AI & Statistics 2014
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AISTATS*2014 Poster Sessions

Poster Session 1, Tuesday, 22 April

Poster layout, Poster session 1, Tuesday, 22 April
Click the image to view the layout in larger size.

Bayesian methods

P001 Sequential crowdsourced labeling as an epsilon-greedy exploration in a Markov Decision Process
Vikas Raykar; Priyanka Agrawal

P002 Active Boundary Annotation using Random MAP Perturbations
Subhransu Maji; Tamir Hazan; Tommi Jaakkola

P003 Bayesian Switching Interaction Analysis Under Uncertainty
Zoran Dzunic; John Fisher III

Notable paper: P004 Analysis of Empirical MAP and Empirical Partially Bayes: Can They be Alternatives to Variational Bayes?
Shinichi Nakajima; Masashi Sugiyama

P005 SMERED: A Bayesian Approach to Graphical Record Linkage and De-duplication
Rebecca Steorts; Rob Hall; Stephen Fienberg

Bayesian nonparametrics

P006 Bayesian Nonparametric Poisson Factorization for Recommendation Systems
Prem Gopalan; Francisco J. Ruiz; Rajesh Ranganath; David Blei

P007 Incremental Tree-Based Inference with Dependent Normalized Random Measures
Juho Lee; Seungjin Choi

P009 Student-t Processes as Alternatives to Gaussian Processes
Amar Shah; Andrew Wilson; Zoubin Ghahramani

P010 Bayesian Logistic Gaussian Process Models for Dynamic Networks
Daniele Durante; David Dunson

P011 The Dependent Dirichlet Process Mixture of Objects for Detection-free Tracking and Object Modeling
Willie Neiswanger; Frank Wood; Eric Xing

P012 Pan-sharpening with a Bayesian nonparametric dictionary learning model
Xinghao Ding; Yiyong Jiang; Yue Huang; John Paisley


P013 Connected Sub-graph Detection
Jing Qian; Venkatesh Saligrama; Yuting Chen

Computational biology and genomics

P014 Sparse Bayesian Variable Selection for the Identification of Antigenic Variability in the Foot-and-Mouth Disease Virus
Vinny Davies; Richard Reeve; William Harvey; Francois Maree; Dirk Husmeier

P015 Context Aware Group Nearest Shrunken Centroids in Large-Scale Genomic Studies
Juemin Yang; Fang Han; Rafael Irizarry; Han Liu,

Gaussian processes

P016 Hybrid Discriminative-Generative Approach with Gaussian Processes
Ricardo Andrade Pacheco; James Hensman; Max Zwiessele; Neil Lawrence

P017 Analytic Long-Term Forecasting with Periodic Gaussian Processes
Nooshin HajiGhassemi; Marc Deisenroth,

P018 Tilted Variational Bayes
James Hensman; Max Zwiessele; Neil Lawrence

P019 Bat Call Identification with Gaussian Process Multinomial Probit Regression and a Dynamic Time Warping Kernel
Vassilios Stathopoulos; Veronica Zamora-Gutierrez; Kate Jones; Mark Girolami

P020 Explicit Link Between Periodic Covariance Functions and State Space Models
Arno Solin; Simo Särkkä

P021 Probabilistic Solutions to Differential Equations and their Application to Riemannian Statistics
Philipp Hennig; Søren Hauberg

Graphical models and approximate inference

P022 Learning Heterogeneous Hidden Markov Random Fields
Jie Liu; Chunming Zhang; Elizabeth Burnside; David Page

P023 Random Bayesian networks with bounded indegree
Eunice Yuh-Jie Chen; Judea Pearl

P024 Active Learning for Undirected Graphical Model Selection
Divyanshu Vats; Robert Nowak; Richard Baraniuk

P025 Nonparametric estimation and testing of exchangeable graph models
Justin Yang; Christina Han; Edoardo Airoldi

P026 An inclusion optimal algorithm for chain graph structure learning
Jose Peña; Dag Sonntag; Jens Nielsen

P027 Joint Structure Learning of Multiple Non-Exchangeable Networks
Chris Oates; Sach Mukherjee

P028 Adaptive Variable Clustering in Gaussian Graphical Models
Siqi Sun; Yuancheng Zhu; Jinbo Xu

P029 On the Testability of Models with Missing Data
Karthika Mohan; Judea Pearl

P030 Interpretable Sparse High-Order Boltzmann Machines
Martin Renqiang Min; Xia Ning; Chao Cheng

P031 Mixed Graphical Models via Exponential Families
Eunho Yang; Yulia Baker; Pradeep Ravikumar; Genevera Allen; Zhandong Liu

P032 Learning Bounded Tree-width Bayesian Networks using Integer Linear Programming
Pekka Parviainen; Hossein Shahrabi Farahani; Jens Lagergren

P033 Learning Optimal Bounded Treewidth Bayesian Networks via Maximum Satisfiability
Jeremias Berg; Matti Järvisalo; Brandon Malone

P034 Exploiting the Limits of Structure Learning via Inherent Symmetry
Peng He; Changshui Zhang

P035 A Gaussian Latent Variable Model for Large Margin Classification of Labeled and Unlabeled Data
Do-kyum Kim; Matthew Der; Lawrence Saul

P036 Learning with Maximum A-Posteriori Perturbation Models
Andreea Gane; Tamir Hazan; Tommi Jaakkola

P037 Efficient Inference for Complex Queries on Complex Distributions
Lili Dworkin; Michael Kearns; Lirong Xia

Model selection

P038 Robust learning of inhomogeneous PMMs
Ralf Eggeling; Teemu Roos; Petri Myllymäki; Ivo Grosse

P039 Fully-Automatic Bayesian Piecewise Sparse Linear Models
Riki Eto; Ryohei Fujimaki; Satoshi Morinaga; Hiroshi Tamano

P040 Efficient Transfer Learning Method for Automatic Hyperparameter Tuning
Dani Yogatama; Gideon Mann

Nonlinear embedding and manifold learning

P041 Linear-time training of nonlinear low-dimensional embeddings
Max Vladymyrov; Miguel Carreira-Perpinan

P042 Sketching the Support of a Probability Measure
Joachim Giesen; Soeren Laue; Lars Kuehne


P043 Lifted MAP Inference for Markov Logic Networks
Somdeb Sarkhel; Deepak Venugopal; Parag Singla; Vibhav Gogate

P044 A Stepwise uncertainty reduction approach to constrained global optimization
Victor Picheny

P045 Global Optimization Methods for Extended Fisher Discriminant Analysis
Satoru Iwata; Yuji Nakatsukasa; Akiko Takeda

P046 Efficient Low-Rank Stochastic Gradient Descent Methods for Solving Semidefinite Programs
Jianhui Chen; Tianbao Yang; Shenghuo Zhu

P047 Distributed optimization of deeply nested systems
Miguel Carreira-Perpinan; Weiran Wang

P048 Accelerated Stochastic Gradient Method for Composite Regularization
Wenliang Zhong; James Kwok

Reinforcement learning and control

P049 Dynamic Resource Allocation for Optimizing Population Diffusion
Shan Xue; Alan Fern; Daniel Sheldon

P050 Optimality of Thompson Sampling for Gaussian Bandits Depends on Priors
Junya Honda; Akimichi Takemura

Scientific data analysis

P051 A Statistical Model for Event Sequence Data
Kevin Heins; Hal Stern

P052 Towards building a Crowd-Sourced Sky Map
Dustin Lang; David Hogg; Bernhard Schölkopf

Semi-supervised learning

P053 Class Proportion Estimation with Application to Multiclass Anomaly Rejection
Tyler Sanderson; Clayton Scott

P054 Heterogeneous Domain Adaptation for Multiple Classes
Joey Tianyi Zhou; Ivor W.Tsang; Sinno Jialin Pan; Mingkui Tan

Social and information networks

P055 Computational Education using Latent Structured Prediction
Tanja Käser; Alexander Schwing; Tamir Hazan; Markus Gross

Sparse estimation, compressed sensing

P057 Path Thresholding: Asymptotically Tuning-Free High-Dimensional Sparse Regression
Divyanshu Vats; Richard Baraniuk

P058 FuSSO: Functional Shrinkage and Selection Operator
Junier Oliva; Barnabas Poczos; Timothy Verstynen; Aarti Singh; Jeff Schneider; Fang-Cheng Yeh; Wen-Yih Tseng

P059 Information-Theoretic Characterization of Sparse Recovery
Cem Aksoylar; Venkatesh Saligrama

P060 Average Case Analysis of High-Dimensional Block-Sparse Recovery and Regression for Arbitrary Designs
Waheed Bajwa; Marco Duarte; Robert Calderbank

Spectral methods

P061 High-Dimensional Density Ratio Estimation with Extensions to Approximate Likelihood Computation
Rafael Izbicki; Ann Lee; Chad Schafer

P062 Low-Rank Spectral Learning
Alex Kulesza; N. Raj Rao; Satinder Singh

Statistical learning theory

P063 Tight Bounds for the Expected Risk of Linear Classifiers and PAC-Bayes Finite-Sample Guarantees
Jean Honorio; Tommi Jaakkola

P064 Decontamination of Mutually Contaminated Models
Gilles Blanchard; Clayton Scott

P065 A Finite-Sample Generalization Bound for Semiparametric Regression: Partially Linear Models
Ruitong Huang; Csaba Szepesvari

P066 New Bounds on Compressive Linear Least Squares Regression
Ata Kaban

P067 PAC-Bayesian Collective Stability
Ben London; Bert Huang; Ben Taskar; Lise Getoor

P068 PAC-Bayesian Theory for Transductive Learning
Luc Bégin; Pascal Germain; François Laviolette; Jean-Francis Roy

Late-breaking Posters

Leonard Santana; James Allison; Simos Meintanis

L002 Dependent Pairs of Maximum Mean Descrepancy Tests
Ioannis Antonoglou; Matthew Blaschko; Arthur Gretton,

L003 Ensembles of limited dependence Bayesian Classifiers for Protein-Protein Interaction prediction from Sequence Data
Jana Kludas; Juho Rousu

L004 Approximate Models and Robust Decisions
James Watson; Chris Holmes

L005 Decision-theoretic justifications for Bayesian hypothesis testing using credible sets
Måns Thulin

L006 Bayesian nonparametric regression via probabilistic ranking models
Tristan Gray-Davies; Chris Holmes; Francois Caron

L007 A parameter-free clustering prior based on partition entropy
Fritz Obermeyer; Jonathan Glidden

L008 A performance comparison of generative and discriminative models in causal and anticausal problems
Patrick Blöbaum; Shohei Shimizu; Takashi Washio

L009 Improving Direct Contact Analysis in the Third Dimension
Marcin Skwark; Christoph Feinauer

L010 Joint Detection of Multiple Causal Disease Variants
Ingileif Hallgrimsdottir; Brielin Brown

L011 Trainable COSFIRE Filters for keypoint detection, object localization, and pattern recognition
George Azzopardi; Nicolai Petkov

L012 Opening the way for deep Gaussian processes on massive data
Andreas Damianou; James Hensman; Neil Lawrence

L013 Uniform random generation of large acyclic digraphs
Jack Kuipers; Giusi Moffa

L014 Reconstruction quality of a biological network when its constituting elements are partially observed
Victor Picheny; Jimmy Vandel; Matthieu Vignes; Nathalie Villa-Vialaneix

L016 Distributed parameter estimation of discrete hierarchical models via marginal likelihoods
Helene Massam; Nanwei Wang

L017 Distributed Coordinate Descent for L1-regularized Logistic Regression
Ilya Trofimov; Alexander Genkin

L018 A Problem with the Use of Cross-Validation for Selecting among Multilevel Models
Wei Wang; Andrew Gelman

L019 A Bayesian Approach to Generalized Ensemble Markov Chain Monte Carlo
Jes Frellsen; Ole Winther; Jesper Ferkinghoff-Borg

L020 The Role of Dimensionality Reduction in Linear Classification
Weiran Wang; Miguel Carreira-Perpinan

L021 Generative Artificial Neural Networks (GANNs) for nonlinear regression with prediction uncertainty. Application to astrophysical parameter estimation using Gaia RVS spectra
Diego Fustes Villadóniga; Minia Manteiga; Carlos Dafonte; Ana Ulla

L022 LASS: A Simple Assignment Model with Laplacian Smoothing
Miguel Carreira-Perpinan; Weiran Wang

L023 A Generalized Method of Moments Algorithm for Learning Probabilistic Context-Free Grammars
Gabi Teodoru

L024 New Guarantees for Alternating Minimization
Sujay Sanghavi

L025 A diagnostic model for autoimmune disease
Guðný Árnadóttir; Dagrún Jónasdóttir; Björn Lúðvíksson; Bjarni Halldórsson

Poster Session 2, Thursday, 24 April

Poster layout, Poster session 1, Thursday, 24 April
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Active learning

P069 Characterizing EVOI-Sufficient k-Response Query Sets in Decision Problems
Robert Cohn; Satinder Singh; Edmund Durfee

P070 Active Area Search via Bayesian Quadrature
Yifei Ma; Roman Garnett; Jeff Schneider

P071 Bayesian Multi-Scale Optimistic Optimization
Ziyu Wang; Babak Shakibi; Lin Jin; Nando de Freitas

P072 On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning
Matthew Hoffman; Bobak Shahriari; Nando de Freitas

P073 Near Optimal Bayesian Active Learning for Decision Making
Shervin Javdani; Yuxin Chen; Amin Karbasi; Andreas Krause; Drew Bagnell; Siddhartha Srinivasa

P074 An Analysis of Active Learning with Uniform Feature Noise
Aaditya Ramdas; Barnabas Poczos; Aarti Singh; Larry Wasserman

Approximate inference

P075 Loopy Belief Propagation in the Presence of Determinism
David Smith; Vibhav Gogate

P076 Black Box Variational Inference
Rajesh Ranganath; Sean Gerrish; David Blei

P077 Expectation Propagation for Likelihoods Depending on an Inner Product of Two Multivariate Random Variables
Tomi Peltola; Pasi Jylänki; Aki Vehtari

P078 Efficient Lifting of MAP LP Relaxations Using k-Locality
Martin Mladenov; Kristian Kersting; Amir Globerson

Asymptotics, consistency

P079 Sparsity and the truncated l^2-norm
Lee Dicker

P080 Non-Asymptotic Analysis of Relational Learning with One Network
Peng He; Changshui Zhang

P081 Fast Distribution To Real Regression
Junier Oliva; Willie Neiswanger; Barnabas Poczos; Jeff Schneider; Eric Xing

P082 Gaussian Copula Precision Estimation with Missing Values
Huahua Wang; Farideh Fazayeli; Soumyadeep chatterjee; Arindam Banerjee


P083 On Estimating Causal Effects based on Supplemental Variables
Takahiro Hayashi; Manabu Kuroki

Computer vision

P084 Visual Boundary Prediction: A Deep Neural Prediction Network and Quality Dissection
Jyri Kivinen; Chris Williams; Nicolas Heess

Deep learning

P085 Avoiding pathologies in very deep networks
David Duvenaud; Oren Rippel; Ryan Adams; Zoubin Ghahramani

P086 To go deep or wide in learning?
Gaurav Pandey; Ambedkar Dukkipati

Feature selection and clustering

P087 An LP for Sequential Learning Under Budgets
Joseph Wang; Kirill Trapeznikov; Venkatesh Saligrama

P088 Jointly Informative Feature Selection
Leonidas Lefakis; Francois Fleuret

P089 An Efficient Algorithm for Large Scale Compressive Feature Learning
Hristo Paskov; John Mitchell; Trevor Hastie

P090 Cluster Canonical Correlation Analysis
Nikhil Rasiwasia; Dhruv Mahajan; Vijay Mahadevan; Gaurav Aggarwal

Information theory

P091 Bias Reduction and Metric Learning for Nearest-Neighbor Estimation of Kullback-Leibler Divergence
Yung-Kyun Noh; Masashi Sugiyama; Song Liu; Marthinus C. du Plessis; Frank Chongwoo Park; Daniel D. Lee

Kernel methods

P092 Learning and Evaluation in Presence of Non-i.i.d. Label Noise
Nico Görnitz; Anne Porbadnigk; Alexander Binder; Claudia Sannelli; Mikio Braun; Klaus-Robert Mueller; Marius Kloft

P093 Efficient Algorithms and Error Analysis for the Modified Nystrom Method
Shusen Wang; Zhihua Zhang

P094 A Geometric Algorithm for Scalable Multiple Kernel Learning
John Moeller; Parasaran Raman; Suresh Venkatasubramanian; Avishek Saha

P095 Recovering Distributions from Gaussian RKHS Embeddings
Motonobu Kanagawa; Kenji Fukumizu

Large margin methods

P096 A New Perspective on Learning Linear Separators with Large L_q L_p Margins
Maria-Florina Balcan; Christopher Berlind

Large scale learning

P097 In Defense of Minhash over Simhash
Anshumali Shrivastava; Ping Li

P098 Online Passive-Aggressive Algorithms for Non-Negative Matrix Factorization and Completion
Mathieu Blondel; Yotaro Kubo; Ueda Naonori

P099 Fugue: Slow-Worker-Agnostic Distributed Learning for Big Models on Big Data
Abhimanu Kumar; Alex Beutel; Qirong Ho; Eric Xing

P100 Scaling Graph-based Semi Supervised Learning to Large Number of Labels Using Count-Min Sketch
Partha Talukdar; William Cohen

Matrix and tensor factorization

P101 Efficient Distributed Topic Modeling with Provable Guarantees
Weicong Ding; Mohammad Rohban; Prakash Ishwar; Venkatesh Saligrama

P102 Robust Stochastic Principal Component Analysis
John Goes; Teng Zhang; Raman Arora; Gilad Lerman

P103 Scalable Collaborative Bayesian Preference Learning
Mohammad Emtiyaz Khan; Young Jun Ko; Matthias Seeger

P104 Latent Gaussian Models for Topic Modeling
Changwei Hu; Eunsu Ryu; David Carlson; Yingjian Wang; Lawrence Carin

P105 Scalable Variational Bayesian Matrix Factorization with Side Information
Yong-Deok Kim; Seungjin Choi

P106 Collaborative Ranking for Local Preferences
Berk Kapicioglu; David Rosenberg; Robert Schapire; Tony Jebara

P107 Estimating Dependency Structures for non-Gaussian Components with Linear and Energy Correlations
Hiroaki Sasaki; Michael Gutmann; Hayaru Shouno; Aapo Hyvarinen

Bayesian nonparametrics

P008 A Non-parametric Conditional Factor Regression Model for Multi-Dimensional Input and Response
Ava Bargi; Richard Yi Xu; Zoubin Ghahramani; Massimo Piccardi

Monte Carlo methods

P108 LAMORE: A Stable, Scalable Approach to Latent Vector Autoregressive Modeling of Categorical Time Series
Yubin Park; Carlos Carvalho; Joydeep Ghosh

P109 Accelerating ABC methods using Gaussian processes
Richard Wilkinson

P110 Scaling Nonparametric Bayesian Inference via Subsample-Annealing
Fritz Obermeyer; Jonathan Glidden; Eric Jonas

P111 Generating Efficient MCMC Kernels from Probabilistic Programs
Lingfeng Yang; Patrick Hanrahan; Noah Goodman

P112 Approximate Slice Sampling for Bayesian Posterior Inference
Christopher DuBois; Anoop Korattikara; Max Welling; Padhraic Smyth

P113 A Level-set Hit-and-run Sampler for Quasi-Concave Distributions
Shane Jensen; Dean Foster

P114 A New Approach to Probabilistic Programming Inference
Frank Wood; Jan Willem van de Meent; Vikash Mansinghka

P115 Spoofing Large Probability Mass Functions to Improve Sampling Times and Reduce Memory Costs
Jon Parker; Hans Engler

Online learning and statistical learning theory

P116 Robust Forward Algorithms via PAC-Bayes and Laplace Distributions
Asaf Noy; Koby Crammer

P117 Doubly Aggressive Selective Sampling Algorithms for Classification
Koby Crammer

P118 Selective Sampling with Drift
Edward Moroshko; Koby Crammer

P119 Improved Bounds for Online Learning Over the Permutahedron and Other Ranking Polytopes
Nir Ailon

Sparse estimation, compressed sensing

P056 Algebraic Reconstruction Bounds and Explicit Inversion for Phase Retrieval at the Identifiability Threshold
Franz Király; Martin Ehler

Structured prediction

P120 Efficiently Enforcing Diversity in Multi-Output Structured Prediction
Abner Guzman-Rivera; Pushmeet Kohli; Dhruv Batra, Virginia Tech; Rob Rutenbar

P121 Learning Structured Models with the AUC Loss and Its Generalizations
Nir Rosenfeld; Ofer Meshi; Danny Tarlow; Amir Globerson

Late-breaking Posters

L015 Comparing Binary Hamiltonian Monte Carlo and Gibbs Sampling for Training Discrete MRFs with Stochastic Approximation
Hanchen Xiong; Sandor Szedmak; Justus Piater

L026 Asymmetric Clustering Index for partitions validation
Marek Śmieja; Dawid Warszycki; Jacek Tabor; Andrzej Bojarski

Additionally, MLSS posters will be shown in the Thursday poster session.