accepted.html

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title: Accepted Papers
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<p>  The proceedings of AISTATS are now available on line: <br />
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href="http://proceedings.mlr.press/v31/">http://proceedings.mlr.press/v31/</a>. <br />

<h2>Accepted Papers</h2>
<br>

  <b>Scoring anomalies: a M-estimation formulation</b><br>
  Stephan Clemencon, Telecom ParisTech; Jeremie Jakubowicz, Telecom Sud
  Management<br><br>





  <b>Bayesian Estimation for Partially Observed MRFs</b><br>
  Yutian Chen, UC Irvine; Max Welling, University of California,
  Irvine<br><br>





  <b>High-dimensional Inference via Lipschitz Sparsity-Yielding
  Regularizers</b><br>
  Zheng Pan, Tsinghua Univ.; Changshui Zhang, Tsinghua Univ.<br><br>





  <b>Learning to Top-K Search using Pairwise Comparisons</b><br>
  Brian Eriksson, Technicolor<br><br>





  <b>Stochastic blockmodeling of relational event dynamics</b><br>
  Christopher DuBois, UC Irvine; Carter Butts, UC Irvine; Padhraic Smyth,
  University of California Irvine<br><br>





  <b>Unsupervised Link Selection in Networks</b><br>
  Quanquan Gu, CS, UIUC; Charu Aggarwal, IBM Research; Jiawei Han, UIUC<br><br>





  <b>Beyond Sentiment: The Manifold of Human Emotions</b><br>
  Seungyeon Kim, Georgia Institute of Technolog; Fuxin Li, Georgia
  Institute of Technology; Guy Lebanon, Georgia Institute of Technology; Irfan
  Essa, Georgia Institute of Technology<br><br>





  <b>Greedy Bilateral Sketch, Completion and Smoothing</b><br>
  Tianyi Zhou, Universityof Technology Sydney; Dacheng Tao, University of
  Technology, Sydney<br><br>





  <b>Further Optimal Regret Bounds for Thompson Sampling</b><br>
  Shipra Agrawal, MSR India; Navin Goyal, MSR India<br><br>





  <b>Faster Training of Structural SVMs with Diverse M-Best
  Cutting-Planes</b><br>
  Abner Guzman-Rivera, University of Illinois; Pushmeet Kohli, Microsoft
  Research Cambridge; Dhruv Batra, Virginia Tech<br><br>





  <b>Structural Expectation Propagation (SEP): Bayesian structure
  learning for networks with latent variables</b><br>
  Nevena Lazic, Microsoft Research; Christopher Bishop, ; John Winn, <br><br>





  <b>DYNA-CARE: Dynamic Cardiac Arrest Risk Estimation</b><br>
  Joyce Ho, University of Texas at Austin; Yubin Park, University of Texas
  at Austin; Carlos Carvalho, University of Texas at Austin; Joydeep Ghosh,
  University of Texas at Austin<br><br>





  <b>Competing with an Infinite Set of Models in Reinforcement
  Learning</b><br>
  Phuong Nguyen, Australian National University; Odalric-Ambrym Maillard,
  Montanuniversitaet Leoben; Daniil Ryabko, INRIA, Lille; Ronald Ortner,
  Montanuniversitaet Leoben<br><br>





  <b>Central Limit Theorems for Conditional Markov Chains</b><br>
  Mathieu Sinn, IBM Research; Bei Chen, IBM Research - Ireland<br><br>





  <b>Efficient Variational Inference for Gaussian Process Regression
  Networks</b><br>
  Trung Nguyen, ANU and NICTA; Edwin Bonilla, NICTA and ANU<br><br>





  <b>Active Learning for Interactive Visualization</b><br>
  Tomoharu Iwata, University of Cambridge; Neil Houlsby, University of
  Cambridge; Zoubin Ghahramani, University of Cambridge<br><br>





  <b>ODE parameter inference using adaptive gradient matching with
  Gaussian processes</b><br>
  Frank Dondelinger, Biomathematics and Statistics Scotland; Dirk
  Husmeier, University of Glasgow; Simon Rogers, University of Glasgow;
  Maurizio Filippone, University of Glasgow<br><br>





  <b>Exact Learning of Bounded Tree-width Bayesian Networks</b><br>
  Janne Korhonen, University of Helsinki; Pekka Parviainen, <br><br>





  <b>Completeness Results for Lifted Variable Elimination</b><br>
  Nima Taghipour, KU Leuven; Daan Fierens, KU LEUVEN; Guy Van den Broeck,
  UCLA; Jesse Davis, KU LEUVEN; Hendrik Blockeel, KU LEUVEN<br><br>





  <b>Fast Near-GRID Gaussian Process Regression</b><br>
  Yuancheng Luo, University of Maryland; Ramani Duraiswami, University of
  Maryland<br><br>





  <b>Convex Collective Matrix Factorization</b><br>
  Guillaume Bouchard, &quot;Xerox Research Centre, Europe&quot;; Dawei Yin,
  Lehigh University; Shengbo Guo, Xerox Research Centre Europe<br><br>





  <b>Meta-Transportability of Causal Effects: A Formal Approach</b><br>
  Elias Bareinboim, UCLA; Judea Pearl, UCLA<br><br>





  <b>Why Steiner-tree type algorithms work for community detection</b><br>
  Mung Chiang, Princeton University; Henry Lam, Boston University; Zhenming
  Liu, Princeton University; Harold Poor, Princeton University<br><br>





  <b>Clustering Oligarchies</b><br>
  Margareta Ackerman, Caltech; Shai Ben David, ; David Loker, University
  of Waterloo; Sivan Sabato, Microsoft Research<br><br>





  <b>Structure Learning of Mixed Graphical Models</b><br>
  Jason Lee, Computational Math &amp; Engineeri; Trevor Hastie, Stanford
  University<br><br>





  <b>A Simple Criterion for Controlling Selection Bias</b><br>
  Eunice Yuh-Jie Chen, UCLA; Judea Pearl, UCLA<br><br>





  <b>Clustered Support Vector Machine</b><br>
  Quanquan Gu, CS, UIUC; Jiawei Han, UIUC<br><br>





  <b>A Competitive Test for Uniformity of Monotone Distributions</b><br>
  Ashkan Jafarpour, Univ. of California, San Diego; Jayadev
  Acharya, University of California, San Diego; Alon Orlitsky, University of
  California, San Diego; Ananda Suresh, University of California, San Diego<br><br>




  <b>Deep Gaussian Processes</b><br>
  Andreas Damianou, University of Sheffield; Neil Lawrence, University of
  Sheffield<br><br>





  <b>Permutation estimation and minimax rates of identifiability</b><br>
  Olivier Collier, IMAGINE-ENPC / CREST-ENSAE; Arnak Dalalyan, Ecole des
  Ponts ParisTech<br><br>





  <b>Bayesian Structure Learning for Functional Neuroimaging</b><br>
  Oluwasanmi Koyejo, University of Texas at Austin; Mijung Park,
  UT Austin; Russell Poldrack, University of Texas at Austin; Joydeep Ghosh,
  University of Texas at Austin; Jonathan Pillow, The University of Texas at
  Austin<br><br>


  <b>Dual Decomposition for Joint Discrete-Continuous Optimization</b><br>
  Christopher Zach, Microsoft Research<br><br>





  <b>Distribution-Free Distribution Regression</b><br>
  Barnabas Poczos, Carnegie Mellon University;  Aarti Singh, Carnegie
  Mellon University; Alessandro  Rinaldo, Carnegie Mellon University; Larry
  Wasserman, <br><br>





  <b>A Last-Step Regression Algorithm for Non-Stationary Online
  Learning</b><br>
  Edward Moroshko, Technion; Koby Crammer, Technion University<br><br>





  <b>Efficiently Sampling Probabilistic Programs via Program Analysis</b><br>
  Arun Chaganty, ; Aditya Nori, Microsoft Research India; Sriram Rajamani,
  <br><br>





  <b>On the Asymptotic Optimality of Maximum Margin Bayesian
  Networks</b><br>
  Sebastian Tschiatschek, TU Graz; Franz Pernkopf, TU Graz<br><br>





  <b>Ultrahigh Dimensional Feature Screening via RKHS Embeddings</b><br>
  Krishnakumar Balasubramanian, Gatech; Bharath Sriperumbudur, Cambridge
  University ; Guy Lebanon, Georgia Institute of Technology<br><br>





  <b>Data-driven covariate selection for nonparametric estimation of
  causal effects</b><br>
  Doris Entner, University of Helsinki; Patrik Hoyer, ; Peter Spirtes, <br><br>





  <b>Mixed LICORS: A Nonparametric Algorithm for Predictive State
  Reconstruction</b><br>
  Georg Goerg, Carnegie Mellon University; Cosma  Shalizi, Carnegie Mellon
  University  <br><br>





  <b>Thompson Sampling in Switching Environments with Bayesian
  Online Change Detection</b><br>
  Joseph Mellor, University of Manchester; Jonathan Shapiro, University of
  Manchester<br><br>





  <b>Collapsed Variational Bayesian Inference for Hidden Markov
  Models</b><br>
  Pengyu Wang, University of Oxford; Phil Blunsom, University of Oxford<br><br>





  <b>Supervised Sequential Classification Under Budget Constraints</b><br>
  Kirill Trapeznikov, Boston University; Venkatesh Saligrama, Boston
  University; david Castanon, Boston University<br><br>





  <b>Computing the M Most Probable Modes of a Graphical Model </b><br>
  Chao Chen, Rutgers University; Vladimir Kolmogorov, IST Austria; Yan
  Zhu, Rutgers University; Dimitris Metaxas, Rutgers University; Christoph
  Lampert, IST Austria<br><br>





  <b>Estimating the Partition Function of Graphical Models  Using
  Langevin Importance Sampling  </b><br>
  Jianzhu Ma, TTIC; Jian Peng, ; Sheng Wang, TTIC; Jinbo Xu, TTIC<br><br>





  <b>Random Projections for Support Vector Machines</b><br>
  Saurabh Paul, Rensselaer Polytechnic Inst; Christos Boutsidis, IBM;
  Malik Magdon-Ismail, ; Petros Drineas, RPI<br><br>





  <b>A unifying representation for a class of dependent random
  measures</b><br>
  Nicholas Foti, Dartmouth College; Sinead Williamson, Carnegie Mellon
  University; Daniel Rockmore, Dartmouth College; Joseph Futoma, Dartmouth
  College<br><br>





  <b>Dynamic Copula Networks for Modeling Real-valued Time Series</b><br>
  Elad Eban, Hebrew University; gideon Rothschild, Hebrew University; Adi
  Mizrahi, Hebrew University; Israel Nelken, Hebrew University; Gal Elidan,
  Hebrew University<br><br>





  <b>A Parallel, Block Greedy Method for Sparse Inverse Covariance
  Estimation for Ultra-high Dimensions</b><br>
  Prabhanjan  Kambadur, IBM TJ Watson Research Center; Aurelie Lozano, <br><br>





  <b>A recursive estimate for the predictive likelihood in a topic
  model</b><br>
  James Scott, ; Jason Baldridge, University of Texas at Austin<br><br>





  <b>Nystrom Approximation for Large-Scale Determinantal Processes</b><br>
  Raja Hafiz Affandi, University of Pennsylvania; Emily Fox, ; Ben Taskar,
  University of Pennsylvania; Alex Kulesza, <br><br>





  <b>A simple sketching algorithm for entropy estimation over
  streaming data</b><br>
  Ioana Cosma, University of Ottawa; Peter Clifford, University of Oxford<br><br>





  <b>Detecting Activations over Graphs using Spanning Tree Wavelet
  Bases</b><br>
  James Sharpnack, Carnegie Mellon University;  Aarti Singh, Carnegie
  Mellon University; Akshay Krishnamurthy, CMU<br><br>





  <b>Learning Social Infectivity in Sparse Low-rank Networks Using
  Multi-dimensional Hawkes Processes</b><br>
  Ke Zhou, Georgia institute of technolog; Le Song, Georgia institute of
  technology; Hongyuan Zha, Georgia institute of technology<br><br>





  <b>Changepoint Detection over Graphs with the Spectral Scan
  Statistic</b><br>
  James Sharpnack, Carnegie Mellon University;  Aarti Singh, Carnegie
  Mellon University; Alessandro  Rinaldo, Carnegie Mellon University<br><br>





  <b>Statistical Tests for Contagion in Observational Social Network
  Studies</b><br>
  Greg Ver Steeg, Information Sciences Institute; Aram Galstyan,
  Information Sciences Institute, USC<br><br>





  <b>Diagonal Orthant Multinomial Probit Models</b><br>
  James Johndrow, Duke University; Kristian Lum, Virginia Tech; David
  Dunson, Duke University<br><br>





  <b>Reconstructing ecological networks with hierarchical Bayesian
  regression and Mondrian processes</b><br>
  Andrej Aderhold, University of St Andrews; Dirk Husmeier, University of
  Glasgow; V. Anne  Smith,  University of St Andrews<br><br>





  <b>Bayesian learning of joint distributions of objects</b><br>
  Anjishnu Banerjee, Duke University; Jared  Murray, Duke University;
  David Dunson, Duke University<br><br>





  <b>Consensus Ranking with Signed Permutations</b><br>
  Raman Arora, TTIC; Marina Meila, University of Washington<br><br>





  <b>Sparse Principal Component Analysis for High Dimensional
  Multivariate Time Series</b><br>
  Zhaoran Wang, Princeton University; Fang Han, Johns Hopkins University;
  Han Liu, <br><br>





  <b>Texture Modeling with Convolutional Spike-and-Slab RBMs and
  Deep Extensions</b><br>
  Heng Luo, Universite de Montreal; Pierre Luc Carrier, Universite de
  Montreal; Aaron Courville, Universite de Montreal; Yoshua Bengio, Universite
  de Montreal<br><br>





  <b>Block Regularized Lasso for Multivariate Multi-Response Linear
  Regression</b><br>
  Weiguang Wang, Syracuse University; Yingbin Liang, Syracuse University;
  Eric Xing, Carnegie Mellon University<br><br>





  <b>Predictive Correlation Screening: Application to Two-stage
  Predictor Design in High Dimension</b><br>
  Hamed Firouzi, University of Michigan; Alfred Hero III, University of
  Michigan<br><br>





  <b>Distributed Learning of Gaussian Graphical Models via Marginal
  Likelihoods</b><br>
  Zhaoshi Meng, University of Michigan; Dennis Wei, University of
  Michigan; Ami Wiesel, The Hebrew University of Jerusalem ; Alfred Hero III,
  University of Michigan<br><br>





  <b>Dynamic Scaled Sampling for Deterministic Constraints</b><br>
  Lei Li, UC Berkeley; Bharath Ramsundar, ; Stuart Russell, UC Berkeley<br><br>





  <b>Localization and Adaptation in Online Learning</b><br>
  Alexander (Sasha) Rakhlin,  University of Pennsylvania; Ohad Shamir, ;
  Karthik Sridharan,  University of Pennsylvania<br><br>





  <b>Recursive Karcher Expectation Estimators And Geometric Law of
  Large Numbers</b><br>
  Hesamoddin Salehian, University of Florida; Guang Cheng, ; Jeffrey Ho,
  UFL; Baba Vemuri, University of Florida<br><br>





  <b>Distributed and Adaptive Darting Monte Carlo through
  Regenerations</b><br>
  Sungjin Ahn, UCI; Yutian Chen, UC Irvine; Max Welling, &quot;University
  of California, Irvine&quot;<br><br>





  <b>Uncover Topic-Sensitive Information Diffusion Networks</b><br>
  NAN DU, GATECH; Le Song, Georgia institute of technology; Hyenkyun Woo,
  ; Hongyuan Zha, Georgia institute of technology<br><br>





  <b>Learning Markov Networks With Arithmetic Circuits</b><br>
  Daniel Lowd, University of Oregon; Amirmohammad Rooshenas, University of
  Oregon<br><br>





  <b>Bethe Bounds and Approximating the Global Optimum</b><br>
  Adrian Weller, Columbia University; Tony Jebara, Columbia University<br><br>



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The conference will start the morning of Monday, April 29 (so arrival on Sunday night is recommended).&nbsp; There will be two poster sessions, Monday and Tuesday evenings.&nbsp;  On Wednesday, May 1st there will be a mixture of AISTATS and Learning Workshop sessions; AISTATS registrants are welcome to attend both AISTATS and Learning sessions on Wednesday.<p> -->

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