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Machine Learning Group Meetings

Meetings are Mondays 11am–Noon (talk starts 11:10am) in LP290C unless otherwise noted.

Meetings are coordinated by Iain Murray.

Spring 2008 Speaker List

Date	Speaker
Jan. 21	Ofer Shai	A Fully Bayesian Approach to Revealing Motifs in Biological Data
Jan. 28	Iain Murray	A critical view of some Gaussian Process implementations
Feb. 4	Danny Tarlow	Dirichlet Process Affinity Propagation
Feb. 11	Ruslan Salakhutdinov Tijmen Tieleman Iain Murray	New methods for learning and evaluating Boltzmann machines
Feb. 18	No meeting	“Family day” holiday and reading week
Feb. 25	Geoffrey Hinton Roland Memisevic	An efficient way to implement three-way interactions in a stackable learning module
Mar. 3	Max Welling (UCI)	Six Ways to Improve the Efficiency of Inference in (Infinite State) Bayesian Networks
Mar. 7	Ryan Adams (Cambridge) 4pm Friday	The Gaussian Process Density Sampler
Mar. 10	Leonid Sigal	Combining Discriminative and Generative Methods for Estimation of Pose and Shape of People
Mar. 17	Simon Lacoste-Julien (Berkeley)	Discriminative Methods with Structure
Mar. 24	Anna Goldenberg (UPenn)	Graphical models for social networks
Mar. 31	Inmar Givoni	Semi-supervised and Hierarchical AP
Apr. 7	Carlos Guestrin (CMU)	Optimizing Sensing from Water to the Web
Apr. 7	Zoubin Ghahramani (Cambridge, CMU) 4pm	Infinite HMMs and extensions
Apr. 10	Terry Sejnowski (Salk) 5.00pm Thursday Bahen 1220	A Critique of Pure Vision
Apr. 14	Graham Taylor	Maximum a Posteriori ICA: Applying Prior Knowledge to the Separation of Acoustic Sources
Apr. 21	No Monday meeting
Apr. 23	Martin Wainwright (Berkeley) 4pm Wednesday	Inference in high-dimensional settings: Trade-offs between computational and statistical efficiency
Apr. 28	Richard Zemel	Learning to annotate images with weakly labeled data
May 5	Tanya Schmah	Applying machine learning to understanding stroke recovery
May 12	Rama Natarajan	Bayesian Model Selection vs. Averaging in Multi-sensory Combination with Conflicting Cues
May 19	Victoria day public holiday
Jun. 16	Jim Huang (talk 1/2)	Cumulative distribution networks and the derivative-sum-product algorithm
Jun. 16	Danny Tarlow (talk 2/2)	Flexible Priors for Exemplar-based Clustering

Past terms’ meetings

Fall 2007 Speaker List

Sept. 10 - Jim Huang - Cumulative distribution networks and the derivative-product algorithm
Sept. 17 - Eddie Ng - Consistent Functional PCA for Financial Time-Series
Sept. 24 - Vincent Cheung - Capturing long-range correlations with patch models
Oct. 1 - Delbert Dueck - Which Algorithm Finds the Best Exemplars Quickly?
Oct. 8 - Thanksgiving day (no meeting)
Oct. 15 - Ben Marlin - Collaborative Filtering and the Missing at Random Assumption
Oct. 22 - Brendan Frey - New Results on Affinity Propagation
Oct. 29 - Gerald Quon - A generative model for the evolution of gene expression, applied to vertebrates
Nov.5 - Yoseph Barash - Deconvolving Patterns of Tissue Specific Alternative Splicing
Nov.12 - Leo J. Lee - The food fair process and exon abundance estimation of gene-wise alternative splicing
Nov.19 - Quaid Morris - RankMotif++: a motif finding algorithm based on a probabilistic model of sequence preferences.
Nov.26 - Emad A. M. Andrews - Connectionist Approaches to Cost-Based Abduction
Dec. 3 - NIPS (no meeting)
Dec. 10 - NIPS highlights
Dec 17 - Ilya Sutskever - Representational Power of Narrow Deep Belief Networks and A Recurrent Neural Network That Learns To Remember

Previous Terms

Spring 2007 Speaker List

Jan. 15th - Dustin Lang - Geometric Features for Fast Image Matching
Jan. 22nd (Bahen 2135) - Paul Viola (Microsoft) - Machine Learning in Microsoft Products:Teleconferencing and Web Browsing
Jan. 29th (Bahen 2140) Special Seminar in ECE Dept - Radu Balan - Exploiting Redundancy of Speech Signals in Speech Enhancement and Blind Source Separation
Feb. 5th - Babak Shahbaba - Nonlinear classification models using Drichlet process mixtures
Feb. 12th - Radford Neal - Constructing Efficient MCMC Methods Using Temporary Mapping and Caching
Feb. 19th - Yee Whye Teh - Stick-breaking Construction for the Indian Buffet Process
Feb. 26th - Lourdes Peña Castillo (BBDMR) - MouseFunc: A Competition for Mouse Gene Function Prediction
Mar. 5th - Sam Roweis - Sparse Boosting
Mar. 12th - Tijmen Tieleman - Some investigations into energy-based models
Mar. 19th - Roland Memisevic - Unsupervised learning of image transformations
Mar. 26th - Renqiang Min - Kernel Learning Using Class Information
Apr. 2nd - Scott Leishman - Shape-Free Statistical Information in Optical Character Recognition
Apr. 9th - Vinod Nair - Inverting Generative Black Boxes with Breeder Learning
Apr. 16th - Ruslan Salakhutdinov - Deep Belief Nets
Apr. 23rd - Rich Zemel - Learning Articulated Skeletons from Motion
Apr. 30th - Andriy Mnih - Three New Models for Statistical Language Modelling
May. 7th - Noa Novershtern - Physical Module Networks: An Integrative Probabilistic Model of Transcription Regulation

Fall 2006 Speaker List

Sept. 11th - Geoff Hinton - Modelling high-dimensional sequential data using distributed hidden state

Sept. 18th - Inmar Givoni - Matrix Tile Analysis

Sept. 25th - Bill Freeman (MIT) - Analysis of contour motions, and the random camera

Oct. 2nd - Danny Tarlow - Using Combinatorial Optimization within Max Product Belief Propagation

Oct. 9th - Thanksgiving day (no meeting)

Oct. 16th - Eric Hsu - Convergent Belief Propagation for Boolean Satisfiability

Oct. 23rd - (3pm) Jim Glass (MIT) - Unsupervised Pattern Discovery in Speech

Oct. 30th - Xuming He - Learning Visual Features for Outdoor Localization

Nov. 6th - Francisco Pereira (CMU)- Beyond brain blobs: machine learning classifiers as instruments for analyzing fMRI data

Nov. 13th - Clement Chung - Identifying PTM Groups

Nov. 20th - Brendan Frey - What do Einstein, Convexified BP and the NetFlix Competition Have in Common?

Nov. 27th - Jo-Anne Ting (USC) - Towards Bayesian Black Box Learning Systems

Dec. 4th - NIPS (no meeting)

Dec. 11th Rama Natarajan - Learning to Encode Dynamic Inputs in Stochastic Neural Populations

Dec. 18th - NIPS highlights

Spring 2006 Speaker List

January 9th Sam Roweis - Fast Geometric Hashing for Automated Astrometry

January 16th Geoff Hinton - Using Unsupervised Learning to Initialize Backpropagation

January 23rd Quaid Morris - Untangling biological networks using subgraph enumeration priors

January 30th Ilya Sutskever - Modelling sequences with spiking neurons

February 6th David Ross - Learning to track by combining flexible discriminative features

February 13th Jennifer Listgarten - Toward an HIV Vaccine: Algorithms for Epitope Prediction and Identification

February 20th - no meeting (reading week)

February 27th Mate Lengyel (Gatbsy) - Bayesian model learning in human visual perception

March 6th Jim Huang - Learning miRNA targets from sequence and expression data

March 13th John Langford (TTI Chicago) - The Reductionist Approach to Solving Learning Problems

March 20th Iain Murray (Gatsby) - MCMC for doubly-intractable distributions

March 27th Ruslan Salakhutdinov - Nonlinear Dimensionality Reduction

April 3rd in LP266 Rich Sutton (Alberta) - Experience-oriented artificial intelligence

April 10th Yoseph Barash - Simultaneous Learning of Multiple cis Elements

April 17th Vinod Nair - Learning to do vision by inverting a graphics model

April 24th Ted Meeds - Binary Matrix Factorization

May 1st Aapo Hyvärinen (Helsinki) - Computationally simple and statistically optimal estimation of non-normalized statistical models

May 8th Shai Ben-David (Waterloo) - Two cases of Theory-Practice gap; Clustering stability and SVMs

Fall 2005 Speaker List

September 12th Nati Srebro - When is clustering hard?
September 19th Liam Stewart - Structure Learning in Sequential Data
September 26th Aaron Hertzmann - Learning Biomechanical Models of Human Motion
October 3rd Brendan Frey - Mixture Modeling by Affinity Propagation
October 10th - Thanksgiving
Special seminar: Tuesday, October 11th, 4:00 PM, Stuart Russell (UC Berkeley) - Reinforcement learning with partial programs in LP266 (joint with the Knowledge Representation group)
October 17th Amir Globerson (Hebrew University) - Metric Learning by Collapsing Classes
October 24th Simon Osindero - An Alternative Infinite Mixture of Gaussian Process Experts
October 31st Roland Memisevic
November 7th Alex Pouget - Neuronal HMM's
November 14th Ben Marlin - Synthetic Low-Light Photography from Multiple Exposures
November 21th Soosan Beheshti (Ryerson) - Minimum Description Complexity
November 28th Rama Natarajan - Spiking Probabilistic Computations in Time
Special seminar: Tuesday, November 29th, 4:00 PM, Michael Kearns (University of Pennsylvania) - Graphical Models for Game Theory: An Overview
December 5th - NIPS (no meeting)
December 12th - NIPS wrap-up and highlights

Spring 2005 Speaker List

Jan. 10 NIPS 17 review
Jan.17 Ofer Shai Probabilistic Inference of Alternative Splicing Events in Microarray Data
Jan. 24 Longhai Li Correcting the Bias in Feature Selection: An Example in Mixture Models
Jan.31 Liam Stewart Models for Action Learning in Dynamical Systems
Feb. 7 Geoff Hinton A Fast Learning Algorithm for Deep Belief Networks
Feb. 9 11am Yoseph Barash From Promoter to Expression - A Probabilistic Framework for Inferring Regulatory Mechanisms
Feb. 14 Reading Week
Feb.21 Tim Lillicrap Adaptive Control in Sensorimotor Systems
Feb.28 Radford Neal Creating Non-Gaussian Processes from Gaussian Processes by the Log-Sum-Exp Approach
Mar. 7 Andriy Mnih Learning Nonlinear Constraints with Contrastive Backpropagation
Mar. 14 Quaid Morris Statistical machine learning for large-scale biology
Mar.21 Kevin Laven Statistical methods for page segmentation and labeling
Mar.28 Dustin Lang Fast Methods for Machine Learning
Apr. 4 Vladimir Jojic An algorithm for design and discovery of protein receptors
Apr.18 Brendan Frey Learning recursive generative models using the sum-product algorithm

Fall 2004 Speaker List

Sept. 13 Simon Osindero Learning Causally Linked MRFs
Sept. 20 David Mackay Line Attractors with Spiking Neurons
Sept. 27 Jennifer Listgarten Multiple Alignment of Continuous Time Series
Oct. 4 Ben Marlin Unsupervised Learning With Non-ignorable Missing Data
Oct. 11 Thanksgiving, no meeting
Oct. 15 Yee Whye Teh (in the Vision meeting) Making Latin Manuscripts Searchable using generalized hidden Markov models
This is a joint seminar with the Vision group. The talk will be held Friday at 1:30pm in Pratt 266. Note the non-standard time and location!
Oct. 18 Vincent Cheung Modelling Motion Patterns with Video Epitomes
Oct. 25 Anitha Kannan Generative models for visual scene analysis
Nov. 1 Rama Natarajan Probabilistic Computation in Spiking Neurons
Nov. 8 Rich Zemel Proximity Graphs for Clustering and Manifold Learning
Nov. 15 Nati Srebro Maximum Margin Matrix Factorization
Nov. 22 Geoff Hinton Learning to perceive how handwritten characters were drawn
Nov. 29 Xuming He Semi-supervised Learning of Random Fields for Image Labeling
Dec. 6 Grigoris Karakoulas Mixture-of-Experts Classification under Different Missing Label Mechanisms
(last meeting in 2004)

Spring 2004 Speaker List

Jan. 5 Vladimir Jojic HIV sequence analysis using epitomes
Jan.12 1pm Greg Wilson How Do Novices Actually Program?
Jan.19 David Ross Incremental Visual Learning for Object Tracking
Jan.26 Romer Rosales Unsupervised Image Translation
Feb. 2 Quaid Morris Automatic functional annotation using a comprehensive set of mouse gene expression profiles.
Feb. 9 Roland Memisevic Unsupervised Kernel Regression
Feb.13 2pm Lawrence Saul Nonlinear dimensionality reduction by semidefinite programming
Feb.16 Reading Week
Feb.23 Golan Yona BIOZON
Mar. 1 Nati Srebro Learning With Matrix Factorization: Methods and Analysis
Mar. 8 Kannan Achan A Segmental HMM for Speech Waveforms
Mar.15 Brendan Frey Bayesian Network for Finding Genes Using Gene Expression Data
Mar.22 Yee Whye Teh Hierarchical Dirichlet Processes
Mar.29 Russ Greiner Budgeted Learning of Naive-Bayes Classifiers
Apr. 5 Sam Roweis Nonlinear Sensor Fusion Networks
Apr.12 Cristian Sminchisescu Mixture Smoothing for Non-Linear Non-Gaussian Systems
Apr.19 Khashayar Rohanimanes Dynamic Conditional Random Fields: Factorized Probabilistic Models for Labeling and Segmenting Sequence Data
Apr.26 Delbert Dueck Gene Expression data: From Clustering to Factor Analysis
May.3 Parham Aarabi Multi-Microphone Speech Processing, or, Why Two Ears Are Better Than One

Fall 2003 Speaker List

Sep 15 Matt Beal, Variational Scoring of Graphical Model Structures
Sept.22 Jacob Goldberger, Using the information bottleneck method for image database clustering
Sept.29 Timothy Hughes, Inferring gene functions and regulatory mechanisms in yeast and mouse using compendia of microarray gene expression data
Oct.6 Romer Rosales, rescheduled
Friday Oct.17 David Blei, 11am-12:30pm in LP290C, Learning the Structure of a Topic Hierarchy from Text
Oct.20 Mikio Braun, Relative Eigenvalue Perturbations and Kernel Methods
Oct.27 Renqiang Min, Using Low-dimensional Codes of Objects for Recognition
Nov.03 Geoffery Hinton, How To Make Autoencoders Work
Nov.10 Xuming He, Learning Multiscale Random Fields for Image Classification
Nov.17 Nina Thiessen, A Visualization Tool for Similarity Relationships
Nov.24 Yousuf Shamim Ahmed, Multiple Random Projection For Fast Nearest Neighbor Search in High Dimension
Dec.1 Chris Harvey, Sparse Features and Fast Indexing for Automated Astrometry
Dec.8 NIPS, No meeting
Dec.15 Avner Magen A Reduction of Dimensionality that Preserves Volumes and its Applications

Spring 2003 Speaker List

Jan.13 Sam Roweis, Learning Compact Approximations for Large Matrices
Jan.20 Mike Jordan, UC Berkeley, Some Recent Convexity-Related Things
Jan.27 Shaojun Wang, The Latent Maximum Entropy Principle
Feb.03 Romer Rosales, Generative Models of Affinity Matrices
Feb.10 Aaron Hertzmann, Style-Based Computer Animation
Feb.24 Brendan Frey, Factor Graphs: What they offer that Bayes nets and MRFs are lacking
Mar.3 Cristian Sminchisescu, Algorithms for High-Dimensional Search. Applications to Three-Dimensional Human Modeling and Motion Reconstruction in Monocular Video Sequences
Mar.8 Matthias Seeger, rescheduled.
Mar.17 Max Welling, Propagation Algorithms for Probabilistic Inference in Graphical Models with Cycles
Apr.07 Benjamin Marlin, Probabilistic Models for Collaborative Filtering
Apr.11 10:30am Matthias Seeger, PAC-Bayesian Theorems for Gaussian Process Classification
Apr.14 Quaid Morris, Graph Denoising: Inferring Protein-Protein Interaction Networks
Apr.28 Max Welling, Under-complete Products of Experts

Fall 2002 Speaker List

Sept.10 Chris Williams, Learning About Multiple Objects in Images: Factorial Learning without Factorial Search.
Sept.17 Max Welling, Propagation Rules for Linear Response Estimates of Joint Pairwise Probabilities.
Sept.24 Russ Salakhutdinov, An Efficient Greedy Way to Perform Unsupervised Text Segmentation.
Oct.01 Groupmeeting cancelled due to talk of Selfridge.
Oct.08 Stark Draper, An information-theoretic approach to communicating and combining sensory measurements.
Oct. 15 Andrew Blake, Microsoft Research, Cambridge. Texture models for interactive image segmentation.
Oct.22 David Ross, Multiple Cause Vector Quantization.
Oct.29 Chris Pal, Probabilistic Montage Models of Images.
Nov.05 Quaid Morris, Practical Probabilistic Inference.
Nov.12 Anitha Kannan, Epitomic Image Analysis.
Nov.19 Adrian Corduneanu, Continuation Methods for Mixing Heterogeneous Sources of Information.
Nov.26 Miguel Carreira-Perpinan, Generalized Elastic Nets.
Dec.03 Pascal Poupart, Value-Directed Compression of POMDPs (Partially Observable Markov Decision Processes).