beal, Adams (2002). Contentful Mental States for Robot
Baby. AAAI 2002.
 Sebastiani, Ramoni and Cohen (1999). Bayesian Clustering of Sensory
Inputs by Dyanmics. UMASS Tech Report.





2006, February 20 
we skip a week while everyone works on AAAI papers



2006, February 13 
 Nelson, Roberts, Isbell, and Mateas (2006). Reinforcement Learning for
Declarative OptimizationBased Drama Management. AAMAS 2006.
 Nelson and Mateas (2005). Searchbased drama management in the
interactive fiction Anchorhead. AIIDE05.


Intelligent Entertainment. [1] is the main paper, [2] may help you
with some of the background.



2006, February 6 
 W. Maass, R. A. Legenstein, and N. Bertschinger (2005). Methods for estimating the
computational power and generalization capability of neural
microcircuits. NIPS.


back to neural stuff



2006, January 30 
 R. O'Donnell (2005). Learning monotone functions from
random examples in polynomial time.


A learning theory paper! Followed by a talk at 2pm in the Learning
Seminar at MiRC 102.



2006, January 23 
 R. Rao and T. Sejnowski (2001). SpikeTimingDependent Hebbian
Plasticity as Temporal Difference Learning. Neural Computation.
 A. Shon, R. Rao and T. Sejnowski (2004). Motion Detection and Prediction
through SpikeTiming Dependent Plasticity.


Our first in a series of neuralinspired learning. The first paper
demonstrates that a model of neural computation simulates temporal
difference learning while the second is an application of those sorts
of ideas to a the problem of motion detection and prediction.





2005, December 5 
 A. von Hessling, A. Goel (2005). Abstracting Reusable Cases from
Reinforcement Learning. ICCBR05.


Remember back in late October when we were to have two talks on
structural learning but we ended up with only one? Today is the other
one. The paper above is background reading.



2005, November 28 
No paper today; however, there is a talk that should
complement our recent discussions at 2pm in TSRB 132:
The Holy Grail of MMOG Design: Supporting Players to Perform





2005, November 21 
 S. Russell and A. Zimdars (2003). QDecomposition for Reinforcement
Learning Agents. ICML03.
2003.
 N. Sprague and D. Ballard (2003). MultipleGoal
Reinforcement Learning with Modular Sarsa(0). ICML.


[1] is our paper, but [2] give s a very nice overview and is much shorter.



2005, November 14 
 Dietterich, T (2000). An Overview of
MAXQ Hierarchical Reinforcement. SARA.


MAXQ!



2005, November 7 
 D. Andre and S. Russell (2002). State Abstraction for Programmable
Reinforcement Learning Agents.. AAAI.


We continue looking at adaptive programming languages and the issues that arise.



2005, October 31 
 P. Norvig and D. Cohn. Adaptive Software.
 D. Andre and S. Russell (2001). Programmable Reinforcement Learning
Agents. NIPS.


We begin our series on adaptive or partial programming. The first
paper provides an overview and background and the second is the first
a few papers from this group that deals with issues that arise when
you want to do this kind of learning inside of programming.



2005, October 24 
 A. von Hessling, A. Goel (2005). Abstracting Reusable Cases from
Reinforcement Learning. ICCBR05.
 J. Jones, A. Goel (2005). Knowledge Organization and
Structural Credit Assignment. Workshop at IJCAI.


In a change of pace, we'll have two presentations on research related
to some of the structural learning papers we've been reading over the
last few weeks. The papers above are background reading.



2005, October 17 
Fall Break



2005, October 10 
 K. Driessens, J. Ramon, H. Blockeel (2001). Speeding up Relational Reinforcement
Learning Through the User of an Incremental First Order Decision Tree
Learner. EMCL.





2005, October 3 
Free Day



2005, September 26 
 C. Guestrin, D. Koller, C. Gerahart, N. Kanodia (2003). Generalizing Plans to New
Environments in Relational MDPs . IJCAI.





2005, September 19 
 K. Nigam, J. Lafferty, A. McCallum (1999). Using Maximum Entropy for Text
Classification. Workshop at IJCAI.
 P. Penfield (2003). Principle of
Maximum Entropy: Simple Form. Class Notes MIT 6.050.
 P. Penfield (2003). Principle of
Maximum Entropy. Class Notes MIT 6.050.


Hm. Yes, well. Let's try that again. [1] is the paper, [2] & [3] are
background that you might find helpful.



2005, September 12 
 T. Jaakkola, M. Meila, & T. Jabara (2000). Maximum Entropy Discrimination. NIPS
1999.


Because I want talk about Jaakkola.



2005, September 5 
Labor Day



2005, August 29 
 C. Bishop and M. Tipping (2000). Variational Relevance Vector
Machine. UAI 2000.


It's next time.



2005, August 22 
 M. Tipping (2000). The Relevance
Vector Machine. NIPS 1999.


A probabilistic / bayesian treatment of SVMs. More next time.



2005, April 25 
 E. Kiciman and A. Fox (to appear). Detecting ApplicationLevel
Failures in Componentbased Internet Services.
IEEE Transactions on Neural Networks:
Special Issue on Adaptive Systems, Spring 2005.



2005, April 18 
 C. Park and H. Park (to appear). Nonlinear discriminant analysis using kernel
functions and the generalized singular value decomposition.
SIAM Journal on Matrix Analysis and Applications.



2005, April 11 
 A. Apostolico and G. Bejerano (2000). Optimal Amnesic Probabilistic Automata
Journal of Computational Biology, 7(3/4).



2005, April 4 
Quals are today



2005, March 28 
Alex Gray will be visiting us in class



2005, March 21 
Spring Break



2005, March 14 
 A. Gray and A Moore (2004). NBody Problems in Statistical Learning
NIPS 2004.



2005, March 7 
 A Josang and Pope (2005). Semantic Constraints for Trust
Transitivity. APCCM 2005.


Uhoh. An application. Must mean it's ending.



2005, February 28 
 A Josang (2001). A Logic for Uncertain Probabilities.
International Journal of Uncertainty, Fuzziness and KnowledgeBased
Systems 9(3).


It never ends....



2005, February 21 
 P. Wang (2001). Confidence as HigherOrder
Uncertainty 2nd International Symposium on Imprecise Probabilitie
and Their Applictions.


The next salvo.



2005, February 14 
 P. Wang (2004). The Limitation of Bayesianism
Artificial Intelligence 158(1).


Holy war!



2005, February 7 
 Andrew Ng and H Jin Kim (2005). Stable Adaptive Control with Online
Learning NIPS 04.


So here's an idea for adapting to changing circumstances without doing
something too dumb along the way.



2005, January 31 
 M. Kaess, R. Zboinski, and F. Dellaert (2004). Multiview Reconstruction of Piecewise
Smooth Subdivision Curves with a Variable Number of Control Points
ECCV 04.
 Green, with discussion by Godsill and Heikkinen (2003). Transdimensional Markov chain Monte
Carlo chapter for the book on Highly Structured Stochastic
Systems.


We are reading [1]. Here's your chance to mock Frank in a public
forum provided you can understand the math. [2] is background you
might find useful to refer to for that purpose, but Frank does warn
that it's quite "advanced". So far, I'd say he's up one point on the
mocking.



2005, January 24 
 Rish (2001). An empirical study of the naive
Bayes classifier IJCAI01 workshop on "Empirical Methods in
AI". Also appeared as IBM Technical Report RC22230.
 Pedro Domingos and Michael Pazzani (1997). On the Optimality of the Simple Bayesian
Classifier under ZeroOne Loss Machine Learning, 29, 103130.


Both [1] and [2] try to characterize the conditions under which
"simple" Bayesian classifiers are optimal, other than the obvious
one. Come prepared to discuss their findings and with an opinion on
their analyses and techniques.



2004, January 17 
MLK Holiday



2005, January 10 
 N. Hill, T. Lal, K Bierig, N Birbaumer and B. Scholkopf
(2005). An Auditory Paradigm for
BrainComputer Interfaces. NIPS 17.


Our first meeting is 1pm this Monday at TSRB 223, where we
will be all term





2004, November 29 
And so the term ends. See you in January. Note: there will be an
actual reading for the first week of the term, so check back here the
week before.



2004, November 22 
 P. Atkar, D. Conner, A. Greenfield, H. Choset, and A. Rizzi (2004). Uniform Coverage of Simple Surfaces
Embedded in R^{3} for AutoBody Painting. Sixth Workshop
on the Algorithmic Foundations of Robotics, Utrecht/Zeist, The
Netherlands, 2004.


You know what we don't talk about enough? Problems in Robotics.



2004, November 15 
 J Fogarty, S. Hudson, and J. Lai (2004). Examining the Robustness of
SensorBased Statistical Models of Human
Interruptibility. Proceedings of the ACM Conference on Human
Factors in Computing Systems, pp. 2072214.
 S Hudson, et al (2003). Predicting Human Interruptibility
with Sensors: A Wizard of Oz Feasibility Study. Proceedings of the
ACM Conference on Human Factors in Computing Systems, pp. 257264.


Here we examine a different subfield of CS's contribution to a related
field of activity discovery by looking at an HCI/ML approach to
modeling interruptibility. [1] is about an actual system while [2] is
a "Wizard of Oz" pilot study that inspired it.



2004, November 8 
 M. Holmes and C. Isbell (2005). Schema Learning:
ExperienceBased Construction of Predictive Action Models . NIPS 17.


We return to something more like activity discovery with a touch of
PSRs.



2004, November 1 
 R. Sutton and B. Tanner (2005). TD Networks. NIPS
2004.


This is the almostfinal version of a paper to appear in NIPS 2005.
The final version will appear Monday, probably right after we finish
talking about this version. In any case, it's a generalization of TD
learning and has connections to PSRs.



2004, October 24 
Skipping a week



2004, October 17 
Fall Break



2004, October 11 
 V. Pavlovic, J. M. Rehg, and J. MacCormick (2001). Learning Switching Linear Models of
Human Motion. NIPS 2000.


More Quals Reading List.



2004, October 4 
 F. Dellaert, S. Seitz, C. Thorpe, and S. Thrun (2001). Feature Correspondence: A Markov
Chain Monte Carlo Approach. NIPS 2001.
 MacKay (1998). Introduction to Monte
Carlo Methods. Learning in Graphial Models, pp 175204.


Right. The Quals Reading List for Intelligent Systems. Here's one of
the papers using MCMC for feature correspondence. I'll let Frank know
if any of you misunderstand it, so be on your toes. The second paper
is for background. Many former quals takers have found it to be
extremely helpful.



2004, September 27 
 P. Stone, M. Littman, S. Singh and M. Kearns (2000). ATTac2000: An Adaptive
Autonomous Bidding Agent. JAIR 15:189206.
 N. Montfort (2003). Arby: An Agent to
Assist in Bargaining and Mediation. Poster/Presentation at Penn
Engineering Graduate Research Symposium.
 N. Montfort (2003). Decision
and Learning in Computational Behavorial Game Theory. Poster at
ICML2003.


We continue our game theory discussion this week by looking at two
applied systems that use game theory principles and/or live in a
domain that is typically modeled using game theory. [2] and [3] are
one page posters, and so should be easy reads; however, they reveal
some very serious opportunities and issues in practical applications
of game theory.



2004, September 20 
 C. Shelton (2000). Balancing Multiple Sources of
Reward in Reinforcement Learning. NIPS 2000.
 M. Kearns (2002). Tutorial Slides on Computational
Game Theory. NIPS 2002.


So, we take a break from activity discovery and begin a discussion on
game theory this week and the next. [1] is a paper on balancing
multiple sources of reward and is the paper we will be discussing. [2]
is a set of slides given at a NIPS tutorial on computational game
theory by Michael "Sauve and Smart" Kearns. You will find it quite
useful as background, and almost certainly want to read it first.



2004, September 13 
 S. Basu, M Bilenko, R Mooney (2004). A Probabilistic Framework for
SemiSupervised Clustering. KDD 2004.


Semisupervised clustering. I'll let you work out the connections to
activity and pattern discovery. Dense. I think it won a best paper
award.



2004, August 30 
 J. B. Tenenbaum, V. De Silva, J. C. Langford (2000). A global geometric framework for nonlinear
dimensionality reduction. Science 290 (5500).
 Odest C. Jenkins and Maja J Mataric (2004).A Spatiotemporal Extension to
Isomap Nonlinear Dimension Reduction. Proceedings, International
Conference on Machine Learning (ICML2004).


Papers on Isomap. So, we will be doing some nonlinear dimensionality
reduction with recent additions to handle time. Some of you will have
read the first paper, but probably not the second.



2004, August 23 
Class is now in TSRB 223
 Ajo Fod, Maja J Mataric, and Odest Chadwicke Jenkins. (2002) Automated Derivation of Primitives for
Movement Classification, Autonomous Robots,12(1), 3954.


Just one paper today, but it's seventeen pages long. We'll be
returning to Jenkins and Mataric again.



2004, August 16 
Boggs 339? What's up with that? Initial planning decisions





2004, April 19 
No class Go finish your projects



2004, April 12 

Patterson, Liao, Fox and Kautz (2003). Inferring HighLevel Behavior
from LowLevel Sensors. Ubicomp.




2004, April 5 

Keogh, Lin and Truppel (2003). Clustering of Time Series
Subsequences is Meaningless: Implications for Previous and Future
Research. ICDM.




2004, March 29 

Oates (2002). PERUSE: An Unsupervised
Algorithm for Finding Recurring Patterns in Time. ICDM.

Chiu, Keogh and Lonardi (2003). Probabilistic Discovery of Time Series
Motifs. SIGKDD.




2004, March 22 

Gentner. Exhuming Similarity.

Heit. What is the probability of the
Bayesian model, given the data?.

Boroditsky and Ramscar. "First, we
assume a spherical cow...".

Love. Three deadly sins of category
learning modelers.

Tenenbaum and Griffiths (2001). Generalization, similarity
and Bayesian inference. Behavorial and Brain Sciences 24,
629640.

We are discussing [2] from last week as well as several short
responses to it (each is something like a page). [5] is a longer
version of [2] from last week that might be useful for reference.



2004, March 15 

J. Tenenbaum (1999). Bayesian Modeling of Human Concept
Learning. NIPS 1999.

J. Tenenbaum (2000). Rules and Similarity in Concept
Learning. NIPS 2000.




2004, March 8 
Spring Break



2004, March 1 
see previous week

As mentioned at our meeting of February 23, we will continue our
discussion of both [1] and [2], focusing on technical details. 


2004, February 23 

Y. Ivanov and B. Blumberg (2002). Solving Weak Transduction with
Expectation Maximization. Robotics and Autonomous Systems
972:115.

R. Burke and B. Blumberg (2002). Using an EthologicallyInspired
Model to Learn Apparent Temporal Causality for Planning in Synthetic
Creatures. Proceedings of AAMAS.

We are now moving into notions of learning temporal clusters
and such. Read [1] thoroughly. Then read [2] thoroughly. Have deep
insightful comments, because I really want to understand this
stuff and if you don't help me, I swear I will destroy you all. We may
take this into next week as well (this is one of those interesting
traps: if you read [1] and keep an interesting discussion going
then there is no reason to read [2] because we'll discuss it next
week... but if you don't read [1] throughly then you'll also have to
read [2] because we won't sustain a discussion for an hour about
[1]... personally, I suggest reading [1] thoroughly).



2004, February 16 

N. Tishby, F. Pereira, and W. Bialek (1999). The Information Bottleneck Method. The 37th
annual Allerton Conference on Communication, Control and Computing.

N. Slonim and N. Tishby (2000). Document Clustering using Word
Clusters via the Information Bottleneck Method. Proceedings of
SIGIR.

[1] is from the reading list and [2] is an application of the
technique. Might be worth comparing to [20040202[2]]. 


2004, February 9 

B. Draper, K. Baek, M. Bartlett, J. Beveridge (2003). Recognizing Faces with PCA and
ICA. Computer Vision and Image Understanding (91)115137.

We are continuing our discussion about ICA, PCA and other As from last
week. Given the chaos of the big move on 2/2 you should expect to
also discuss papers [1] and [2] from last week.



2004, February 2 

A. Bell and T. Sejnowski (1996). Edges are the 'Independent
Components' of Natural Scenes. NIPS 1996.
An extended version from Vision
Research 37(23) 33273338 is also available.

C. Isbell and P. Viola (1998). Restructuring
Sparse High Dimensional Data for Effective
Retrieval. NIPS 1998.

A. Bell and T. Sejnowski (1995). An information maximisation approach to blind
separation and blind deconvolution. Neural Computation, 7, 6,
11291159.

[2] and [3] are from the reading list. [3] is reference zero for
ICA, while [1] and [2] are applications of ICA. Read [2] and [3]
carefully, and use [3] for background for this discussion if you have
further questions about the ICA algorithm.



2004, January 26 

J. Kleinberg (2002). An Impossibility Result for
Clustering. Proceedings of NIPS, 2002.

P. Berkhin (2002). Survey Of
Clustering Data Mining Techniques .

[1] is from the reading list. [2] provides some background. For
Monday, pay attention to the first three sections, and think of the
rest as reference material.



2004, January 26 
MLK Holiday



2004, January 12 

M. Littman, R. Sutton, and S. Singh (2001). Predictive Representations of
State. Proceedings of NIPS, 2001.

S. Singh, M. Littman, N. Jong, D. Pardoe and P. Stone (2003). Learning Predictive State
Representations. Proceedings of ICML, 2003.
 (optional)
R. Rivest and R. Schapire (1994). DiversityBased Inference of
Finite Automata. Journal of the ACM, 1994.

[3] is background. It's optional, but worth referring to as you read
[1] and [2]. [1] should be read before [2] but [3] can be read
(skimmed, whatever) before, after, or during.

