Overview | Program Highlights | Tutorials | Conference Sessions | Workshops | Demonstrations | Accepted Papers | Committee 2008
Posters
8:45pm - 12:00am Monday, December 08, 2008
Monday Poster Layout- S. Cohen, K. Gimpel, N. Smith: Unsupervised Bayesian Parameter Estimation for Probabilistic Grammars
- D. Downey, O. Etzioni: Analyzing the Monotonic Feature Abstraction for Text Classification
- J. Boyd-Graber, D. Blei: Syntactic Topic Models
- L. Yang, R. Jin, R. Sukthankar: Semi-supervised Learning with Weakly-Related Unlabeled Data : Towards Better Text Categorization
- M. Dudik, S. Phillips: Generative and Discriminative Learning with Unknown Labeling Bias
- K. Weinberger, O. Chapelle: Large Margin Taxonomy Embedding for Document Categorization
- M. Blaschko, A. Gretton: Learning Taxonomies by Dependence Maximization
- J. Huang, B. Frey: Structured ranking learning using cumulative distribution networks
- S. Clémençon, N. Vayatis: Overlaying classifiers: a practical approach for optimal ranking
- S. Goel, J. Langford, A. Strehl: Predictive Indexing for Fast Search
- R. Tillman, D. Danks, C. Glymour: Integrating Locally Learned Causal Structures with Overlapping Variables
- J. Pellet, A. Elisseeff: Finding Latent Causes in Causal Networks: an Efficient Approach Based on Markov Blankets
- G. Schweikert, C. Widmer, B. Schölkopf, G. Raetsch: An empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis
- A. Bouchard-Côté, M. Jordan, D. Klein: Efficient Inference in Phylogenetic InDel Trees
- D. Bagnell, D. Bradley: (Not) Sparse Coding
- C. Shen, A. Welsh, L. Wang: PSDBoost: Matrix-Generation Linear Programming for Positive Semidefinite Matrices Learning
- T. Zhang: Multi-stage Convex Relaxation for Learning with Sparse Regularization
- P. Rai, H. Daume III: Nonparametric Bayesian Sparse Hierarchical Factor Modeling and Regression
- S. Haufe, V. Nikulin, A. Ziehe, K. Muller, G. Nolte: Estimating vector fields using sparse basis field expansions
- I. Steinwart, A. Christmann: Sparsity of SVMs that use the epsilon-insensitive loss
- P. Ravikumar, G. Raskutti, M. Wainwright, B. Yu: Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of \ell_1-regularizedMLE
- N. Komodakis, N. Paragios, G. Tziritas: Clustering via LP-based Stabilities
- S. Ben-David, M. Ackerman: Measures of Clustering Quality: A Working Set of Axioms for Clustering
- O. Shamir, N. Tishby: On the Reliability of Clustering Stability in the Large Sample Regime
- L. Huang, D. Yan, M. Jordan, N. Taft: Spectral Clustering with Perturbed Data
- M. Maier, U. von Luxburg, M. Hein: Influence of graph construction on graph-based clustering measures
- V. Sindhwani, J. Hu, A. Mojsilovic: Regularized Co-Clustering with Dual Supervision
- D. Zhao, X. Tang: Cyclizing Clusters via Zeta Function of a Graph
- D. Gorur, Y. Teh: An Efficient Sequential Monte Carlo Algorithm for Coalescent Clustering
- Y. Weiss, A. Torralba, R. Fergus: Spectral Hashing
- P. Li, K. Church, T. Hastie: One sketch for all: Theory and Application of Conditional Random Sampling
- Z. Hussain, J. Shawe-Taylor: Theory of matching pursuit
- M. Mohri, A. Rostamizadeh: Rademacher Complexity Bounds for Non-I.I.D. Processes
- S. Kakade, K. Sridharan, A. Tewari: On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization
- M. Shah: Risk Bounds for Randomized Sample Compressed Classifiers
- T. Kanamori, S. Hido, M. Sugiyama: Efficient Direct Density Ratio Estimation for Non-stationarity Adaptation and Outlier Detection
- P. Garrigues, L. El Ghaoui: An Homotopy Algorithm for the Lasso with Online Observations
- Y. Guo: Supervised Exponential Family Principal Component Analysis via Convex Optimizatio
- A. Smith, X. Huo, H. Zha: Convergence and Rate of Convergence of A Manifold-Based Dimension Reduction
- C. Walder, B. Schölkopf: Diffeomorphic Dimensionality Reduction
- S. Bickel, C. Sawade, T. Scheffer: Transfer Learning by Distribution Matching for Targeted Advertising
- I. Mukherjee, D. Blei: Relative Performance Guarantees for Approximate Inference in Latent Dirichlet Allocation
- K. Saenko, T. Darrell: Unsupervised Learning of Visual Sense Models for Polysemous Words
- T. Minka, J. Winn: Gates
- D. Agarwal, L. Zhang: Fast Computation of Posterior Mode in Multi-Level Hierarchical Models
- I. Murray, R. Salakhutdinov: Evaluating probabilities under high-dimensional latent variable models
- L. Kroc, A. Sabharwal, B. Selman: Counting Solution Clusters Using Belief Propagation
- D. Lee, A. Gray: Fast High-dimensional Kernel Summations Using the Monte Carlo Multipole Method
- Z. Harchaoui, F. Bach, E. Moulines: Kernel Change-point Analysis
- K. Yu, W. Xu, Y. Gong: Deep Learning with Kernel Regularization for Visual Recognition
- V. Nair, G. Hinton: Implicit Mixtures of Restricted Boltzmann Machines
- F. Perez-Cruz: Estimation of Information Theoretic Measures for Continuous Random Variables
- K. Chaudhuri, C. Monteleoni: Privacy-preserving logistic regression
- J. McAuley, T. Caetano, A. Smola: Robust Near-Isometric Matching via Structured Learning of Graphical Models
- G. Heitz, G. Elidan, B. Packer, D. Koller: LOOPS: Localizing Object Outlines using Probabilistic Shape
- J. Mairal, F. Bach, J. Ponce, G. Sapiro, A. Zisserman: SDL: Supervised Dictionary Learning
- A. Gupta, J. Shi, L. Davis: A "Shape Aware" Model for semi-supervised Learning of Objects and its Context
- L. Latecki, C. Lu, M. Sobel, X. Bai: Multiscale Random Fields with Application to Contour Grouping
- J. Le Roux, A. de Cheveigné, L. Parra: Adaptive Template Matching with Shift-Invariant Semi-NMF
- Z. Lu, T. Leen, J. Kaye: Hierarchical Fisher Kernels for Longitudinal Data
- B. Calderhead, M. Girolami, N. Lawrence: Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes
- T. Tran, D. Phung, H. Bui, S. Venkatesh: Hierarchical Conditional Random Fields for Recursive Sequential Data
- J. Robinson, A. Hartemink: Non-stationary dynamic Bayesian networks
- D. Nguyen-Tuong, M. Seeger, J. Peters: Local Gaussian Process Regression for Real Time Online Model Learning
- M. Herbster, M. Pontil, G. Lever: On-Line Prediction on Large Diameter Graphs
- F. Radlinski, D. Chakrabarti, R. Kumar, E. Upfal: Mortal Multi-Armed Bandits
- M. Petrik, B. Scherrer: Biasing Approximate Dynamic Programming with a Lower Discount Factor
- P. Coquelin, R. Deguest, R. Munos: Particle Filter-based Policy Gradient in POMDPs
- M. Milani Fard, J. Pineau: MDPs with Non-Deterministic Policies
- R. Sutton, C. Szepesvari, H. Maei: A Convergent O(n) Temporal-difference Algorithm for Off-policy Learning with Linear Function Approxi
- D. Precup, J. Taylor, P. Panangaden: Bounding Performance Loss in Approximate MDP Homomorphisms
- O. Simsek, A. Barto: Skill Characterization Based on Betweenness
- D. Ray, B. King-Casas, P. Montague, P. Dayan: Bayesian Model of Behaviour in Economic Games
- D. Di Castro, D. Volkinshtein, R. Meir: Temporal Difference Based Actor Critic Learning - Convergence and Neural Implementation
- C. Kolodziejski, B. Porr, M. Tamosiunaite, F. Woergoetter: On the equivalence between TD learning and differential Hebbian learning using a local third factor
- C. Kemp, F. Xu: An ideal observer model of infant object perception
- J. Reynolds, M. Mozer: Temporal Dynamics of Cognitive Control
- T. Griffiths, C. Lucas, J. Williams, M. Kalish: Modeling human function learning with Gaussian processes
- T. Schmah, G. Hinton, R. Zemel: Competing RBM density models for classification of fMRI images
- P. Xu, T. Horiuchi, P. Abshire: Short-Term Depression in VLSI Stochastic Synapse
- M. Grosse-Wentrup: Understanding Brain Connectivity Patterns during Motor Imagery for Brain-Computer Interfacing
- C. Fung, K. Wong, S. Wu: Tracking Changing Stimuli in Continuous Attractor Neural Networks
- J. Lewi, R. Butera, L. Paninski: Designing neurophysiology experiments to optimally constrain receptive field models along parametric
- B. Nessler, M. Pfeiffer, W. Maass: Hebbian Learning of Bayes Optimal Decisions
- M. Botvinick, J. An: Goal-directed decision making in prefrontal cortex: a computational framework
- B. Yu, J. Cunningham, G. Santhanam, S. Ryu, K. Shenoy, M. Sahani: Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity
- V. Gómez, A. Kaltenbrunner, V. López, H. Kappen: Self-organization using dynamical synapses
- A. Ponzi, J. Wickens: Cell Assemblies in Large Sparse Inhibitory Networks of Biologically Realistic Spiking Neurons
- J. Huo, Z. Yang, A. Murray: Bio-inspired Real Time Sensory Map Realignment in a Robotic Barn Owl
- T. Sandler, J. Blitzer, P. Talukdar, L. Ungar: Regularized Learning with Networks of Features