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ORCID18th AISTATS 2015: San Diego, California, USA
- Guy Lebanon, S. V. N. Vishwanathan:
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2015, San Diego, California, USA, May 9-12, 2015. JMLR Workshop and Conference Proceedings 38, JMLR.org 2015
Preface
- Guy Lebanon, S. V. N. Vishwanathan:
Preface.
Accepted Papers
- Ayan Acharya, Joydeep Ghosh, Mingyuan Zhou
:
Nonparametric Bayesian Factor Analysis for Dynamic Count Matrices. - Sreangsu Acharyya, Joydeep Ghosh:
Parameter Estimation of Generalized Linear Models without Assuming their Link Function. - David G. Anderson, Simon Du, Michael W. Mahoney, Christopher Melgaard, Kunming Wu, Ming Gu:
Spectral Gap Error Bounds for Improving CUR Matrix Decomposition and the Nyström Method. - Onur Atan, Cem Tekin, Mihaela van der Schaar:
Global Multi-armed Bandits with Hölder Continuity. - Martin Azizyan, Aarti Singh, Larry A. Wasserman:
Efficient Sparse Clustering of High-Dimensional Non-spherical Gaussian Mixtures. - Stephen H. Bach, Bert Huang, Lise Getoor:
Unifying Local Consistency and MAX SAT Relaxations for Scalable Inference with Rounding Guarantees. - Gábor Balázs, András György, Csaba Szepesvári:
Near-optimal max-affine estimators for convex regression. - Aviad Barzilai, Koby Crammer:
Convex Multi-Task Learning by Clustering. - Alessio Benavoli, Francesca Mangili:
Gaussian Processes for Bayesian hypothesis tests on regression functions. - Aditya Bhaskara, Ananda Theertha Suresh, Morteza Zadimoghaddam:
Sparse Solutions to Nonnegative Linear Systems and Applications. - Avradeep Bhowmik, Joydeep Ghosh, Oluwasanmi Koyejo:
Generalized Linear Models for Aggregated Data. - Yuri Burda, Roger B. Grosse, Ruslan Salakhutdinov:
Accurate and conservative estimates of MRF log-likelihood using reverse annealing. - David E. Carlson, Volkan Cevher, Lawrence Carin:
Stochastic Spectral Descent for Restricted Boltzmann Machines. - Alexandra Carpentier:
Implementable confidence sets in high dimensional regression. - Sougata Chaudhuri, Ambuj Tewari:
Online Ranking with Top-1 Feedback. - Sheng Chen, Arindam Banerjee:
One-bit Compressed Sensing with the k-Support Norm. - Tianqi Chen, Sameer Singh, Ben Taskar, Carlos Guestrin:
Efficient Second-Order Gradient Boosting for Conditional Random Fields. - Wenlin Chen, Yixin Chen, Kilian Q. Weinberger:
Filtered Search for Submodular Maximization with Controllable Approximation Bounds. - Xiangli Chen, Brian D. Ziebart:
Predictive Inverse Optimal Control for Linear-Quadratic-Gaussian Systems. - Yetian Chen, Lingjian Meng, Jin Tian:
Exact Bayesian Learning of Ancestor Relations in Bayesian Networks. - Dehua Cheng, Xinran He, Yan Liu:
Model Selection for Topic Models via Spectral Decomposition. - Anna Choromanska, Mikael Henaff, Michaël Mathieu, Gérard Ben Arous, Yann LeCun:
The Loss Surfaces of Multilayer Networks. - Alexandre Défossez, Francis R. Bach:
Averaged Least-Mean-Squares: Bias-Variance Trade-offs and Optimal Sampling Distributions. - Weicong Ding, Prakash Ishwar, Venkatesh Saligrama:
A Topic Modeling Approach to Ranking. - Marwa El Halabi, Volkan Cevher:
A totally unimodular view of structured sparsity. - Mehrdad Farajtabar, Manuel Gomez-Rodriguez, Mohammad Zamani, Nan Du, Hongyuan Zha, Le Song:
Back to the Past: Source Identification in Diffusion Networks from Partially Observed Cascades. - Ethan Fetaya, Ohad Shamir, Shimon Ullman:
Graph Approximation and Clustering on a Budget. - Robert Finn, Brian Kulis:
A Sufficient Statistics Construction of Exponential Family Levy Measure Densities for Nonparametric Conjugate Models. - Søren Frejstrup Maibing, Christian Igel:
Computational Complexity of Linear Large Margin Classification With Ramp Loss. - Zhe Gan, Ricardo Henao, David E. Carlson, Lawrence Carin:
Learning Deep Sigmoid Belief Networks with Data Augmentation. - Shuyang Gao, Greg Ver Steeg, Aram Galstyan:
Efficient Estimation of Mutual Information for Strongly Dependent Variables. - Nicolas Goix, Anne Sabourin, Stéphan Clémençon:
On Anomaly Ranking and Excess-Mass Curves. - José P. González-Brenes:
Modeling Skill Acquisition Over Time with Sequence and Topic Modeling. - Suriya Gunasekar, Makoto Yamada, Dawei Yin, Yi Chang:
Consistent Collective Matrix Completion under Joint Low Rank Structure. - Fangjian Guo, Charles Blundell, Hanna M. Wallach, Katherine A. Heller:
The Bayesian Echo Chamber: Modeling Social Influence via Linguistic Accommodation. - Jihun Hamm:
Preserving Privacy of Continuous High-dimensional Data with Minimax Filters. - Satoshi Hara, Tetsuro Morimura, Toshihiro Takahashi, Hiroki Yanagisawa, Taiji Suzuki:
A Consistent Method for Graph Based Anomaly Localization. - Tatsunori B. Hashimoto, Yi Sun, Tommi S. Jaakkola:
Metric recovery from directed unweighted graphs. - James Hensman, Alexander G. de G. Matthews, Zoubin Ghahramani:
Scalable Variational Gaussian Process Classification. - Matthew D. Hoffman, David M. Blei:
Stochastic Structured Variational Inference. - Michael C. Hughes, Dae Il Kim, Erik B. Sudderth:
Reliable and Scalable Variational Inference for the Hierarchical Dirichlet Process. - Tomoharu Iwata, Koh Takeuchi:
Cross-domain recommendation without shared users or items by sharing latent vector distributions. - Rishabh K. Iyer, Jeff A. Bilmes:
Submodular Point Processes with Applications to Machine learning. - Ali Jadbabaie, Alexander Rakhlin, Shahin Shahrampour, Karthik Sridharan:
Online Optimization : Competing with Dynamic Comparators. - Ariel Jaffe, Boaz Nadler, Yuval Kluger:
Estimating the accuracies of multiple classifiers without labeled data. - Kevin G. Jamieson, Sumeet Katariya, Atul Deshpande, Robert D. Nowak:
Sparse Dueling Bandits. - Varun Jampani, S. M. Ali Eslami, Daniel Tarlow, Pushmeet Kohli, John M. Winn:
Consensus Message Passing for Layered Graphical Models. - Shuichi Katsumata, Akiko Takeda:
Robust Cost Sensitive Support Vector Machine. - Yoshinobu Kawahara, Rishabh K. Iyer, Jeff A. Bilmes:
On Approximate Non-submodular Minimization via Tree-Structured Supermodularity. - Rajiv Khanna, Joydeep Ghosh, Russell A. Poldrack, Oluwasanmi Koyejo:
Sparse Submodular Probabilistic PCA. - Arto Klami, Abhishek Tripathi, Johannes Sirola, Lauri Väre, Frédéric Roulland:
Latent feature regression for multivariate count data. - Matthäus Kleindessner, Ulrike von Luxburg:
Dimensionality estimation without distances. - Aryeh Kontorovich, Roi Weiss:
A Bayes consistent 1-NN classifier. - Korlakai Vinayak Rashmi, Ran Gilad-Bachrach:
DART: Dropouts meet Multiple Additive Regression Trees. - Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabás Póczos, Larry A. Wasserman:
On Estimating L22 Divergence. - Volodymyr Kuleshov, Arun Tejasvi Chaganty, Percy Liang:
Tensor Factorization via Matrix Factorization. - Alex Kulesza, Nan Jiang, Satinder Singh:
Low-Rank Spectral Learning with Weighted Loss Functions. - Sven Kurras:
Symmetric Iterative Proportional Fitting. - Branislav Kveton, Zheng Wen, Azin Ashkan, Csaba Szepesvári:
Tight Regret Bounds for Stochastic Combinatorial Semi-Bandits. - Simon Lacoste-Julien, Fredrik Lindsten, Francis R. Bach:
Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering. - Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh:
Particle Gibbs for Bayesian Additive Regression Trees. - Chen-Yu Lee, Saining Xie, Patrick W. Gallagher, Zhengyou Zhang, Zhuowen Tu:
Deeply-Supervised Nets. - Jay Lee, Manzil Zaheer, Stephan Günnemann, Alexander J. Smola:
Preferential Attachment in Graphs with Affinities. - Juho Lee, Seungjin Choi:
Bayesian Hierarchical Clustering with Exponential Family: Small-Variance Asymptotics and Reducibility. - Guy Lever, Ronnie Stafford:
Modelling Policies in MDPs in Reproducing Kernel Hilbert Space. - Bo Li, Yevgeniy Vorobeychik:
Scalable Optimization of Randomized Operational Decisions in Adversarial Classification Settings. - Lihong Li, Rémi Munos, Csaba Szepesvári:
Toward Minimax Off-policy Value Estimation. - Ping Li, Cun-Hui Zhang:
Compressed Sensing with Very Sparse Gaussian Random Projections. - Xin Li, Yuhong Guo:
Max-Margin Zero-Shot Learning for Multi-class Classification. - Xin Li, Feipeng Zhao, Yuhong Guo:
Conditional Restricted Boltzmann Machines for Multi-label Learning with Incomplete Labels. - Yen-Huan Li, Jonathan Scarlett, Pradeep Ravikumar, Volkan Cevher:
Sparsistency of 1-Regularized M-Estimators. - Kuan Liu, Aurélien Bellet, Fei Sha:
Similarity Learning for High-Dimensional Sparse Data. - Mario Lucic, Mesrob I. Ohannessian, Amin Karbasi, Andreas Krause:
Tradeoffs for Space, Time, Data and Risk in Unsupervised Learning. - Yifei Ma, Danica J. Sutherland, Roman Garnett, Jeff G. Schneider:
Active Pointillistic Pattern Search. - Shike Mei, Xiaojin Zhu:
The Security of Latent Dirichlet Allocation. - Igor Melnyk, Arindam Banerjee:
A Spectral Algorithm for Inference in Hidden semi-Markov Models. - Ofer Meshi, Nathan Srebro, Tamir Hazan:
Efficient Training of Structured SVMs via Soft Constraints. - James Neufeld, Dale Schuurmans, Michael H. Bowling:
Variance Reduction via Antithetic Markov Chains. - Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Eric P. Xing, Hy Trac, Shirley Ho, Jeff G. Schneider:
Fast Function to Function Regression. - Pedro A. Ortega, Kee-Eung Kim, Daniel D. Lee:
Reactive bandits with attitude. - Saurabh Paul, Malik Magdon-Ismail, Petros Drineas:
Feature Selection for Linear SVM with Provable Guarantees. - Robert Peharz, Sebastian Tschiatschek, Franz Pernkopf, Pedro M. Domingos:
On Theoretical Properties of Sum-Product Networks. - Vu Pham, Laurent El Ghaoui:
Robust sketching for multiple square-root LASSO problems. - Rajesh Ranganath, Linpeng Tang, Laurent Charlin, David M. Blei:
Deep Exponential Families. - Sashank J. Reddi, Aaditya Ramdas, Barnabás Póczos, Aarti Singh, Larry A. Wasserman:
On the High Dimensional Power of a Linear-Time Two Sample Test under Mean-shift Alternatives. - Shaogang Ren, Shuai Huang, John A. Onofrey, Xenios Papademetris, Xiaoning Qian:
A Scalable Algorithm for Structured Kernel Feature Selection. - Jonathan Root, Jing Qian, Venkatesh Saligrama:
Learning Efficient Anomaly Detectors from K-NN Graphs. - Anirban Roychowdhury, Brian Kulis:
Gamma Processes, Stick-Breaking, and Variational Inference. - Hiroaki Sasaki, Yung-Kyun Noh, Masashi Sugiyama:
Direct Density-Derivative Estimation and Its Application in KL-Divergence Approximation. - Mark Schmidt, Reza Babanezhad, Mohamed Osama Ahmed, Aaron Defazio, Ann Clifton, Anoop Sarkar:
Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields. - François Schnitzler, Jia Yuan Yu, Shie Mannor:
Sensor Selection for Crowdsensing Dynamical Systems. - Clayton Scott:
A Rate of Convergence for Mixture Proportion Estimation, with Application to Learning from Noisy Labels. - Eleni Sgouritsa, Dominik Janzing, Philipp Hennig, Bernhard Schölkopf:
Inference of Cause and Effect with Unsupervised Inverse Regression. - Nihar B. Shah, Sivaraman Balakrishnan, Joseph K. Bradley, Abhay Parekh, Kannan Ramchandran, Martin J. Wainwright:
Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence. - Roshan Shariff, András György, Csaba Szepesvári:
Exploiting Symmetries to Construct Efficient MCMC Algorithms With an Application to SLAM. - Tianlin Shi, Jacob Steinhardt, Percy Liang:
Learning Where to Sample in Structured Prediction. - Arno Solin, Simo Särkkä:
State Space Methods for Efficient Inference in Student-t Process Regression. - Shuang Song, Kamalika Chaudhuri, Anand D. Sarwate:
Learning from Data with Heterogeneous Noise using SGD. - Suvrit Sra, Reshad Hosseini, Lucas Theis, Matthias Bethge:
Data modeling with the elliptical gamma distribution. - Sanvesh Srivastava, Volkan Cevher, Quoc Tran-Dinh, David B. Dunson:
WASP: Scalable Bayes via barycenters of subset posteriors. - Julien Stoehr, Nial Friel:
Calibration of conditional composite likelihood for Bayesian inference on Gibbs random fields. - Julian Straub, Jason Chang, Oren Freifeld, John W. Fisher III:
A Dirichlet Process Mixture Model for Spherical Data. - Siqi Sun, Hai Wang, Jinbo Xu:
Inferring Block Structure of Graphical Models in Exponential Families. - Zoltán Szabó, Arthur Gretton, Barnabás Póczos, Bharath K. Sriperumbudur:
Two-stage sampled learning theory on distributions. - Rikiya Takahashi, Tetsuro Morimura:
Predicting Preference Reversals via Gaussian Process Uncertainty Aversion. - Alex Tank, Nicholas J. Foti, Emily B. Fox:
Streaming Variational Inference for Bayesian Nonparametric Mixture Models. - Jin Tian:
Missing at Random in Graphical Models. - Jan-Willem van de Meent, Hongseok Yang, Vikash Mansinghka, Frank D. Wood:
Particle Gibbs with Ancestor Sampling for Probabilistic Programs. - Herke van Hoof, Jan Peters, Gerhard Neumann:
Learning of Non-Parametric Control Policies with High-Dimensional State Features. - Greg Ver Steeg, Aram Galstyan:
Maximally Informative Hierarchical Representations of High-Dimensional Data. - Fulton Wang, Cynthia Rudin:
Falling Rule Lists. - Xu Wang, Konstantinos Slavakis, Gilad Lerman:
Multi-Manifold Modeling in Non-Euclidean spaces. - Yining Wang, Aarti Singh:
Column Subset Selection with Missing Data via Active Sampling. - Yu-Xiang Wang, James Sharpnack, Alexander J. Smola, Ryan J. Tibshirani:
Trend Filtering on Graphs. - Fabian L. Wauthier, Peter Donnelly:
A Greedy Homotopy Method for Regression with Nonconvex Constraints. - Adrian Weller:
Revisiting the Limits of MAP Inference by MWSS on Perfect Graphs. - Yi Wu, David P. Wipf, Jeong-Min Yun:
Understanding and Evaluating Sparse Linear Discriminant Analysis. - Kevin S. Xu:
Stochastic Block Transition Models for Dynamic Networks. - Zhirong Yang, Jaakko Peltonen, Samuel Kaski:
Majorization-Minimization for Manifold Embedding. - Zichao Yang, Andrew Gordon Wilson, Alexander J. Smola, Le Song:
A la Carte - Learning Fast Kernels. - Yaoliang Yu, Xun Zheng, Micol Marchetti-Bowick, Eric P. Xing:
Minimizing Nonconvex Non-Separable Functions. - Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou:
A Simple Homotopy Algorithm for Compressive Sensing. - Shandian Zhe, Zenglin Xu, Xinqi Chu, Yuan (Alan) Qi, Youngja Park:
Scalable Nonparametric Multiway Data Analysis. - Mingyuan Zhou:
Infinite Edge Partition Models for Overlapping Community Detection and Link Prediction. - Xiangyang Zhou, Jiaxin Zhang, Brian Kulis:
Power-Law Graph Cuts. - Yuancheng Zhu, Rina Foygel Barber:
The Log-Shift Penalty for Adaptive Estimation of Multiple Gaussian Graphical Models.
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