Machine Learning and Friends Lunch
home
past talks
resources
conferences
 
FALL 2006
Date
Speaker
Abstract
Lab
Institution
12/07
Hema Raghavan
TBD
CIIR
UMASS
11/30
Gideon Mann
TBD
IESL
UMASS
11/16
Pippin Wolfe
TBD
ALL
UMASS
11/09
George Konidaris
TBD
ALL
UMASS
11/02
Stephan Murtagh
Predicting the Difficulty of Factored Multi-Agent Domains
MLL
UMASS
10/26
Charles Sutton
Local Training and Belief Propagation
IESL
UMASS
10/19
John Fisher
Information-driven Inference in Resource-constrained Envronments
MIT
10/12
Marek Petrik
Basis Construction for Value Function Approximation Using Laplacian and Krylov Methods
Resource-Bounded
Reasoning Laboratory
UMASS
Andrew Fast
The NFL Coaching Network: Analysis of the Social Network Among Professional Football Coaches
KDL
UMASS
10/05
Fernando Diaz
Pseudo-Aligned Multilingual Corpora
CIIR
UMASS
09/29 (Friday 1.30)
Erik Sudderth
Learning Object Appearance Models via Transformed Dirichlet Processes
Laboratory for Information and Decision Systems
&
Computer Science and Artificial Intelligence Laboratory
MIT
09/28
Amit Singhal
Challenges in Running a Commercial Search Engine
Google
09/14
Pallika Kanani
Resource-Bounded Information Gathering for Entity Resolution
IESL
UMASS
Ben Carterette
Minimal Test Collections for Retrieval Evaluation (SIGIR 06 Best Paper)
CIIR
UMASS
SUMMER 2006
Date
Speaker
Abstract
Lab
Institution
05/11
Charles Elkan
Modeling Word Burstiness Using the Dirichlet Distribution
UCSD
SPRING 2006
Date
Speaker
Abstract
Lab
Institution
05/11
Wei Li
Pachinko Allocation: DAG-Structured Mixture Models of Topic Correlations
IESL
UMASS
05/04
Matthew J. Rattigan
Using Structure Indices for Efficient Approximation of Network Properties
KDL
UMASS
04/13
Don Metzler
Beyond Bags of Words: A Markov Random Field Model for Information Retrieval
CIIR
UMASS
04/06
Sharon Goldwater
Learning Linguistic Structure using Nonparametric Bayesian Techniques
COG
Brown
03/30
Jerod Weinman
Leveraging Character Similarity to Improve Recognition of Novel Fonts
VIS
UMASS
03/30
Shaolei Feng
A Hierarchical, HMM based Automatic Evaluation of OCR Accuracy for a Digital Library of Books
MIR
UMASS
03/16
Brendan Burns
Reasoning about sensor uncertainty in probabilistic planning
LPR
UMASS
03/09
Ron Bekkerman
Non-Bayesian Networks
CIIR
UMASS
03/02
Chris Pal
Computer Vision and Machine Learning - Representations and Algorithms for Creating Interactive Visual Media
IESL
UMASS
02/23
Aron Culotta
Integrating Probabilistic Extraction Models and Data Mining to Discover Relations and Patterns in Text
IESL
UMASS
02/16
Fernando Diaz
Regularizing Ad Hoc Information Retrieval Scores
CIIR
UMASS
FALL 2005
Date
Speaker
Abstract
Lab
Institution
12/01
George Konidaris
Agent Space vs. Problem Space: Knowledge and Skill Transfer in Reinforcement Learning
ALL
UMASS
11/17
Hal Daume III
Learning in Search: Practical Learning Theory for Hard Language Problems
NLG
ISI
11/10
Khash Rohanimanesh
Concurrent Decision Making in Markov Decision Processes
ALL
UMASS
11/03
Mauro Maggioni
Diffusion Wavelets for multiscale analysis on manifolds and graphs: constructions and applications
Applied
Mathematics
Yale
10/27
Dima Lisin
Using All Available Information: Combining Feature Vectors and Bags of Features for Object Class Recognition.
VIS
UMASS
10/20
Xuerui Wang
Group and Topic Discovery from Relations and Their Attributes
IESL
UMASS
10/13
Wei Wei
Identifying 802.11 Wireless Traffic from Passive Measurements Using Iterative Bayesian Inference
CNRG
UMass
10/06
Micheal Kelm
Feature Extraction and Classification in Magnetic Resonance Spectroscopic Imaging
Multidimensional
Image
Processing
Univ. of Heidelberg
10/06
Victoria Manfredi
Switching Kalman Filters for Prediction and Tracking in an AdaptiveMeteorological Sensing Network
CNRG
UMass
09/29
Andras Ferencz
Building Classification Cascades for Visual Identification from One Example
Computer
Vision
Group
UC Berkeley
09/19
Matthew Taylor
Behavior Transfer for Value-Function-Based Reinforcement Learning
UT Austin
SPRING 2005
Date
Speaker
Abstract
Lab
Institution
05/12
Stephen Murtagh
Solving Large Problem Domains with Goal Regression
MLL
UMass
05/05
Chris Pal
Probability Models for Joint Row Column Clustering and High Dynamic Range Image Processing
IESL
UMass
04/28
Sridhar Mahadevan
SAMUEL MEETS AMAREL: A Coordinate-Free Framework for Learning Behavior and Representation
ALL
UMass
04/14
David Parkes
Optimal Coordinated Learning Among Self-Interested Agents in the Multi-Armed Bandit Problem
EconCS
Harvard
04/07
Ron Bekkerman
Distributional Clustering and Graphical Models
IESL
UMass
04/07
Andrés Corrada-Emmanuel
Optimal Number of Topics in LDA Models
IESL
UMass
03/31
Charles Sutton
Cliquewise Training for Undirected Models
IESL
UMass
03/24
Steve Hart
A Relational Representation for Procedural Task Knowledge
LPR
UMass
03/09
Martin C. Martin
... But What About The Structure?
IcoSystem
03/03
Mike Ross
Learning Static Object Segmentation from Motion Segmentation
CSAIL
MIT
02/24
Sam Roweis
Neighbourhood Components Analysis
Machine Learning
University of Toronto
02/17
Roni Khardon
Learning and Logic: Theoretical Foundations and Efficient Systems
Dept. of Computer Science
Tufts University
02/10
Ivon Arroyo
Applications of machine learning to education: Inferring learning and attitudes from students' behavior in a tutoring system
CKC
UMass
FALL 2004
Date
Speaker
Abstract
Lab
Institution
12/09
CMPSCI 689
Machine Learning Class Projects
UMass
12/02
Robert Platt
Generalization and Differentiation of Robot Manipulation Skills
LPR
UMass
11/18
Özgür Şimşek
Acquiring useful skills in reinforcement learning
ALL
UMass
11/4
TJ Brunette
From Sequence to Structure: A Holistic Approach
LPR
UMass
10/28
Charles Sutton
Learning in Markov Random Fields with Contrastive Free Energies
IESL
UMass
10/21
Hema Raghavan
Interactive Feature Selection in Active Learning for Text Classification
CIIR
UMass
10/21
Gary Huang
Document Classification via Multiple Linear SVM Projections
IESL
UMass
10/14
Ramesh Nallapati
Generative Classification Models for Information Retrieval
CIIR
UMass
10/07
Alicia Wolfe
Object Based Abstraction Using MDP Homomorphisms
ALL
UMass
09/30
Yee Whye Teh
Hierarchical Dirichlet processes: A Bayesian approach to sharing clusters among related groups
Dept. of Computer Science
UC-Berkeley
09/23
Sarah Osentoski
Learning Hierarchical models of activity
ALL
UMass
09/23
Jerod Weinman
Sign Detection in Natural Images with Conditional Random Fields
VIS
UMass
09/16
Erik Learned-Miller
Toward better face recognition: non-parametric maximum likelihood methods for separating causes in images
VIS
UMass
SUMMER 2004
Date
Speaker
Abstract
Lab
Institution
08/26
Ryan McDonald
Conditional models for natural language dependency parsing
Computer Science Dept.
UPenn
07/22
Aron Culotta
Extracting social networks and contact information from email and the Web
IESL
UMass
07/22
Toni Rath
A search engine for historical manuscript images
CIIR
UMass
07/15
Brendan Burns
Approximate Models, Utility and Robotic Motion Planning
LPR
UMass
06/24
Nir Friedman
Probabilistic Models for Identifying Regulation Networks
CMBL
The Hebrew University of Jerusalem
SPRING 2004
Date
Speaker
Abstract
Lab
Institution
05/13
Natalia Hernandez Gardiol
Planning with action equivalences
AI Lab
MIT
05/06
David Stracuzzi
Learning Many Layers of Structure
MLL
UMass
04/29
Andrés Corrada-Emmanuel
Variational Approximations for Inference over Graphical Models
CIIR
UMass
04/22
Sam Roweis
Discrimination with Generative Models
Machine Learning
University of Toronto
04/15
Claudia Goldman-Shenhar
Decentralized Language Learning Through Acting
RBR
UMass
04/08
David Blei
Fast and Flexible Clustering---Variational methods for the Dirichlet process
CS
UC-Berkeley
04/08
Paul Utgoff
Learning From Textbooks
MLL
UMass
04/01
Andrew Arnt
Attribute Measurement Policies for Cost-effective Classification
RBR
UMass
03/25
Anders Jonsson
A novel approach to abstraction discovery in MDPs
ALL
UMass
03/11
Ramesh Nallapati
Discriminative models for Information Retrieval
CIIR
UMass
03/04
Robert Moll
Machine Learning for Combinatorial Optimization
TCSC
UMass
02/26
Michael Rosenstein
From Supervisory Control To Intelligent Prosthetics
ALL
UMass
02/19
Andrew Fagg
Reaching and Grasping Skills
LPR
UMass
02/12
Charles Sutton
Models for Labeling and Segmenting Sequence Data
IESL
UMass
FALL 2003
Date
Speaker
Abstract
Lab
Institution
12/11
CMPSCI 689
Machine Learning Class Projects
 
UMass
11/20
Jeremy Pickens
Polyphonic Music Modeling with Random Fields
CIIR
UMass
11/13
Michael Jordan
Chinese restaurant process priors for graphical models
 
UC-Berkeley
11/06
David Jensen
Information Awareness: A Prospective Technical Assessment
KDL
UMass
10/23
Brendan Burns
Opportunities for Machine Learning in Path Planning
LPR
UMass
10/16
Alvaro Bolivar
Relevance Models: An application in the Real World
CIIR
UMass
10/16
Aron Culotta
Relation extraction using tree kernels
IESL
UMass
10/16
Gary Huang
Trend Detection and Entity Identification
IESL
UMass
10/09
Mohammad Ghavamzadeh
Hierarchical Policy Gradient Algorithms
ALL
UMass
10/02
Amy McGovern
Untangling Multiple-Instance Learning
KDL
UMass
09/25
Rob Platt
Extending Fingertip Grasping to Whole Body Grasping
LPR
UMass
09/18
Balaraman Ravindran
Hierarchical Reinforcement Learning
ALL
UMass
09/11
Rod Grupen
A Developmental Organization for Robot Behavior
LPR
UMass
SUMMER 2003
Date
Speaker
Abstract
Lab
Institution
07/31
Daniel Bernstein
Contract Algorithms and Robots on Rays: Unifying Two Scheduling Problems
RBR
UMass
07/24
R. Manmatha
Automatic Image Annotation and Retrieval using Cross-Media Relevance Models
CIIR
UMass
07/03
Fuchun Peng
Latent Maximum Entropy: A new approach to mixture model estimation
CIIR
UMass
06/12
Fernando Pereira
Shallow Parsing with Conditional Random Fields plus an overview of recent work in Bioinformatics at UPenn
UPenn
SPRING 2003
Date
Speaker
Abstract
Lab
Institution
05/08
Zhengzhu Feng
Symbolic Generalization for On-line Planning
RBR
UMass
05/01
Dr. Leon Peshkin
DYNAMIC BAYESIAN NETS FOR LANGUAGE MODELING
Harvard
04/24
Ping Ji
Modeling Frame-level Errors in GSM Wireless Channels
CNRG
UMass
04/17
Anita Raja
Meta-Level Control in Bounded-Rational Agents
MAS
UMass
04/10
Victor Lavrenko
Density Allocation for Modeling Discrete Data
CIIR
UMass
04/03
Michael T. Rosenstein
SUPERVISED ACTOR-CRITIC REINFORCEMENT LEARNING
ALL
UMass
03/27
Jennifer Neville
Learning Relational Probability Trees
KDL
UMass