[Artificial Intelligence and Statistics Logo] Artificial Intelligence and Statistics 2015


AISTATS 2015 Poster Sessions

Poster Session 1, May 10 (Sunday) 19:30 - 22:00

S01 Revisiting the Limits of MAP Inference by MWSS on Perfect Graphs
Adrian Weller

S02 Graph Approximation and Clustering on a Budget
Ethan Fetaya, Ohad Shamir, Shimon Ullman

S03 Nonparametric Bayesian Factor Analysis for Dynamic Count Matrices
Ayan Acharya, Joydeep Ghosh, Mingyuan Zhou

S04 Feature Selection for Linear SVM with Provable Guarantees
Saurabh Paul, Malik Magdon-Ismail, Petros Drineas

S05 Implementable confidence sets in high dimensional regression
Alexandra Carpentier

S06 Model Selection for Topic Models via Spectral Decomposition
Dehua Cheng, Xinran He, Yan Liu

S07 Robust Cost Sensitive Support Vector Machine
Shuichi Katsumata, Akiko Takeda

S08 Deeply-Supervised Nets
Chen-Yu Lee, Saining Xie, Patrick Gallagher, Zhengyou Zhang, Zhuowen Tu

S09 A Greedy Homotopy Method for Regression with Nonconvex Constraints
Fabian Wauthier, Peter Donnelly

S10 Robust sketching for multiple square-root LASSO problems
Vu Pham, Laurent El Ghaoui

S11 Tight Regret Bounds for Stochastic Combinatorial Semi-Bandits
Branislav Kveton, Zheng Wen, Azin Ashkan, Csaba Szepesvari

S12 On Estimating L_2^2 Divergence
Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos, Larry Wasserman

S13 Estimating the accuracies of multiple classifiers without labeled data
Ariel Jaffe, Boaz Nadler, Yuval Kluger

S14 Inference of Cause and Effect with Unsupervised Inverse Regression
Eleni Sgouritsa, Dominik Janzing, Philipp Hennig, Bernhard Schölkopf

S15 Learning Efficient Anomaly Detectors from K-NN Graphs
Jonathan Root, Jing Qian, Venkatesh Saligrama

S16 Calibration of conditional composite likelihood for Bayesian inference on Gibbs random fields
Julien Stoehr, Nial Friel

S17 Scalable Optimization of Randomized Operational Decisions in Adversarial Classification Settings
Bo Li, Yevgeniy Vorobeychik

S18 Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering
Simon Lacoste-Julien, Fredrik Lindsten, Francis Bach

S19 Cross-domain recommendation without shared users or items by sharing latent vector distributions
Tomoharu Iwata, Takeuchi Koh

S20 On the High Dimensional Power of a Linear-Time Two Sample Test under Mean-shift Alternatives
Sashank Reddi, Aaditya Ramdas, Barnabas Poczos, Aarti Singh, Larry Wasserman

S21 Active Pointillistic Pattern Search
Yifei Ma, Dougal Sutherland, Roman Garnett, Jeff Schneider

S22 A Simple Homotopy Algorithm for Compressive Sensing
Lijun Zhang, Nanjing University; Tianbao Yang, University of Iowa; Rong Jin, Michigan State University; Zhi-Hua Zhou, Nanjing University

S23 A Consistent Method for Graph Based Anomaly Localization
Satoshi Hara, IBM Research Tokyo; Tetsuro Morimura, IBM Research Tokyo; Toshihiro Takahashi, IBM Research Tokyo; Hiroki Yanagisawa, IBM Research Tokyo; Taiji Suzuki, Tokyo Institute of Technology

S24 Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence
Nihar Shah, Sivaraman Balakrishnan, Joseph Bradley, Abhay Parekh, Kannan Ramchandran, Martin Wainwright

S25 Minimizing Nonconvex Non-Separable Functions
Yaoliang Yu, Xun Zheng, Micol Marchetti-Bowick, Eric Xing

S26 Power-Law Graph Cuts
Xiangyang Zhou, Jiaxin Zhang, Brian Kulis

S27 Inferring Block Structure of Graphical Models in Exponential Families
Siqi Sun, Hai Wang, Jinbo Xu

S28 Maximally Informative Hierarchical Representations of High-Dimensional Data
Greg Ver Steeg, Aram Galstyan

S29 Exploiting Symmetries to Construct Efficient MCMC Algorithms With an Application to SLAM
Roshan Shariff, András György, Csaba Szepesvari

S30 The Bayesian Echo Chamber: Modeling Social Influence via Linguistic Accommodation
Fangjian Guo, Charles Blundell, Hanna Wallach, Katherine Heller

S31 DART: Dropouts meet Multiple Additive Regression Trees
Rashmi Korlakai Vinayak, Ran Gilad-Bachrach

S32 Efficient Training of Structured SVMs via Soft Constraints
Ofer Meshi, Nathan Srebro, Tamir Hazan

S33 Convex Multi-Task Learning by Clustering
Aviad Barzilai, Koby Crammer

S34 Reliable and Scalable Variational Inference for the Hierarchical Dirichlet Process
Michael Hughes, Dae Il Kim, Erik Sudderth

S35 A Bayes consistent 1-NN classifier
Aryeh Kontorovich, Roi Weiss

S36 Infinite Edge Partition Models for Overlapping Community Detection and Link Prediction
Mingyuan Zhou

S37 Submodular Point Processes with Applications to Machine learning
Rishabh Iyer, Jeffrey Bilmes

S38 Spectral Gap Error Bounds for Improving CUR Matrix Decomposition and the Nystr\"{o}m Method
David Anderson, Simon Du, Michael Mahoney, Christopher Melgaard, Kunming Wu, Ming Gu

S39 Similarity Learning for High-Dimensional Sparse Data
Kuan Liu, Aurélien Bellet, Fei Sha

S40 Bayesian Hierarchical Clustering with Exponential Family: Small-Variance Asymptotics and Reducibility
Juho Lee, Seungjin Choi

S41 Dimensionality estimation without distances
Matthäus Kleindessner, Ulrike von Luxburg

S42 Symmetric Iterative Proportional Fitting
Sven Kurras

S43 On Anomaly Ranking and Excess-Mass Curves
Nicolas Goix, Anne Sabourin, Stéphan Clémençon

S44 Predicting Preference Reversals via Gaussian Process Uncertainty Aversion
Rikiya Takahashi, Tetsuro Morimura

S45 State Space Methods for Efficient Inference in Student-t Process Regression
Arno Solin, Simo Särkkä

S46 Direct Density-Derivative Estimation and Its Application in KL-Divergence Approximation
Hiroaki Sasaki, Yung-Kyun Noh, Masashi Sugiyama

S47 Sensor Selection for Crowdsensing Dynamical Systems
Francois Schnitzler, Jia Yuan Yu, Shie Mannor

S48 Latent feature regression for multivariate count data
Arto Klami, Abhishek Tripathi, Johannes Sirola, Lauri Väre, Frederic Roulland

S49 Learning Deep Sigmoid Belief Networks with Data Augmentation
Zhe Gan, Ricardo Henao, David Carlson, Lawrence Carin

Poster Session 2, May 11 (Monday) 19:30 - 22:00

M01 Multi-Manifold Modeling in Non-Euclidean spaces
Xu Wang, Konstantinos Slavakis, Gilad Lerman

M02 On Approximate Non-submodular Minimization via Tree-Structured Supermodularity
Yoshinobu Kawahara, Rishabh Iyer, Jeffrey Bilmes

M03 Majorization-Minimization for Manifold Embedding
Zhirong Yang, Jaakko Peltonen, Samuel Kaski

M04 Gaussian Processes for Bayesian hypothesis tests on regression functions
Alessio Benavoli, Francesca Mangili

M05 Variance Reduction via Antithetic Markov Chains
James Neufeld, Dale Schuurmans, Michael Bowling

M06 Consensus Message Passing for Layered Graphical Models
Varun Jampani, S. M. Ali Eslami, Daniel Tarlow, Pushmeet Kohli, John Winn

M07 A Sufficient Statistics Construction of Exponential Family Le ́vy Measure Densities for Nonparametric Conjugate Models
Robert Finn, Brian Kulis

M08 Filtered Search for Submodular Maximization with Controllable Approximation Bounds
Wenlin Chen, Yixin Chen, Kilian Weinberger

M09 Reactive bandits with attitude
Pedro Ortega, Kee-Eung Kim, Daniel Lee

M10 Gamma Processes, Stick-Breaking, and Variational Inference
Anirban Roychowdhury, Brian Kulis

M11 Learning from Data with Heterogeneous Noise using SGD
Shuang Song, Kamalika Chaudhuri, Anand Sarwate

M12 Toward Minimax Off-policy Value Estimation
Lihong Li, Remi Munos, Csaba Szepesvari

M13 Data modeling with the elliptical gamma distribution
Suvrit Sra, Reshad Hosseini, Lucas Theis, Matthias Bethge

M14 Streaming Variational Inference for Bayesian Nonparametric Mixture Models
Alex Tank, Nicholas Foti, Emily Fox

M15 Stochastic Structured Variational Inference
Matthew Hoffman, David Blei

M16 Preserving Privacy of Continuous High-dimensional Data with Minimax Filters
Jihun Hamm

M17 Global Multi-armed Bandits with Hölder Continuity
Onur Atan, Cem Tekin, Mihaela van der Schaar

M18 Scalable Variational Gaussian Process Classification
James Hensman, Alexander Matthews, Zoubin Ghahramani

M19 Modelling Policies in MDPs in Reproducing Kernel Hilbert Space
Guy Lever, Ronnie Stafford

M20 On Theoretical Properties of Sum-Product Networks
Robert Peharz, Sebastian Tschiatschek, Franz Pernkopf, Pedro Domingos

M21 Efficient Sparse Clustering of High-Dimensional Non-spherical Gaussian Mixtures
Martin Azizyan, Aarti Singh, Larry Wasserman

M22 A Topic Modeling Approach to Ranking
Weicong Ding, Prakash Ishwar, Venkatesh Saligrama

M23 Near-optimal max-affine estimators for convex regression
Gabor Balazs, András György, Csaba Szepesvari

M24 A Rate of Convergence for Mixture Proportion Estimation, with Application to Learning from Noisy Labels
Clayton Scott

M25 Falling Rule Lists
Fulton Wang, Cynthia Rudin

M26 Column Subset Selection with Missing Data via Active Sampling
Yining Wang, Aarti Singh

M27 One-bit Compressed Sensing with the k-Support Norm
Sheng Chen, Arindam Banerjee

M28 Conditional Restricted Boltzmann Machines for Multi-label Learning with Incomplete Labels
Xin Li, Feipeng Zhao, Yuhong Guo

M29 Metric recovery from directed unweighted graphs
Tatsunori Hashimoto, Yi Sun, Tommi Jaakkola

M30 Max-Margin Zero-Shot Learning for Multi-class Classification
Xin Li, Yuhong Guo

M31 A totally unimodular view of structured sparsity
Marwa El Halabi, Volkan Cevher

M32 Deep Exponential Families
Rajesh Ranganath, Linpeng Tang, Laurent Charlin, David Blei

M33 Sparse Solutions to Nonnegative Linear Systems and Applications
Aditya Bhaskara, Ananda Suresh, Morteza Zadimoghaddam

M34 Accurate and conservative estimates of MRF log-likelihood using reverse annealing
Yuri Burda, Roger Grosse, Ruslan Salakhutdinov

M35 Online Optimization : Competing with Dynamic Comparators
Ali Jadbabaie, Alexander Rakhlin, Shahin Shahrampour, Karthik Sridharan

M36 The Log-Shift Penalty for Adaptive Estimation of Multiple Gaussian Graphical Models
Yuancheng Zhu, Rina Foygel Barber

M37 Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields
Mark Schmidt, Reza Babanezhad, Mohamed Ahmed, Aaron Defazio, Ann Clifton, Anoop Sarkar

M38 Missing at Random in Graphical Models
Jin Tian

M39 WASP: Scalable Bayes via barycenters of subset posteriors
Sanvesh Srivastava, Volkan Cevher, Quoc Dinh, David Dunson

M40 Particle Gibbs for Bayesian Additive Regression Trees
Balaji Lakshminarayanan, Daniel Roy, Yee Whye Teh

M41 Scalable Nonparametric Multiway Data Analysis
Shandian Zhe, Zenglin Xu, Xinqi Chu, Yuan Qi, Youngja Park

M42 Trend Filtering on Graphs
Yu-Xiang Wang, James Sharpnack, Alex Smola, Ryan Tibshirani

M43 Efficient Second-Order Gradient Boosting for Conditional Random Fields
Tianqi Chen, Sameer Singh, Ben Taskar, Carlos Guestrin

M44 Preferential Attachment in Graphs with Affinities
Jay Lee, Manzil Zaheer, Stephan Günnemann, Alex Smola

M45 Learning of Non-Parametric Control Policies with High-Dimensional State Features
Herke Van Hoof, Jan Peters, Gerhard Neumann, TU Darmstadt

M46 Consistent Collective Matrix Completion under Joint Low Rank Structure
Suriya Gunasekar, Makoto Yamada, Dawei Yin, Yi Chang

M47 Compressed Sensing with Very Sparse Gaussian Random Projections
Ping Li, Cun-Hui Zhang

M48 The Loss Surfaces of Multilayer Networks
Anna Choromanska, MIkael Henaff, Michael Mathieu, Gerard Ben Arous, Yann LeCun

M49 Particle Gibbs with Ancestor Sampling for Probabilistic Programs
Jan Willem van de Meent, Hongseok Yang, Vikash Mansinghka, Frank Wood

M50 Predictive Inverse Optimal Control for Linear-Quadratic-Gaussian Systems
Xiangli Chen, Brian Ziebart

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