AISTATS*2012 Accepted Papers

Note: the corresponding author is indicated by an asterisk (*).

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Accepted oral presentations

Using More Data to Speed-up Training Time Shai Shalev-Shwartz; Ohad Shamir*; Eran Tromer
Structured Sparse Canonical Correlation Analysis Xi Chen*; Liu Han; Jaime Carbonell
Lightning-speed Structure Learning of Nonlinear Continuous Networks Gal Elidan*
Hierarchical Latent Dictionaries for Models of Brain Activation Alona Fyshe*; Emily Fox; David Dunson; Tom Mitchell
CorrLog: Correlated Logistic Models for Joint Prediction of Multiple Labels Wei Bian*; Bo Xie; Dacheng Tao
Efficient Gaussian Process Inference for Short-Scale Spatio-Temporal Modeling Jaakko Luttinen*; Alexander Ilin
Factorized Asymptotic Bayesian Inference for Mixture Modeling Ryohei Fujimaki*; Satoshi Morinaga
Marginal Regression For Multitask Learning Mladen Kolar*; Han Liu
Maximum Margin Temporal Clustering Minh Hoai*; Fernando De la Torre
High-dimensional Sparse Inverse Covariance Estimation using Greedy Methods Christopher Johnson*; Ali Jalali; Pradeep Ravikumar
Online Incremental Feature Learning with Denoising Autoencoders Guanyu Zhou; Kihyuk Sohn; Honglak Lee*
Fast Learning Rate of Multiple Kernel Learning: Trade-Off between Sparsity and Smoothness Taiji Suzuki*; Masashi Sugiyama
Data dependent kernels in nearly-linear time Guy Lever*; Tom Diethe; John Shawe-Taylor
Minimax rates for homology inference Sivaraman Balakrishnan*; Alesandro Rinaldo; Don Sheehy; Aarti Singh; Larry Wasserman
Regression for sets of polynomial equations Franz Király*; Paul von Büenau; Jan Müller; Duncan Blythe; Frank Meinecke; Klaus-Robert Müller
Online Clustering with Experts Anna Choromanska; Claire Monteleoni*
Adaptive MCMC with Bayesian Optimization Nimalan Mahendran; Ziyu Wang*; Firas Hamze; Nando de Freitas
Classifier Cascade for Minimizing Feature Evaluation Cost Minmin Chen*; Zhixiang Xu; Kilian Weinberger; Olivier Chapelle; Dor Kedem
Minimax Rates of Estimation for Sparse PCA in High Dimensions Vincent Vu*; Jing Lei
Evaluation of marginal likelihoods via the density of states Michael Habeck*
Online-to-Confidence-Set Conversions and Application to Sparse Stochastic Bandits Yasin Abbasi-Yadkori*; David Pal; Csaba Szepesvari
Learning Fourier Sparse Set Functions Peter Stobbe*; Andreas Krause
A Bayesian Analysis of the Radioactive Releases of Fukushima Ryota Tomioka*; Morten Mørup
A Differentially Private Stochastic Gradient Descent Algorithm for Multiparty Classification Arun Rajkumar; Shivani Agarwal*

Accepted poster presentations

Infinite-Dimensional Kalman Filtering Approach to Spatio-Temporal Gaussian Process Regression Simo Särkkä*; Jouni Hartikainen
On Estimation and Selection for Topic Models Matt Taddy*
Generalized Optimal Reverse Prediction Martha White*; Dale Schuurmans
Multi-label Subspace Ensemble Tianyi Zhou*; Dacheng Tao
On a Connection between Maximum Variance Unfolding, Shortest Path Problems and IsoMap Alexander Paprotny*; Jochen Garcke
Copula Network Classifiers (CNCs) Gal Elidan*
Part & Clamp: Efficient Structured Output Learning Patrick Pletscher*; Cheng Soon Ong
Efficient Sampling from Combinatorial Space via Bridging Dahua Lin*; John Fisher
Minimax hypothesis testing for curve registration Olivier Collier*
Kernel Topic Models Philipp Hennig*; David Stern; Ralf Herbrich; Thore Graepel
Efficient and Exact MAP-MRF Inference using Branch and Bound Min Sun*; murali telaprolu; Honglak Lee; silvio Savarese
Protocols for Learning Classifiers on Distributed Data Hal Daume III; Jeff Phillips; Avishek Saha*; Suresh Venkatasubramanian
Variable Selection for Gaussian Graphical Models Jean Honorio*; Dimitris Samaras; Irina Rish; Guillermo Cecchi
Statistical Optimization in High Dimensions Huan Xu*; Constantine Caramanis; Shie Mannor
Robust Multi-task Regression with Grossly Corrupted Observations Huan Xu*; Chenlei Leng
Deep Learning Made Easier by Linear Transformations in Perceptrons Tapani Raiko*; Harri Valpola; Yann LeCun
Generic Methods for Optimization-Based Modeling Justin Domke*
Forward Basis Selection for Sparse Approximation over Dictionary Xiaotong Yuan*; Shuicheng Yan
Closed-Form Entropy Limits - A Tool to Monitor Likelihood Optimization of Probabilistic Generative Models Jörg Lücke*; Marc Henniges
Consistency and Rates for Clustering with DBSCAN Bharath Sriperumbudur; Ingo Steinwart*
A Variance Minimization Criterion to Active Learning on Graphs Ming Ji*; Jiawei Han
Domain Adaptation: A Small Sample Statistical Approach Ruslan Salakhutdinov*; Sham Kakade; Dean Foster
Stochastic Bandit Based on Empirical Moments Junya Honda*; Akimichi Takemura
Regularization Paths with Guarantees for Convex Semidefinite Optimization Joachim Giesen; Martin Jaggi; Soeren Laue*
Random Feature Maps for Dot Product Kernels Purushottam Kar*; Harish Karnick
There's a Hole in My Data Space: Piecewise Predictors for Heterogeneous Learning Problems Ofer Dekel*; Ohad Shamir
Sparse Additive Machine Tuo Zhao*; Han Liu
Locality Preserving Feature Learning Quanquan Gu*; Marina Danilevsky; Zhenhui Li; Jiawei Han
Nonlinear low-dimensional regression using auxiliary coordinates Weiran Wang; Miguel Carreira-Perpinan*
Scalable Inference on Kingman's Coalescent using Pair Similarity Dilan Gorur*; Levi Boyles; Max Welling
Deep Boltzmann Machines as Feed-Forward Hierarchies Grégoire Montavon*; Mikio Braun; Klaus-Robert Müller
Gaussian Processes for time-marked time-series data John Cunningham*; Zoubin Ghahramani; Carl Rasmussen
Wilks' phenomenon and penalized likelihood-ratio test for nonparametric curve registration Arnak Dalalyan*; Olivier Collier
A metric learning perspective of SVM: on the relation of LMNN and SVM Huyen Do*; Alexandros Kalousis; Jun WANG; Adam Woznica
Markov Logic Mixtures of Gaussian Processes: Towards Machines Reading Regression Data Martin Schiegg*; Marion Neumann; Kristian Kersting
Optimistic planning for Markov decision processes Lucian Busoniu*; Remi Munos
Bayesian Quadrature for Ratios Michael Osborne*; Roman Garnett; Stephen Roberts; Christopher Hart; Suzanne Aigrain; Neale Gibson
Bandit Theory meets Compressed Sensing for high dimensional Stochastic Linear Bandit Alexandra Carpentier*; Remi Munos
Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing Antoine Bordes*; Xavier Glorot; Jason Weston; Yoshua Bengio
Adaptive Metropolis with Online Relabeling Rémi Bardenet*; Olivier Cappé; Gersende Fort; Balázs Kégl
Fast Variational Mode-Seeking Bo Thiesson*; Jingu Kim
Max-Margin Min-Entropy Models Kevin Miller; M. Pawan Kumar; Ben Packer*; Danny Goodman; Daphne Koller
Exchangeability Characterizes Optimality of Sequential Normalized Maximum Likelihood and Bayesian Prediction with Jeffreys Prior Fares Hedayati*; Peter Bartlett
Lifted Variable Elimination with Arbitrary Constraints Nima Taghipour*; daan Fierens; Jesse Davis; Hendrik Blockeel
Complexity of Bethe Approximation Jinwoo Shin*
Message-Passing Algorithms for MAP Estimation Using DC Programming Akshat Kumar*; Shlomo Zilberstein; Marc Toussaint
Detecting Network Cliques with Radon Basis Pursuit Xiaoye Jiang*; Yuan Yao; Han Liu; Leonidas Guibas
Multiple Texture Boltzmann Machines Jyri Kivinen*; Christopher Williams
Nonparametric Estimation of Conditional Information and Divergences Barnabas Poczos*; Jeff Schneider
Deterministic Annealing for Semi-Supervised Structured Output Learning Paramveer Dhillon*; Sathiya Keerthi; Kedar Bellare; Olivier Chapelle; Sundararajan Sellamanickam
Fast interior-point inference in high-dimensional sparse, penalized state-space models Eftychios Pnevmatikakis*; Liam Paninski
Exact Subspace Segmentation and Outlier Detection by Low-Rank Representation Guangcan Liu; Huan Xu*; Shuicheng Yan
Graphlet decomposition of a weighted network Hossein Azari Soufiani; Edoardo M. Airoldi*
Efficient Distributed Linear Classification Algorithms via the Alternating Direction Method of Multipliers Caoxie Zhang; Honglak Lee*; Kang Shin
A Composite Likelihood View for Multi-Label Classification Yi Zhang*; Jeff Schneider
Subset Infinite Relational Models Katsuhiko Ishiguro*; Naonori Ueda; Hiroshi Sawada
Bayesian Classifier Combination Hyun-Chul Kim*; Zoubin Ghahramani
Globally Optimizing Graph Partitioning Problems Using Message Passing Elad Mezuman*; Yair Weiss
Learning Low-order Models for Enforcing High-order Statistics Patrick Pletscher*; Pushmeet Kohli
Bayesian Group Factor Analysis Seppo Virtanen; Arto Klami*; Suleiman Khan; Samuel Kaski
Statistical test for consistent estimation of causal effects in linear non-Gaussian models Doris Entner*; Patrik Hoyer; Peter Spirtes
History-alignment models for bias-aware prediction of virological response to HIV combination therapy Jasmina Bogojeska*; Daniel Stöckel; Maurizio Zazzi; Rolf Kaiser; Francesca Incardona; Michal Rosen-Zvi; Thomas Lengauer
Semiparametric Pseudo-Likelihood Estimation in Markov Random Fields Antonino Freno*
Bayesian regularization of non-homogeneous dynamic Bayesian networks by globally coupling interaction parameters Marco Grzegorzyk*; Dirk Husmeier
Exploiting Unrelated Tasks in Multi-Task Learning bernardino Romera Paredes; Andreas Argyriou; Nadia Berthouze; Massimiliano Pontil*
Causality with Gates John Winn*
No Internal Regret via Neighborhood Watch Dean Foster; Alexander Rakhlin*
Controlling Selection Bias in Causal Inference Elias Bareinboim*; Judea Pearl
Multi-armed Bandit Problems with History Pannagadatta Shivaswamy*; Thorsten Joachims
A Family of MCMC Methods on Implicitly Defined Manifolds Marcus Brubaker*; Mathieu Salzmann; Raquel Urtasun
The adversarial stochastic shortest path problem with unknown transition probabilities Gergely Neu*; Andras Gyorgy; Csaba Szepesvari
Fast, Exact Model Selection and Permutation Testing for l2-Regularized Logistic Regression Bryan Conroy*; Paul Sajda
Approximate Inference by Intersecting Semidefinite Bound and Local Polytope Jian Peng*; Tamir Hazan; Nathan Srebro; Jinbo Xu
An Autoregressive Approach to Nonparametric Hierarchical Dependent Modeling Zhihua Zhang*; Dakan Wang; Edward Chang
Perturbation based Large Margin Approach for Ranking Eunho Yang*; Ambuj Tewari; Pradeep Ravikumar
A Simple Geometric Interpretation of SVM using Stochastic Adversaries Roi Livni; Koby Crammer; Amir Globerson*
Lifted Linear Programming Martin Mladenov*; Babak Ahmadi; Kristian Kersting
Error bounds for Kernel Fisher Linear Discriminant in Gaussian Hilbert space Robert Durrant*; Ata Kaban
Krylov Subspace Descent for Deep Learning Oriol Vinyals*; Daniel Povey
Scalable Personalization of Long-Term Physiological Monitoring: Active Learning Methodologies for Epileptic Seizure Onset Detection Guha Balakrishnan; Zeeshan Syed*
Informative Priors for Markov Blanket Discovery Adam Pocock*; Mikel Lujan; Gavin Brown
On Nonparametric Guidance for Learning Autoencoder Representations Jasper Snoek*; Ryan Adams; Hugo Larochelle
On Bayesian Upper Confidence Bounds for Bandit Problems Emilie Kaufmann*; Olivier Cappé; Aurélien Garivier
Bayesian Comparison of Machine Learning Algorithms on Single and Multiple Datasets Alexandre Lacoste*; Francois Laviolette; Mario Marchand
Local Anomaly Detection Venkatesh Saligrama*; Manqi Zhao
A General Framework for Structured Sparsity via Proximal Optimization luca Baldassarre*; Jean Morales; Andreas Argyriou; Massimiliano Pontil
Universal Measurement Bounds for Structured Sparse Signal Recovery Nikhil Rao*; Ben Recht; Robert Nowak
A Stick-Breaking Likelihood for Categorical Data Analysis with Latent Gaussian Models Mohammad Khan*; Shakir Mohamed; Benjamin Marlin; Kevin Murphy
Learning from Weak Teachers Ruth Urner*; Shai Ben David; Ohad Shamir
Beyond Logarithmic Bounds in Online Learning Francesco Orabona*; Nicolò Cesa-Bianchi; Claudio Gentile
High-Dimensional Structured Feature Screening Using Binary Markov Random Fields Jie Liu*; Chunming Zhang; Catherine McCarty; Peggy Peissig; Elizabeth Burnside; David Page
Factorized Diffusion Map Approximation Saeed Amizadeh*; Hamed Valizadegan; Milos Hauskrecht
Contextual Bandit Learning with Predictable Rewards Alekh Agarwal*; Miroslav Dudik; Satyen Kale; John Langford; Robert Schapire
A Hybrid Neural Network-Latent Topic Model Li Wan*; Leo Zhu; Rob Fergus
Stick-Breaking Beta Processes and the Poisson Process John Paisley*; David Blei; Michael Jordan
On Bisubmodular Maximization Ajit Singh*; Andrew Guillory; Jeff Bilmes
Bayesian Inference for Change Points in Dynamical Systems with Reusable States - a Chinese Restaurant Process Approach Florian Stimberg*; Andreas Ruttor; Manfred Opper
A Two-Graph Guided Multi-task Lasso Approach for eQTL Mapping Xiaohui Chen; Xinghua Shi*; Xing Xu; Zhiyong Wang; Ryan Mills; Charles Lee; jinbo Xu
Information Theoretic Model Validation for Spectral Clustering Morteza Haghir Chehreghani*; Alberto Giovanni Busetto; Joachim M. Buhmann
Hierarchical Relative Entropy Policy Search Christian Daniel*; Gerhard Neumann; Jan Peters
Online Clustering of Processes Azadeh Khaleghi*; Daniil Ryabko; Jeremie Mary; Philippe Preux
Movement Segmentation and Recognition for Imitation Learning Franziska Meier*; Evangelos Theodorou; Stefan Schaal
Beta-Negative Binomial Process and Poisson Factor Analysis Mingyuan Zhou*; Lauren Hannah; David Dunson; Lawrence Carin
On Average Reward Policy Evaluation in Infinite-State Partially Observable Systems Yuri Grinberg*; Doina Precup
On Sparse, Spectral and Other Parameterizations of Binary Probabilistic Models David Buchman*; Mark Schmidt; Shakir Mohamed; David Poole; nando de Freitas
Scaling up Kernel SVM on Limited Resources: A Low-rank Linearization Approach Kai Zhang*; Liang Lan; Zhuang Wang; Fabian Moerchen
UPAL: Unbiased Pool Based Active Learning Ravi Ganti*; Alexander Gray
High-Rank Matrix Completion Brian Eriksson; Laura Balzano; Robert Nowak*
Lifted coordinate descent for learning with trace-norm regularization Miroslav Dudik*; Zaid Harchaoui; Jerome Malick
Testing for Membership to the IFRA and the NBU Classes of Distributions Radhendushka Srivastava*; Ping Li; Debasis Sengupta
A Nonparametric Bayesian Model for Multiple Clustering with Overlapping Feature Views Donglin Niu; Jennifer Dy*; Zoubin Ghahramani
Primal-Dual methods for sparse constrained matrix completion Yu Xin*; Tommi Jaakkola
Fast Variational Bayesian Inference for Non-Conjugate Matrix Factorization Models Matthias Seeger*; Guillaume Bouchard
Efficient Hypergraph Clustering Marius Leordeanu*; Cristian Sminchisescu
Sparse Higher-Order Principal Components Analysis Genevera Allen*
Flexible Martingale Priors for Deep Hierarchies Jacob Steinhardt*; Zoubin Ghahramani
Age-Layered Expectation Maximization for Parameter Learning in Bayesian Networks Avneesh Saluja; Priya Krishnan Sundararajan*; Ole J Mengshoel
Constrained 1-Spectral Clustering Syama Sundar Rangapuram*; Matthias Hein
Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation J. Zico Kolter*; Tommi Jaakkola
Quilting Stochastic Kronecker Product Graphs to Generate Multiplicative Attribute Graphs Hyokun Yun*; S V N Vishwanathan
Structured Output Learning with High Order Loss Functions Daniel Tarlow*; Richard Zemel
Joint Estimation of Structured Sparsity and Output Structure in Multiple-Output Regression via Inverse-Covariance Regularization Kyung-Ah Sohn; Seyoung Kim*
Low rank continuous-space graphical models Carl Smith*; Frank Wood; Liam Paninski
Transductive Learning of Structural SVMs via Prior Knowledge Constraints Chun-Nam Yu*
Sample Complexity of Composite Likelihood Joseph Bradley*; Carlos Guestrin
Sparsistency of the Edge Lasso over Graphs James Sharpnack*; Aarti Singh; Alessandro Rinaldo
SpeedBoost: Anytime Prediction with Uniform Near-Optimality Alex Grubb*; Drew Bagnell
Randomized Optimum Models for Structured Prediction Daniel Tarlow*; Ryan Adams; Richard Zemel
Active Learning from Multiple Knowledge Sources Yan Yan; Romer Rosales*; Glenn Fung; Faisal Farooq; Bharat Rao; Jennifer Dy
Multiresolution Deep Belief Networks Yichuan Tang*; Abdel-rahman Mohamed
Discriminative Mixtures of Sparse Latent Fields for Risk Management Felix Agakov*; Peter Orchard; Amos Storkey
Probabilistic acoustic tube: a probabilistic generative model of speech for speech analysis/synthesis Zhijian Ou*; Yang Zhang
Memory-efficient inference in dynamic graphical models using multiple cores Galen Andrew*; Jeff Bilmes