Machine Learning and Friends Lunch

past talks


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
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
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

Date Speaker Abstract Lab Institution
05/11 Charles Elkan Modeling Word Burstiness Using the Dirichlet Distribution UCSD


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
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
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
UC Berkeley
09/19 Matthew Taylor Behavior Transfer for Value-Function-Based Reinforcement Learning UT Austin


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


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


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


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


Date Speaker Abstract Lab Institution
05/08 Zhengzhu Feng Symbolic Generalization for On-line Planning RBR UMass
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
03/27 Jennifer Neville Learning Relational Probability Trees KDL UMass