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<h1>AISTATS 2016 Poster Sessions</h1>

<h2>Accepted papers</h2>
All accepted papers are available <a href="http://jmlr.org/proceedings/papers/v51/">here</a>.
<br><br>

<h2>Poster Format</h2>
The poster board is 0.98m (wide) x 2.54m (high). We recommend <b>A0 portrait</b> as
the poster size.

<br>
<img src="{{ site.baseurl }}/image/poster_board.gif" height="400">

<h2><span id="poster_session1">Poster Session 1 (May 9)</span></h2>

<p>Poster 1: <b>Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures</b><br>Mario Lucic, Olivier Bachem, Andreas Krause</p>
<p>Poster 2: <b>Revealing Graph Bandits for Maximizing Local Influence</b><br>Alexandra Carpentier, Michal Valko</p>
<p>Poster 3: <b>Convex block-sparse linear regression with expanders, provably</b><br>Anastasios Kyrillidis, Bubacarr Bah, Rouzbeh Hasheminezhad, Quoc Tran Dinh, Luca Baldassarre, Volkan Cevher</p>
<p>Poster 4: <b>Clamping Improves TRW and Mean Field Approximations</b><br>Adrian Weller, Justin Domke</p>
<p>Poster 5: <b>Control Functionals for Quasi-Monte Carlo Integration</b><br>Chris Oates, Mark Girolami</p>
<p>Poster 6: <b>Sparse Representation of Multivariate Extremes with Applications to Anomaly Ranking</b><br>Nicolas Goix, Anne Sabourin, Stéphan Clémençon</p>
<p>Poster 7: <b>A Robust-Equitable Copula Dependence Measure for Feature Selection</b><br>Yale Chang, Yi Li, Adam Ding, Jennifer Dy</p>
<p>Poster 8: <b>Random Forest for the Contextual Bandit Problem</b><br>Raphaël Féraud, Robin Allesiardo, Tanguy Urvoy, Fabrice Clérot</p>
<p>Poster 9: <b>Learning Sparse Additive Models with Interactions in High Dimensions</b><br>Hemant Tyagi, Anastasios Kyrillidis, Bernd Gärtner, Andreas Krause</p>
<p>Poster 10: <b>Bipartite Correlation Clustering - Maximizing Agreements</b><br>Megasthenis Asteris, Anastasios Kyrillidis, Dimitris Papailiopoulos, Alexandros Dimakis</p>
<p>Poster 11: <b>Breaking Sticks and Ambiguities with Adaptive Skip-gram</b><br>Sergey Bartunov, Dmitry Kondrashkin, Anton Osokin, Dmitry Vetrov</p>
<p>Poster 12: <b>Top Arm Identification in Multi-Armed Bandits with Batch Arm Pulls</b><br>Kwang-Sung Jun, Kevin Jamieson, Robert Nowak, Xiaojin Zhu</p>
<p>Poster 13: <b>Limits on Sparse Support Recovery via Linear Sketching with Random Expander Matrices</b><br>Jonathan Scarlett, Volkan Cevher</p>
<p>Poster 14: <b>Maximum Likelihood for Variance Estimation in High-Dimensional Linear Models</b><br>Lee Dicker, Murat Erdogdu</p>
<p>Poster 15: <b>Scalable Gaussian Process Classification via Expectation Propagation</b><br>Daniel Hernandez-Lobato, Jose Miguel Hernandez-Lobato</p>
<p>Poster 16: <b>Precision Matrix Estimation in High Dimensional Gaussian Graphical Models with Faster Rates</b><br>Lingxiao Wang, Quanquan Gu</p>
<p>Poster 17: <b>On the Reducibility of Submodular Functions</b><br>Jincheng Mei, Hao Zhang, Bao-Liang Lu</p>
<p>Poster 18: <b>Accelerated Stochastic Gradient Descent for Minimizing Finite Sums</b><br>Atsushi Nitanda</p>
<p>Poster 19: <b>Fast Convergence of Online Pairwise Learning Algorithms</b><br>Martin Boissier, Siwei Lyu, Yiming Ying, Ding-Xuan Zhou</p>
<p>Poster 20: <b>Computationally Efficient Bayesian Learning of Gaussian Process State Space Models</b><br>Andreas Svensson, Arno Solin, Simo Särkkä, Thomas Schön</p>
<p>Poster 21: <b>Generalized Ideal Parent (GIP): Discovering non-Gaussian Hidden Variables</b><br>Yaniv Tenzer, Gal Elidan</p>
<p>Poster 22: <b>On Sparse Variational Methods and the Kullback-Leibler Divergence between Stochastic Processes</b><br>Alex Matthews, James Hensman, Richard Turner, Zoubin Ghahramani</p>
<p>Poster 23: <b>Non-stochastic Best Arm Identification and Hyperparameter Optimization</b><br>Kevin Jamieson Ameet Tawalkar</p>
<p>Poster 24: <b>A Linearly-Convergent Stochastic L-BFGS Algorithm</b><br>Philipp Moritz, Robert Nishihara, Michael Jordan</p>
<p>Poster 25: <b>No Regret Bound for Extreme Bandits</b><br>Robert Nishihara, David Lopez-Paz, Leon Bottou</p>
<p>Poster 26: <b>Online Learning to Rank with Feedback at the Top</b><br>Sougata Chaudhuri, Ambuj Tewari</p>
<p>Poster 27: <b>Score Permutation Based Finite Sample Inference for Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) Models</b><br>Balazs Csaji</p>
<p>Poster 28: <b>CRAFT: ClusteR-specific Assorted Feature selecTion</b><br>Vikas Garg, Cynthia Rudin, Tommi Jaakkola</p>
<p>Poster 29: <b>Time-Varying Gaussian Process Bandit Optimization</b><br>Ilija Bogunovic, Jonathan Scarlett, Volkan Cevher</p>
<p>Poster 30: <b>Bayes-Optimal Effort Allocation in Crowdsourcing: Bounds and Index Policie</b><br>Weici Hu, Peter Frazier</p>
<p>Poster 31: <b>Bayesian Markov Blanket Estimation</b><br>Dinu Kaufmann, Sonali Parbhoo, Aleksander Wieczorek, Sebastian Keller, David Adametz, Volker Roth</p>
<p>Poster 32: <b>Unsupervised Ensemble Learning with Dependent Classifiers</b><br>Ethan Fetaya, Boaz Nadler, Ariel Jaffe, Ting Ting Jiang, Yuval Kluger</p>
<p>Poster 33: <b>Multi-Level Cause-Effect Systems</b><br>Krzysztof Chalupka, Frederick Eberhardt, Pietro Perona</p>
<p>Poster 34: <b>Deep Kernel Learning</b><br>Andrew Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric Xing</p>
<p>Poster 35: <b>Latent Point Process Allocation</b><br>Chris Lloyd, Tom Gunter, Michael Osborne, Stephen Roberts, Tom Nickson</p>
<p>Poster 36: <b>Bayesian generalised ensemble Markov chain Monte Carlo</b><br>Jes Frellsen, Ole Winther, Zoubin Ghahramani, Jesper Ferkinghoff-Borg</p>
<p>Poster 37: <b>A Lasso-based Sparse Knowledge Gradient Policy for Sequential Optimal Learning</b><br>Yan Li, Han Liu, Warren Powell</p>
<p>Poster 38: <b>Optimal Statistical and Computational Rates for One Bit Matrix Completion</b><br>Quanquan Gu, Renkun Ni</p>
<p>Poster 39: <b>PAC-Bayesian Bounds based on the Rényi Divergence</b><br>Luc Bégin, Pascal Germain, François Laviolette, Jean-Francis Roy</p>
<p>Poster 40: <b>Simple and Scalable Constrained Clustering: A Generalized Spectral Method</b><br>Mihai Cucuringu, Ioannis Koutis, Gary Miller, Richard Peng, Sanjay Chawla</p>
<p>Poster 41: <b>Geometry Aware Mappings for High Dimensional Sparse Factors</b><br>Avradeep Bhowmik, Nathan Liu, Erheng Zhong, Badri Bhaskar, Suju Rajan</p>
<p>Poster 42: <b>Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree</b><br>Chen-Yu Lee, Patrick Gallagher, Zhuowen Tu</p>
<p>Poster 43: <b>Rivalry of Two Families of Algorithms for Memory-Restricted Streaming PCA</b><br>Chun-Liang Li, Hsuan-Tien Lin, Chi-Jen Lu</p>
<p>Poster 44: <b>Quantization based Fast Inner Product Search</b><br>Ruiqi Guo, Sanjiv Kumar, Krzysztof Choromanski, David Simcha</p>
<p>Poster 45: <b>An Improved Convergence Analysis of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization</b><br>Tuo Zhao, Xingguo Li, Raman Arora, Han Liu, Mingyi Hong</p>
<p>Poster 46: <b>Learning Structured Low-Rank Representation via Matrix Factorization</b><br>Jie Shen,Ping Li</p>
<p>Poster 47: <b>A PAC RL Algorithm for Episodic POMDPs</b><br>Zhaohan Guo, Shayan Doroudi, Emma Brunskill</p>
<p>Poster 48: <b>Large-Scale Optimization Algorithms for Sparse Conditional Gaussian Graphical Models</b><br>Calvin McCarter, Seyoung Kim</p>
<p>Poster 49: <b>Graph Connectivity in Noisy Sparse Subspace Clustering</b><br>Yining Wang, Yu-Xiang Wang, Aarti Singh</p>
<p>Poster 50: <b>The Nonparametric Kernel Bayes Smoother</b><br>Yu Nishiyama, Amir Afsharinejad, Shunsuke Naruse, Byron Boots, Le Song</p>
<p>Poster 51: <b>Universal Models of Multivariate Temporal Point Processes</b><br>Asela Gunawardana, Chris Meek</p>
<p>Poster 52: <b>Nonparametric Budgeted Stochastic Gradient Descent</b><br>Trung Le, Vu Nguyen, Dinh Phung</p>
<p>Poster 53: <b>Online Relative Entropy Policy Search using Reproducing Kernel Hilbert Space Embeddings</b><br>Zhitang Chen, Pascal Poupart, Yanhui Geng</p>
<p>Poster 54: <b>Relationship between PreTraining and Maximum Likelihood Estimation in Deep Boltzmann Machines</b><br>Muneki Yasuda</p>
<p>Poster 55: <b>Enumerating Equivalence Classes of Bayesian Networks using CPDAG Graphs</b><br>Eunice Chen, Arthur Choi, Adnan Darwiche</p>
<p>Poster 56: <b>NuC-MKL: A Convex Approach to Non Linear Multiple Kernel Learning</b><br>Eli Meirom, Pavel Kisilev</p>
<p>Poster 57: <b>Improper Deep Kernels</b><br>Uri Heinemann, Roi Livni, Elad Eban, Gal Elidan, Amir Globerson</p>
<p>Poster 58: <b>Randomization and The Pernicious Effects of Limited Budgets on Auction Experiments</b><br>Guillaume Basse, Hossein Azari, Diane Lambert</p>
<p>Poster 59: <b>Mondrian Forests for Large-Scale Regression when Uncertainty Matters</b><br>Balaji Lakshminarayanan, Dan Roy, Yee Whye Teh</p>
<p>Poster 60: <b>Provable Tensor Methods for Learning Mixtures of Generalized Linear Models</b><br>Hanie Sedghi, Majid Janzamin, Anima Anandkumar</p>

<h2><span id="poster_session2">Poster Session 2 (May 10)</span></h2>

<p>Poster 1: <b>C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching</b><br>Daniel Ritchie, Andreas Stuhlmüller, Noah Goodman</p>
<p>Poster 2: <b>Tightness of LP Relaxations for Almost Balanced Models</b><br>Adrian Weller, David Sontag</p>
<p>Poster 3: <b>Inverse Reinforcement Learning with Simultaneous Estimation of Rewards and Dynamics</b><br>Michael Herman, Tobias Gindele, Jörg Wagner, Felix Schmitt, Wolfram Burgard</p>
<p>Poster 4: <b>Survey Propagation beyond Constraint Satisfaction Problems</b><br>Christopher Srinivasa, Siamak Ravanbakhsh, Brendan Frey</p>
<p>Poster 5: <b>Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation</b><br>Søren Hauberg, Oren Freifeld, Anders Boesen Lindbo Larsen, John Fisher, Lars Hansen</p>
<p>Poster 6: <b>K2-ABC: Approximate Bayesian Computation with Kernel Embeddings</b><br>Mijung Park, Wittawat Jitkrittum, Dino Sejdinovic</p>
<p>Poster 7: <b>Fast Dictionary Learning with a Smoothed Wasserstein Loss</b><br>Antoine Rolet, Marco Cuturi, Gabriel Peyré</p>
<p>Poster 8: <b>New Resistance Distances with Global Information on Large Graphs</b><br>Canh Hao Nguyen, Hiroshi Mamitsuka</p>
<p>Poster 9: <b>Batch Bayesian Optimization via Local Penalization</b><br>Javier Gonzalez, Zhenwen Dai, Philipp Hennig, Neil Lawrence</p>
<p>Poster 10: <b>Learning relationships between data obtained independently</b><br>Alexandra Carpentier, Teresa Schlueter</p>
<p>Poster 11: <b>Fast and Scalable Structural SVM with Slack Rescaling</b><br>Heejin Choi, Ofer Meshi, Nathan Srebro</p>
<p>Poster 12: <b>Probabilistic Approximate Least-Squares</b><br>Simon Bartels, Philipp Hennig</p>
<p>Poster 13: <b>Approximate Inference Using DC Programming For Collective Graphical Models</b><br>Thien Nguyen, Akshat Kumar, Hoong Chuin Lau, Daniel Sheldon</p>
<p>Poster 14: <b>Sequential Inference for Deep Gaussian Process</b><br>Yali Wang, Marcus Brubaker, Brahim Chaib-draa, Raquel Urtasun</p>
<p>Poster 15: <b>Variational Tempering</b><br>Stephan Mandt, James McInerney, Farhan Abrol, Rajesh Ranganath, David Blei</p>
<p>Poster 16: <b>On Convergence of Model Parallel Proximal Gradient Algorithm for Stale Synchronous Parallel System</b><br>Yi Zhou, Yaoliang Yu, Wei Dai, Yingbin Liang, Eric Xing</p>
<p>Poster 17: <b>Scalable MCMC for Mixed Membership Stochastic Blockmodels</b><br>Wenzhe Li, Max Welling, Sungjin Ahn</p>
<p>Poster 18: <b>Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo</b><br>Markus Heinonen, Henrik Mannerström, Juho Rousu, Samuel Kaski, Harri Lähdesmäki</p>
<p>Poster 19: <b>A Deep Generative Deconvolutional Image Model</b><br>Yunchen Pu, Xin Yuan, Andrew Stevens, Chunyuan Li, Lawrence Carin</p>
<p>Poster 20: <b>Distributed Multi-Task Learning</b><br>Jialei Wang, Mladen Kolar, Nathan Srebro</p>
<p>Poster 21: <b>A Fixed-Point Operator for Inference in Variational Bayesian Latent Gaussian Models</b><br>Rishit Sheth, Roni Khardon</p>
<p>Poster 22: <b>Learning Probabilistic Submodular Diversity Models Via Noise Contrastive Estimation</b><br>Sebastian Tschiatschek, Josip Djolonga, Andreas Krause</p>
<p>Poster 23: <b>Fast Saddle-Point Algorithm for Generalized Dantzig Selector and FDR Control with the Ordered $\ell_1$-Norm</b><br>Sangkyun Lee, Damian Brzyski, Malgorzata Bogdan</p>
<p>Poster 24: <b>GLASSES: Relieving The Myopia Of Bayesian Optimisation</b><br>Javier Gonzalez, Michael Osborne, Neil Lawrence</p>
<p>Poster 25: <b>Stochastic Variational Inference for the HDP-HMM</b><br>Aonan Zhang, San Gultekin, John Paisley</p>
<p>Poster 26: <b>Stochastic Neural Networks with Monotonic Activation Functions</b><br>Siamak Ravanbakhsh, Barnabas Poczos, Jeff Schneider, Dale Schuurmans, Russell Greiner</p>
<p>Poster 27: <b>(Bandit) Convex Optimization with Biased Noisy Gradient Oracles</b><br>Xiaowei Hu, Prashanth L.A., András György, Csaba Szepesvari</p>
<p>Poster 28: <b>Variational Gaussian Copula Inference</b><br>Shaobo Han, Xuejun Liao, David Dunson, Lawrence Carin, <span style="font-style: normal"></p>
<p>Poster 29: <b>Low-Rank Approximation of Weighted Tree Automata</b><br>Guillaume Rabusseau, Borja Balle, Shay Cohen</p>
<p>Poster 30: <b>Accelerating Optimization via Adaptive Prediction</b><br>Scott Yang, Mehryar Mohri</p>
<p>Poster 31: <b>Model-based Co-clustering for High Dimensional Sparse Data</b><br>Aghiles Salah, Nicoleta Rogovschi, Mohamed Nadif</p>
<p>Poster 32: <b>DUAL-LOCO: Distributing Statistical Estimation Using Random Projections</b><br>Christina Heinze, Brian McWilliams, Nicolai Meinshausen</p>
<p>Poster 33: <b>High Dimensional Bayesian Optimization via Restricted Projection Pursuit Models</b><br>Chun-Liang Li, Barnabas Poczos, Jeff Schneider, Kirthevasan Kandasamy</p>
<p>Poster 34: <b>On the Use of Non-Stationary Strategies for Solving Two-Player Zero-Sum Markov Games</b><br>Julien Perolat, Bilal Piot, Bruno Scherrer, Olivier Pietquin</p>
<p>Poster 35: <b>Manifold Learning with Adaptive Spectral Transform</b><br>Hanxiao Liu, Yiming Yang</p>
<p>Poster 36: <b>Pseudo-Marginal Slice Sampling</b><br>Iain Murray, Matthew Graham</p>
<p>Poster 37: <b>How to learn a graph from smooth signals</b><br>Vassilis Kalofolias</p>
<p>Poster 38: <b>Pareto Front Identification from Stochastic Bandit Feedback</b><br>Peter Auer, Chao-Kai Chiang, Ronald Ortner, Madalina Drugan</p>
<p>Poster 39: <b>Sketching, embedding and dimensionality reduction in information theoretic spaces</b><br>Amir Ali Abdullah, Suresh Venkatasubramanian, Ravi Kumar, Sergei Vassilvitskii, Andrew McGregor</p>
<p>Poster 40: <b>AdaDelay: Delay Adaptive Distributed Stochastic Optimization</b><br>Suvrit Sra, Adams Wei Yu, Mu Li, Alex Smola</p>
<p>Poster 41: <b>Exponential Stochastic Cellular Automata for Massively Parallel Inference</b><br>Manzil Zaheer, Michael Wick, Jean-Baptiste Tristan, Alex Smola, Guy Steele</p>
<p>Poster 42: <b>Globally Sparse Probabilistic PCA</b><br>Pierre-Alexandre Mattei, Charles Bouveyron, Pierre Latouche</p>
<p>
<font color="red">(Best paper award)</font><br>
Poster 43: <b>Provable Bayesian Inference via Particle Mirror Descent</b><br>Bo Dai, Niao He, Hanjun Dai, Le Song</p>
<p>Poster 44: <b>Unsupervised Feature Selection by Preserving Stochastic Neighbors</b><br>Xiaokai Wei, Philip S. Yu</p>
<p>Poster 45: <b>Improved Learning Complexity in Combinatorial Pure Exploration Bandits</b><br>Victor Gabillon, Alessandro Lazaric, Mohammad Ghavamzadeh, Ronald Ortner, Peter Bartlett</p>
<p>Poster 46: <b>Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces</b><br>William Herlands, Andrew Wilson, Seth Flaxman, Daniel Neill, Wilbert Van Panhuis, Eric Xing, Hannes Nickisch</p>
<p>Poster 47: <b>Optimization as Estimation with Gaussian Processes in Bandit Settings</b><br>Zi Wang, Bolei Zhou, Stefanie Jegelka</p>
<p>Poster 48: <b>Inference for High-dimensional Exponential Family Graphical Models</b><br>Jialei Wang, Mladen Kolar</p>
<p>Poster 49: <b>Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization</b><br>Changyou Chen, David Carlson, Zhe Gan, Chunyuan Li, Lawrence Carin</p>
<p>Poster 50: <b>Fitting Spectral Decay with the $k$-Support Norm</b><br>Andrew McDonald, Massimiliano Pontil, Dimitris Stamos</p>
<p>Poster 51: <b>Early Stopping as Nonparametric Variational Inference</b><br>David Duvenaud, Dougal Maclaurin, Ryan Adams</p>
<p>Poster 52: <b>Bayesian Nonparametric Kernel-Learning</b><br>Junier B. Oliva, Avinava Dubey, Andrew Wilson, Barnabas Poczos, Jeff Schneider, Eric Xing</p>
<p>Poster 53: <b>Tight Variational Bounds via Random Projections and I-Projections</b><br>Lun-Kai Hsu, Tudor Achim, Stefano Ermon</p>
<p>Poster 54: <b>Bethe Learning of Graphical Models via MAP Decoding</b><br>Kui Tang, Nicholas Ruozzi, David Belanger, Tony Jebara</p>
<p>Poster 55: <b>DREVS: Determinantal Regularization for Ensemble Variable Selection</b><br>Veronika Rockova, Gemma Moran, Edward George</p>
<p>Poster 56: <b>Scalable and Sound Low-Rank Tensor Learning</b><br>Hao Cheng, Yaoliang Yu, Xinhua Zhang, Eric Xing, Dale Schuurmans</p>
<p>Poster 57: <b>Efficient Non-negative Matrix Factorization for Discrete Data with Structural Side-Information</b><br>Changwei Hu, Piyush Rai, Lawrence Carin</p>
<p>Poster 58: <b>Scalable Bilinear Non-negative Latent Factor Models for Multi-Relational Data</b><br>Changwei Hu, Piyush Rai, Lawrence Carin</p>
<p>Poster 59: <b>Consistently Estimating Markov Chains with Noisy Aggregate Data</b><br>Garrett Bernstein, Daniel Sheldon</p>
<p>Poster 60: <b>Unwrapping ADMM: Efficient Distributed Computing via Transpose Reduction</b><br>Gavin Taylor, Tom Goldstein</p>
<p>Poster 61: <b>Unbounded Bayesian Optimization via Regularization</b><br>Bobak Shahriari, Alexandre Bouchard-Cote, Nando de Freitas</p>
<p>Poster 62: <b>Non-Gaussian Component Analysis with Log-Density Gradient Estimation</b><br>Hiroaki Sasaki, Gang Niu, Masashi Sugiyama</p>
<p>Poster 63: <b>Parallel Markov Chain Monte Carlo via Spectral Clustering</b><br>Guillaume Basse, Aaron Smith, Natesh Pillai</p>

<h2><span id="poster_session3">Poster Session 3 (May 11)</span></h2>

<b>In addition to AISTATS 2016 posters, MLSS posters will be displayed in this
    session.</b>

<p>Poster 1: <b>Probability Inequalities for Kernel Embeddings in Sampling without Replacement</b><br>Markus Schneider</p>
<p>Poster 2: <b>Tensor vs Matrix Methods: Robust Tensor Decomposition under Block Sparse Perturbations</b><br>Anima Anandkumar, Prateek Jain, Yang Shi, Niranjan Uma Naresh</p>
<p>Poster 3: <b>Nearly optimal classification for semimetrics</b><br>Lee-Ad Gottlieb, Aryeh Kontorovich, Pinhas Nisnevitch</p>
<p>Poster 4: <b>Large-Scale Semi-Supervised Learning Using Streaming Approximation</b><br>Sujith Ravi, Qiming Diao</p>
<p>Poster 5: <b>Low-Rank and Sparse Structure Pursuit via Alternating Minimization</b><br>Quanquan Gu, Zhaoran Wang, Han Liu</p>
<p>Poster 6: <b>Tractable and Scalable Schatten Quasi-Norm Approximations for Rank Minimization</b><br>Fanhua Shang, Yuanyuan Liu, James Cheng</p>
<p>
<font color="red">(Best paper award)</font><br>
Poster 7: <b>Scalable geometric density estimation</b><br>Ye Wang, Antonio Canale, David Dunson</p>
<p>Poster 8: <b>Ordered Weighted l1 Regularized Regression with Strongly Correlated Covariates: Theoretical Aspects</b><br>Mario Figueiredo, Robert Nowak</p>
<p>Poster 9: <b>A Convex Surrogate Operator for General Non-Modular Loss Functions</b><br>Jiaqian Yu, Matthew Blaschko</p>
<p>Poster 10: <b>Online learning with noisy side observations</b><br>Tomáš Kocák, Gergely Neu, Michal Valko</p>
<p>Poster 11: <b>Black-Box Policy Search with Probabilistic Programs</b><br>Jan-Willem Vandemeent, Brooks Paige, David Tolpin, Frank Wood</p>
<p>Poster 12: <b>Efficient Bregman Projections onto the Generalized Permutahedron</b><br>Cong Han Lim, Stephen Wright</p>
<p>Poster 13: <b>Searching for Generalized Instrumental Variables</b><br>Benito Van der Zander, Maciej Liśkiewicz</p>
<p>Poster 14: <b>Controlling Bias in Adaptive Data Analysis Using Information Theory</b><br>Daniel Russo, James Zou</p>
<p>Poster 15: <b>A Column Generation Bound Minimization Approach with PAC-Bayesian Generalization Guarantees</b><br>François Laviolette, Mario Marchand, Jean-Francis Roy</p>
<p>Poster 16: <b>Graph Sparsification Approaches for Laplacian Smoothing Problems</b><br>Veeru Sadhanala, Yu-Xiang Wang, Ryan Tibshirani, Alex Smola</p>
<p>Poster 17: <b>Scalable Exemplar Clustering and Facility Location via Augmented Block Coordinate Descent with Column Generation</b><br>Ian En-Hsu Yen, Dmitry Malioutov, Abhishek Kumar</p>
<p>Poster 18: <b>Robust Covariate Shift Regression</b><br>Xiangli Chen, Brian Ziebart, Mathew Monfort, Anqi Liu</p>
<p>Poster 19: <b>On Lloyd's algorithm: new theoretical insights for clustering in practice</b><br>Cheng Tang, Claire Monteleoni</p>
<p>Poster 20: <b>Towards stability and optimality in stochastic gradient descent</b><br>Panos Toulis, Dustin Tran, Edo Airoldi</p>
<p>Poster 21: <b>Communication Efficient Distributed Agnostic Boosting</b><br>Shang-Tse Chen, Maria-Florina Balcan, Duen Horng Chau</p>
<p>Poster 22: <b>Differentially Private Causal Inference</b><br>Matt Kusner, Yu Sun, Karthik Sridharan, Kilian Weinberger</p>
<p>Poster 23: <b>Efficient Sampling for k-Determinantal Point Processes</b><br>Chengtao Li, Stefanie Jegelka,Suvrit Sra</p>
<p>Poster 24: <b>A Fast and Reliable Policy Improvement Algorithm</b><br>Yasin Abbasi-Yadkori, Peter Bartlett, Stephen Wright</p>
<p>Poster 25: <b>Learning Sigmoid Belief Networks via Monte Carlo Expectation Maximization</b><br>Zhao Song, Ricardo Henao, David Carlson, Lawrence Carin</p>
<p>Poster 26: <b>Active Learning Algorithms for Graphical Model Selection</b><br>Gautamd Dasarathy, Aarti Singh, Maria-Florina Balcan, Jong Park</p>
<p>Poster 27: <b>Streaming Kernel Principal Component Analysis</b><br>Jeff Phillips, Mina Ghashami, Daniel Perry</p>
<p>Poster 28: <b>Back to the future: Radial Basis Function networks revisited</b><br>Qichao Que, Mikhail Belkin</p>
<p>Poster 29: <b>Cut Pursuit: fast algorithms to learn piecewise constant functions</b><br>Loic Landrieu, Guillaume Obozinski</p>
<p>Poster 30: <b>Loss Bounds and Time Complexity for Speed Priors</b><br>Daniel Filan, Jan Leike, Marcus Hutter</p>
<p>Poster 31: <b>NYTRO: When Subsampling Meets Early Stopping</b><br>Raffaello Camoriano, Lorenzo Rosasco, Alessandro Rudi, Tomás M.  Angles L.</p>
<p>Poster 32: <b>Spectral M-estimation</b><br>Dustin Tran, Minjae Kim, Finale Doshi-Velez</p>
<p>Poster 33: <b>Chained Gaussian Processes</b><br>Alan Saul, James Hensman, Aki Vehtari, Neil Lawrence</p>
<p>
<font color="red">(Best paper award)</font><br>
Poster 34: <b>Multiresolution Matrix Compression</b><br>Nedelina Teneva, Pramod Kaushik Mudrakarta, Risi Kondor</p>
<p>Poster 35: <b>Supervised neighborhoods for distributed nonparametric regression</b><br>Adam Bloniarz, Ameet Tawalkar, Bin Yu, Christopher Wu</p>
<p>Poster 36: <b>Global Convergence of a Grassmannian Gradient Descent Algorithm for Subspace Estimation</b><br>Laura Balzano, Dejiao Zhang</p>
<p>Poster 37: <b>Online and Distributed Bayesian Moment Matching for SPNs</b><br>Abdullah Rashwan, Pascal Poupart, Han Zhao</p>
<p>Poster 38: <b>Online (and Offline) Robust PCA: Novel Algorithms and Correctness Results</b><br>Jinchun Zhan, Brian Lois, Han Guo, Namrata Vaswani</p>
<p>Poster 39: <b>Parallel Majorization Minimization with Dynamically Restricted Domains for Nonconvex Optimization</b><br>Yan Kaganovsky, Ikenna Odinaka, David Carlson, Lawrence Carin</p>
<p>Poster 40: <b>Discriminative Structure Learning of Arithmetic Circuits</b><br>Amirmohammad Rooshenas, Daniel Lowd</p>
<p>Poster 41: <b>One Scan 1-Bit Compressed Sensing</b><br>Ping Li</p>

<h2>MLSS Posters in Session 3 (May 11)</h2>
<p>Poster 42: <b>Neural Networks in Sensors</b><br> Gilles Backhus</p>
<p>Poster 43: <b>Accurate high-dimensional discrete inference with EP approximations. Applications in digital communications.</b><br> Pablo Martinez Olmos</p>
<p>Poster 44: <b>Early identification of patients at risk of drop-out during a 4-week inpatient psychiatric rehabilitation program</b><br> Massimiliano Grassi</p>
<p>Poster 45: <b>Active Exploration and Learning of Skill Hierarchies</b><br> Sébastien Forestier</p>
<p>Poster 46: <b>Online Active Learning for Linear Regression</b><br> Carlos Riquelme</p>
<p>Poster 47: <b>Non-monotone Quadratic Potential Games</b><br> Javier Zazo Ruiz</p>
<p>Poster 48: <b>Automated music composition – Bridging time scales in neural networks</b><br> Florian Colombo</p>
<p>Poster 49: <b>Semi-Supervised Hidden Markov Jump Processes for Human Activity Recognition</b><br> Alfredo Nazábal</p>
<p>Poster 50: <b>Semantic Parsing through Seq2seq Prediction of Canonical Forms</b><br> Chunyang Xiao</p>
<p>Poster 51: <b>Effect of running on spatial integration in different classes of neurons of mouse visual cortex</b><br> Mario Dipoppa</p>
<p>Poster 52: <b>HIV Therapy Selection with infinite POMDPs</b><br> Sonali Parbhoo</p>
<p>Poster 53: <b>Modeling the human-algorithm interaction in recommendation systems</b><br> Sven Schmit</p>
<p>Poster 54: <b>SGD-Trust: Stabilizing Stochastic Gradients</b><br> Arturo Fernandez</p>
<p>Poster 55: <b>Extreme Bandits with Graph Side Information</b><br> Andrea Locatelli</p>
<p>Poster 56: <b>Bandits with Knapsacks for Interactive Education Software</b><br> Ciara Pike-Burke</p>
<p>Poster 57: <b>Predicting Heart Failure Deterioration using Physical Activity Recordings</b><br> Johanna Ernst</p>
<p>Poster 58: <b>Electronic deep neural networks for ultra-efficient data processing</b><br> Jonathan Binas</p>
<p>Poster 59: <b>Deep learning methods for semantic parsing on WikiData</b><br> Daniil Sorokin</p>
<p>Poster 60: <b>Simplifying Regularizing and Strenghtening Sum-Product Networks Structure Learning</b><br>  Antonio Vergari</p>
<p>Poster 61: <b>Artificial neuron meets real neuron: pattern selectivity in V4</b><br> Reza Abbasi Asl</p>
<p>Poster 62: <b>Efficient Bayesian regression with the Laplacian kernel using the Mondrian process</b><br> Matej Balog</p>
<p>Poster 63: <b>Acceleration of convolutional neural networks</b><br> Aizhan Ibraimova</p>
<p>Poster 64: <b>Neural Network Interpolators for the Large Scale Structure of the Universe</b><br> Joseph Faulkner</p>
<p>Poster 65: <b>Automated quantitative analyses of collectively migrating malaria parasites</b><br> Sabrina Rossberger</p>
<p>Poster 66: <b>Using cloud computing to study the Extended Spring Indices.</b><br> Emma Izquierdo-Verdiguier</p>
<p>Poster 67: <b>How to find a biomarker to accurately diagnose the early stage of Parkinson’s Disease? An analysis of time-frequency activity and connectivity patterns using resting-state fMRI</b><br> Katherine Baquero</p>
<p>Poster 68: <b>Hidden semi-Markov Models comparison with GMM and HMM and applications to audio processing</b><br>  Lilian Besson</p>
<p>Poster 69: <b>Modeling the Dynamics of Online Learning Activity</b><br> Charalampos Mavroforakis</p>
<p>Poster 70: <b>Generic Properties of Scattering Networks</b><br> Thomas Wiatowski</p>
<p>Poster 71: <b>Causal Information Bottleneck</b><br> Aleksander Wieczorek</p>

</div>

<!--back up -->

<!--<h2><span id="poster_session1">Poster Session 1 (May 9)</span></h2>-->

<!--<p>5: <b>Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures</b><br>Mario Lucic, Olivier Bachem, Andreas Krause</p>-->
<!--<p>8: <b>Revealing Graph Bandits for Maximizing Local Influence</b><br>Alexandra Carpentier, Michal Valko</p>-->
<!--<p>9: <b>Convex block-sparse linear regression with expanders, provably</b><br>Anastasios Kyrillidis, Bubacarr Bah, Rouzbeh Hasheminezhad, Quoc Tran Dinh, Luca Baldassarre, Volkan Cevher</p>-->
<!--<p>12: <b>Clamping Improves TRW and Mean Field Approximations</b><br>Adrian Weller, Justin Domke</p>-->
<!--<p>21: <b>Control Functionals for Quasi-Monte Carlo Integration</b><br>Chris Oates, Mark Girolami</p>-->
<!--<p>26: <b>Sparse Representation of Multivariate Extremes with Applications to Anomaly Ranking</b><br>Nicolas Goix, Anne Sabourin, Stéphan Clémençon</p>-->
<!--<p>30: <b>A Robust-Equitable Copula Dependence Measure for Feature Selection</b><br>Yale Chang, Yi Li, Adam Ding, Jennifer Dy</p>-->
<!--<p>34: <b>Random Forest for the Contextual Bandit Problem</b><br>Raphaël Féraud, Robin Allesiardo, Tanguy Urvoy, Fabrice Clérot</p>-->
<!--<p>50: <b>Learning Sparse Additive Models with Interactions in High Dimensions</b><br>Hemant Tyagi, Anastasios Kyrillidis, Bernd Gärtner, Andreas Krause</p>-->
<!--<p>51: <b>Bipartite Correlation Clustering - Maximizing Agreements</b><br>Megasthenis Asteris, Anastasios Kyrillidis, Dimitris Papailiopoulos, Alexandros Dimakis</p>-->
<!--<p>52: <b>Breaking Sticks and Ambiguities with Adaptive Skip-gram</b><br>Sergey Bartunov, Dmitry Kondrashkin, Anton Osokin, Dmitry Vetrov</p>-->
<!--<p>58: <b>Top Arm Identification in Multi-Armed Bandits with Batch Arm Pulls</b><br>Kwang-Sung Jun, Kevin Jamieson, Robert Nowak, Xiaojin Zhu</p>-->
<!--<p>63: <b>Limits on Sparse Support Recovery via Linear Sketching with Random Expander Matrices</b><br>Jonathan Scarlett, Volkan Cevher</p>-->
<!--<p>68: <b>Maximum Likelihood for Variance Estimation in High-Dimensional Linear Models</b><br>Lee Dicker, Murat Erdogdu</p>-->
<!--<p>76: <b>Scalable Gaussian Process Classification via Expectation Propagation</b><br>Daniel Hernandez-Lobato, Jose Miguel Hernandez-Lobato</p>-->
<!--<p>77: <b>Precision Matrix Estimation in High Dimensional Gaussian Graphical Models with Faster Rates</b><br>Lingxiao Wang, Quanquan Gu</p>-->
<!--<p>80: <b>On the Reducibility of Submodular Functions</b><br>Jincheng Mei, Hao Zhang, Bao-Liang Lu</p>-->
<!--<p>82: <b>Accelerated Stochastic Gradient Descent for Minimizing Finite Sums</b><br>Atsushi Nitanda</p>-->
<!--<p>83: <b>Fast Convergence of Online Pairwise Learning Algorithms</b><br>Martin Boissier, Siwei Lyu, Yiming Ying, Ding-Xuan Zhou</p>-->
<!--<p>85: <b>Computationally Efficient Bayesian Learning of Gaussian Process State Space Models</b><br>Andreas Svensson, Arno Solin, Simo Särkkä, Thomas Schön</p>-->
<!--<p>88: <b>Generalized Ideal Parent (GIP): Discovering non-Gaussian Hidden Variables</b><br>Yaniv Tenzer, Gal Elidan</p>-->
<!--<p>92: <b>On Sparse Variational Methods and the Kullback-Leibler Divergence between Stochastic Processes</b><br>Alex Matthews, James Hensman, Richard Turner, Zoubin Ghahramani</p>-->
<!--<p>95: <b>Non-stochastic Best Arm Identification and Hyperparameter Optimization</b><br>Kevin Jamieson Ameet Tawalkar</p>-->
<!--<p>99: <b>A Linearly-Convergent Stochastic L-BFGS Algorithm</b><br>Philipp Moritz, Robert Nishihara, Michael Jordan</p>-->
<!--<p>103: <b>No Regret Bound for Extreme Bandits</b><br>Robert Nishihara, David Lopez-Paz, Leon Bottou</p>-->
<!--<p>105: <b>Online Learning to Rank with Feedback at the Top</b><br>Sougata Chaudhuri, Ambuj Tewari</p>-->
<!--<p>109: <b>Score Permutation Based Finite Sample Inference for Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) Models</b><br>Balazs Csaji</p>-->
<!--<p>111: <b>CRAFT: ClusteR-specific Assorted Feature selecTion</b><br>Vikas Garg, Cynthia Rudin, Tommi Jaakkola</p>-->
<!--<p>127: <b>Time-Varying Gaussian Process Bandit Optimization</b><br>Ilija Bogunovic, Jonathan Scarlett, Volkan Cevher</p>-->
<!--<p>134: <b>Bayes-Optimal Effort Allocation in Crowdsourcing: Bounds and Index Policie</b><br>Weici Hu, Peter Frazier</p>-->
<!--<p>135: <b>Bayesian Markov Blanket Estimation</b><br>Dinu Kaufmann, Sonali Parbhoo, Aleksander Wieczorek, Sebastian Keller, David Adametz, Volker Roth</p>-->
<!--<p>148: <b>Unsupervised Ensemble Learning with Dependent Classifiers</b><br>Ethan Fetaya, Boaz Nadler, Ariel Jaffe, Ting Ting Jiang, Yuval Kluger</p>-->
<!--<p>150: <b>Multi-Level Cause-Effect Systems</b><br>Krzysztof Chalupka, Frederick Eberhardt, Pietro Perona</p>-->
<!--<p>151: <b>Deep Kernel Learning</b><br>Andrew Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric Xing</p>-->
<!--<p>157: <b>Latent Point Process Allocation</b><br>Chris Lloyd, Tom Gunter, Michael Osborne, Stephen Roberts, Tom Nickson</p>-->
<!--<p>161: <b>Bayesian generalised ensemble Markov chain Monte Carlo</b><br>Jes Frellsen, Ole Winther, Zoubin Ghahramani, Jesper Ferkinghoff-Borg</p>-->
<!--<p>162: <b>A Lasso-based Sparse Knowledge Gradient Policy for Sequential Optimal Learning</b><br>Yan Li, Han Liu, Warren Powell</p>-->
<!--<p>166: <b>Optimal Statistical and Computational Rates for One Bit Matrix Completion</b><br>Quanquan Gu, Renkun Ni</p>-->
<!--<p>169: <b>PAC-Bayesian Bounds based on the Rényi Divergence</b><br>Luc Bégin, Pascal Germain, François Laviolette, Jean-Francis Roy</p>-->
<!--<p>171: <b>Simple and Scalable Constrained Clustering: A Generalized Spectral Method</b><br>Mihai Cucuringu, Ioannis Koutis, Gary Miller, Richard Peng, Sanjay Chawla</p>-->
<!--<p>178: <b>Geometry Aware Mappings for High Dimensional Sparse Factors</b><br>Avradeep Bhowmik, Nathan Liu, Erheng Zhong, Badri Bhaskar, Suju Rajan</p>-->
<!--<p>180: <b>Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree</b><br>Chen-Yu Lee, Patrick Gallagher, Zhuowen Tu</p>-->
<!--<p>181: <b>Rivalry of Two Families of Algorithms for Memory-Restricted Streaming PCA</b><br>Chun-Liang Li, Hsuan-Tien Lin, Chi-Jen Lu</p>-->
<!--<p>185: <b>Quantization based Fast Inner Product Search</b><br>Ruiqi Guo, Sanjiv Kumar, Krzysztof Choromanski, David Simcha</p>-->
<!--<p>186: <b>An Improved Convergence Analysis of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization</b><br>Tuo Zhao, Xingguo Li, Raman Arora, Han Liu, Mingyi Hong</p>-->
<!--<p>187: <b>Learning Structured Low-Rank Representation via Matrix Factorization</b><br>Jie Shen,Ping Li</p>-->
<!--<p>188: <b>A PAC RL Algorithm for Episodic POMDPs</b><br>Zhaohan Guo, Shayan Doroudi, Emma Brunskill</p>-->
<!--<p>191: <b>Large-Scale Optimization Algorithms for Sparse Conditional Gaussian Graphical Models</b><br>Calvin McCarter, Seyoung Kim</p>-->
<!--<p>194: <b>Graph Connectivity in Noisy Sparse Subspace Clustering</b><br>Yining Wang, Yu-Xiang Wang, Aarti Singh</p>-->
<!--<p>198: <b>The Nonparametric Kernel Bayes Smoother</b><br>Yu Nishiyama, Amir Afsharinejad, Shunsuke Naruse, Byron Boots, Le Song</p>-->
<!--<p>199: <b>Universal Models of Multivariate Temporal Point Processes</b><br>Asela Gunawardana, Chris Meek</p>-->
<!--<p>200: <b>Nonparametric Budgeted Stochastic Gradient Descent</b><br>Trung Le, Vu Nguyen, Dinh Phung</p>-->
<!--<p>201: <b>Online Relative Entropy Policy Search using Reproducing Kernel Hilbert Space Embeddings</b><br>Zhitang Chen, Pascal Poupart, Yanhui Geng</p>-->
<!--<p>204: <b>Relationship between PreTraining and Maximum Likelihood Estimation in Deep Boltzmann Machines</b><br>Muneki Yasuda</p>-->
<!--<p>211: <b>Enumerating Equivalence Classes of Bayesian Networks using CPDAG Graphs</b><br>Eunice Chen, Arthur Choi, Adnan Darwiche</p>-->
<!--<p>218: <b>NuC-MKL: A Convex Approach to Non Linear Multiple Kernel Learning</b><br>Eli Meirom, Pavel Kisilev</p>-->
<!--<p>413: <b>Improper Deep Kernels</b><br>Uri Heinemann, Roi Livni, Elad Eban, Gal Elidan, Amir Globerson</p>-->
<!--<p>515: <b>Randomization and The Pernicious Effects of Limited Budgets on Auction Experiments</b><br>Guillaume Basse, Hossein Azari, Diane Lambert</p>-->
<!--<p>546: <b>Mondrian Forests for Large-Scale Regression when Uncertainty Matters</b><br>Balaji Lakshminarayanan, Dan Roy, Yee Whye Teh</p>-->

<!--<h2><span id="poster_session2">Poster Session 2 (May 10)</span></h2>-->

<!--<p>10: <b>C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching</b><br>Daniel Ritchie, Andreas Stuhlmüller, Noah Goodman</p>-->
<!--<p>13: <b>Tightness of LP Relaxations for Almost Balanced Models</b><br>Adrian Weller, David Sontag</p>-->
<!--<p>37: <b>Inverse Reinforcement Learning with Simultaneous Estimation of Rewards and Dynamics</b><br>Michael Herman, Tobias Gindele, Jörg Wagner, Felix Schmitt, Wolfram Burgard</p>-->
<!--<p>106: <b>Survey Propagation beyond Constraint Satisfaction Problems</b><br>Christopher Srinivasa, Siamak Ravanbakhsh, Brendan Frey</p>-->
<!--<p>147: <b>Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation</b><br>Søren Hauberg, Oren Freifeld, Anders Boesen Lindbo Larsen, John Fisher, Lars Hansen</p>-->
<!--<p>159: <b>K2-ABC: Approximate Bayesian Computation with Kernel Embeddings</b><br>Mijung Park, Wittawat Jitkrittum, Dino Sejdinovic</p>-->
<!--<p>222: <b>Fast Dictionary Learning with a Smoothed Wasserstein Loss</b><br>Antoine Rolet, Marco Cuturi, Gabriel Peyré</p>-->
<!--<p>231: <b>New Resistance Distances with Global Information on Large Graphs</b><br>Canh Hao Nguyen, Hiroshi Mamitsuka</p>-->
<!--<p>232: <b>Batch Bayesian Optimization via Local Penalization</b><br>Javier Gonzalez, Zhenwen Dai, Philipp Hennig, Neil Lawrence</p>-->
<!--<p>234: <b>Learning relationships between data obtained independently</b><br>Alexandra Carpentier, Teresa Schlueter</p>-->
<!--<p>236: <b>Fast and Scalable Structural SVM with Slack Rescaling</b><br>Heejin Choi, Ofer Meshi, Nathan Srebro</p>-->
<!--<p>243: <b>Probabilistic Approximate Least-Squares</b><br>Simon Bartels, Philipp Hennig</p>-->
<!--<p>246: <b>Approximate Inference Using DC Programming For Collective Graphical Models</b><br>Thien Nguyen, Akshat Kumar, Hoong Chuin Lau, Daniel Sheldon</p>-->
<!--<p>261: <b>Sequential Inference for Deep Gaussian Process</b><br>Yali Wang, Marcus Brubaker, Brahim Chaib-draa, Raquel Urtasun</p>-->
<!--<p>263: <b>Variational Tempering</b><br>Stephan Mandt, Farhan Abrol, Rajesh Ranganath, James McInerney, David Blei</p>-->
<!--<p>267: <b>On Convergence of Model Parallel Proximal Gradient Algorithm for Stale Synchronous Parallel System</b><br>Yi Zhou, Yaoliang Yu, Wei Dai, Yingbin Liang, Eric Xing</p>-->
<!--<p>273: <b>Scalable MCMC for Mixed Membership Stochastic Blockmodels</b><br>Wenzhe Li, Max Welling, Sungjin Ahn</p>-->
<!--<p>275: <b>Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo</b><br>Markus Heinonen, Henrik Mannerström, Juho Rousu, Samuel Kaski, Harri Lähdesmäki</p>-->
<!--<p>276: <b>A Deep Generative Deconvolutional Image Model</b><br>Yunchen Pu, Xin Yuan, Andrew Stevens, Chunyuan Li, Lawrence Carin</p>-->
<!--<p>278: <b>Distributed Multi-Task Learning</b><br>Jialei Wang, Mladen Kolar, Nathan Srebro</p>-->
<!--<p>287: <b>A Fixed-Point Operator for Inference in Variational Bayesian Latent Gaussian Models</b><br>Rishit Sheth, Roni Khardon</p>-->
<!--<p>288: <b>Learning Probabilistic Submodular Diversity Models Via Noise Contrastive Estimation</b><br>Sebastian Tschiatschek, Josip Djolonga, Andreas Krause</p>-->
<!--<p>291: <b>Fast Saddle-Point Algorithm for Generalized Dantzig Selector and FDR Control with the Ordered $\ell_1$-Norm</b><br>Sangkyun Lee, Damian Brzyski, Malgorzata Bogdan</p>-->
<!--<p>302: <b>GLASSES: Relieving The Myopia Of Bayesian Optimisation</b><br>Javier Gonzalez, Michael Osborne, Neil Lawrence</p>-->
<!--<p>304: <b>Stochastic Variational Inference for the HDP-HMM</b><br>Aonan Zhang, San Gultekin, John Paisley</p>-->
<!--<p>305: <b>Stochastic Neural Networks with Monotonic Activation Functions</b><br>Siamak Ravanbakhsh, Barnabas Poczos, Jeff Schneider, Dale Schuurmans, Russell Greiner</p>-->
<!--<p>308: <b>(Bandit) Convex Optimization with Biased Noisy Gradient Oracles</b><br>Xiaowei Hu, Prashanth L.A., András György, Csaba Szepesvari</p>-->
<!--<p>311: <b>Variational Gaussian Copula Inference</b><br>Shaobo Han, Xuejun Liao, David Dunson, Lawrence Carin, <span style="font-style: normal"></p>-->
<!--<p>314: <b>Low-Rank Approximation of Weighted Tree Automata</b><br>Guillaume Rabusseau, Borja Balle, Shay Cohen</p>-->
<!--<p>317: <b>Accelerating Optimization via Adaptive Prediction</b><br>Scott Yang, Mehryar Mohri</p>-->
<!--<p>325: <b>Model-based Co-clustering for High Dimensional Sparse Data</b><br>Aghiles Salah, Nicoleta Rogovschi, Mohamed Nadif</p>-->
<!--<p>326: <b>DUAL-LOCO: Distributing Statistical Estimation Using Random Projections</b><br>Christina Heinze, Brian McWilliams, Nicolai Meinshausen</p>-->
<!--<p>332: <b>High Dimensional Bayesian Optimization via Restricted Projection Pursuit Models</b><br>Chun-Liang Li, Barnabas Poczos, Jeff Schneider, Kirthevasan Kandasamy</p>-->
<!--<p>335: <b>On the Use of Non-Stationary Strategies for Solving Two-Player Zero-Sum Markov Games</b><br>Julien Perolat, Bilal Piot, Bruno Scherrer, Olivier Pietquin</p>-->
<!--<p>338: <b>Manifold Learning with Adaptive Spectral Transform</b><br>Hanxiao Liu, Yiming Yang</p>-->
<!--<p>340: <b>Pseudo-Marginal Slice Sampling</b><br>Iain Murray, Matthew Graham</p>-->
<!--<p>343: <b>How to learn a graph from smooth signals</b><br>Vassilis Kalofolias</p>-->
<!--<p>351: <b>Pareto Front Identification from Stochastic Bandit Feedback</b><br>Peter Auer, Chao-Kai Chiang, Ronald Ortner, Madalina Drugan</p>-->
<!--<p>353: <b>Sketching, embedding and dimensionality reduction in information theoretic spaces</b><br>Amir Ali Abdullah, Suresh Venkatasubramanian, Ravi Kumar, Sergei Vassilvitskii, Andrew McGregor</p>-->
<!--<p>355: <b>AdaDelay: Delay Adaptive Distributed Stochastic Optimization</b><br>Suvrit Sra, Adams Wei Yu, Mu Li, Alex Smola</p>-->
<!--<p>356: <b>Exponential Stochastic Cellular Automata for Massively Parallel Inference</b><br>Manzil Zaheer, Michael Wick, Jean-Baptiste Tristan, Alex Smola, Guy Steele</p>-->
<!--<p>357: <b>Globally Sparse Probabilistic PCA</b><br>Pierre-Alexandre Mattei, Charles Bouveyron, Pierre Latouche</p>-->
<!--<p>361: <b>Provable Bayesian Inference via Particle Mirror Descent</b><br>Bo Dai, Niao He, Hanjun Dai, Le Song</p>-->
<!--<p>365: <b>Unsupervised Feature Selection by Preserving Stochastic Neighbors</b><br>Xiaokai Wei, Philip S. Yu</p>-->
<!--<p>366: <b>Improved Learning Complexity in Combinatorial Pure Exploration Bandits</b><br>Victor Gabillon, Alessandro Lazaric, Mohammad Ghavamzadeh, Ronald Ortner, Peter Bartlett</p>-->
<!--<p>369: <b>Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces</b><br>William Herlands, Andrew Wilson, Seth Flaxman, Daniel Neill, Wilbert Van Panhuis, Eric Xing, Hannes Nickisch</p>-->
<!--<p>370: <b>Optimization as Estimation with Gaussian Processes in Bandit Settings</b><br>Zi Wang, Bolei Zhou, Stefanie Jegelka</p>-->
<!--<p>378: <b>Inference for High-dimensional Exponential Family Graphical Models</b><br>Jialei Wang, Mladen Kolar</p>-->
<!--<p>381: <b>Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization</b><br>Changyou Chen, David Carlson, Zhe Gan, Chunyuan Li, Lawrence Carin</p>-->
<!--<p>383: <b>Fitting Spectral Decay with the $k$-Support Norm</b><br>Andrew McDonald, Massimiliano Pontil, Dimitris Stamos</p>-->
<!--<p>385: <b>Early Stopping as Nonparametric Variational Inference</b><br>David Duvenaud, Dougal Maclaurin, Ryan Adams</p>-->
<!--<p>389: <b>Bayesian Nonparametric Kernel-Learning</b><br>Junier B. Oliva, Avinava Dubey, Andrew Wilson, Barnabas Poczos, Jeff Schneider, Eric Xing</p>-->
<!--<p>390: <b>Tight Variational Bounds via Random Projections and I-Projections</b><br>Lun-Kai Hsu, Tudor Achim, Stefano Ermon</p>-->
<!--<p>392: <b>Bethe Learning of Graphical Models via MAP Decoding</b><br>Kui Tang, Nicholas Ruozzi, David Belanger, Tony Jebara</p>-->
<!--<p>395: <b>DREVS: Determinantal Regularization for Ensemble Variable Selection</b><br>Veronika Rockova, Gemma Moran, Edward George</p>-->
<!--<p>399: <b>Scalable and Sound Low-Rank Tensor Learning</b><br>Hao Cheng, Yaoliang Yu, Xinhua Zhang, Eric Xing, Dale Schuurmans</p>-->
<!--<p>400: <b>Efficient Non-negative Matrix Factorization for Discrete Data with Structural Side-Information</b><br>Changwei Hu, Piyush Rai, Lawrence Carin</p>-->
<!--<p>404: <b>Scalable Bilinear Non-negative Latent Factor Models for Multi-Relational Data</b><br>Changwei Hu, Piyush Rai, Lawrence Carin</p>-->
<!--<p>406: <b>Consistently Estimating Markov Chains with Noisy Aggregate Data</b><br>Garrett Bernstein, Daniel Sheldon</p>-->
<!--<p>409: <b>Unwrapping ADMM: Efficient Distributed Computing via Transpose Reduction</b><br>Gavin Taylor, Tom Goldstein</p>-->
<!--<p>415: <b>Unbounded Bayesian Optimization via Regularization</b><br>Bobak Shahriari, Alexandre Bouchard-Cote, Nando de Freitas</p>-->
<!--<p>421: <b>Non-Gaussian Component Analysis with Log-Density Gradient Estimation</b><br>Hiroaki Sasaki, Gang Niu, Masashi Sugiyama</p>-->
<!--<p>477: <b>Parallel Markov Chain Monte Carlo via Spectral Clustering</b><br>Guillaume Basse, Aaron Smith, Natesh Pillai</p>-->

<!--<h2><span id="poster_session3">Poster Session 3 (May 11)</span></h2>-->

<!--<b>In addition to AISTATS 2016 posters, MLSS posters will be displayed in this-->
    <!--session.</b>-->

<!--<p>23: <b>Probability Inequalities for Kernel Embeddings in Sampling without Replacement</b><br>Markus Schneider</p>-->
<!--<p>104: <b>Tensor vs Matrix Methods: Robust Tensor Decomposition under Block Sparse Perturbations</b><br>Anima Anandkumar, Prateek Jain, Yang Shi, Niranjan Uma Naresh</p>-->
<!--<p>154: <b>Nearly optimal classification for semimetrics</b><br>Lee-Ad Gottlieb, Aryeh Kontorovich, Pinhas Nisnevitch</p>-->
<!--<p>190: <b>Large-Scale Semi-Supervised Learning Using Streaming Approximation</b><br>Sujith Ravi, Qiming Diao</p>-->
<!--<p>212: <b>Low-Rank and Sparse Structure Pursuit via Alternating Minimization</b><br>Quanquan Gu, Zhaoran Wang, Han Liu</p>-->
<!--<p>221: <b>Tractable and Scalable Schatten Quasi-Norm Approximations for Rank Minimization</b><br>Fanhua Shang, Yuanyuan Liu, James Cheng</p>-->
<!--<p>321: <b>Scalable geometric density estimation</b><br>Ye Wang, Antonio Canale, David Dunson</p>-->
<!--<p>346: <b>Ordered Weighted l1 Regularized Regression with Strongly Correlated Covariates: Theoretical Aspects</b><br>Mario Figueiredo, Robert Nowak</p>-->
<!--<p>374: <b>A Convex Surrogate Operator for General Non-Modular Loss Functions</b><br>Jiaqian Yu, Matthew Blaschko</p>-->
<!--<p>423: <b>Online learning with noisy side observations</b><br>Tomáš Kocák, Gergely Neu, Michal Valko</p>-->
<!--<p>424: <b>Black-Box Policy Search with Probabilistic Programs</b><br>Jan-Willem Vandemeent, Brooks Paige, David Tolpin, Frank Wood</p>-->
<!--<p>428: <b>Efficient Bregman Projections onto the Generalized Permutahedron</b><br>Cong Han Lim, Stephen Wright</p>-->
<!--<p>430: <b>Searching for Generalized Instrumental Variables</b><br>Benito Van der Zander, Maciej Liśkiewicz</p>-->
<!--<p>437: <b>Provable Tensor Methods for Learning Mixtures of Generalized Linear Models</b><br>Hanie Sedghi, Majid Janzamin, Anima Anandkumar</p>-->
<!--<p>439: <b>Controlling Bias in Adaptive Data Analysis Using Information Theory</b><br>Daniel Russo, James Zou</p>-->
<!--<p>444: <b>A Column Generation Bound Minimization Approach with PAC-Bayesian Generalization Guarantees</b><br>François Laviolette, Mario Marchand, Jean-Francis Roy</p>-->
<!--<p>446: <b>Graph Sparsification Approaches for Laplacian Smoothing Problems</b><br>Veeru Sadhanala, Yu-Xiang Wang, Ryan Tibshirani, Alex Smola</p>-->
<!--<p>451: <b>Scalable Exemplar Clustering and Facility Location via Augmented Block Coordinate Descent with Column Generation</b><br>Ian En-Hsu Yen, Dmitry Malioutov, Abhishek Kumar</p>-->
<!--<p>454: <b>Robust Covariate Shift Regression</b><br>Xiangli Chen, Brian Ziebart, Mathew Monfort, Anqi Liu</p>-->
<!--<p>458: <b>On Lloyd's algorithm: new theoretical insights for clustering in practice</b><br>Cheng Tang, Claire Monteleoni</p>-->
<!--<p>472: <b>Towards stability and optimality in stochastic gradient descent</b><br>Panos Toulis, Dustin Tran, Edo Airoldi</p>-->
<!--<p>473: <b>Communication Efficient Distributed Agnostic Boosting</b><br>Shang-Tse Chen, Maria-Florina Balcan, Duen Horng Chau</p>-->
<!--<p>476: <b>Differentially Private Causal Inference</b><br>Matt Kusner, Yu Sun, Karthik Sridharan, Kilian Weinberger</p>-->
<!--<p>479: <b>Efficient Sampling for k-Determinantal Point Processes</b><br>Chengtao Li, Stefanie Jegelka,Suvrit Sra</p>-->
<!--<p>482: <b>A Fast and Reliable Policy Improvement Algorithm</b><br>Yasin Abbasi-Yadkori, Peter Bartlett, Stephen Wright</p>-->
<!--<p>493: <b>Learning Sigmoid Belief Networks via Monte Carlo Expectation Maximization</b><br>Zhao Song, Ricardo Henao, David Carlson, Lawrence Carin</p>-->
<!--<p>495: <b>Active Learning Algorithms for Graphical Model Selection</b><br>Gautamd Dasarathy, Aarti Singh, Maria-Florina Balcan, Jong Park</p>-->
<!--<p>500: <b>Streaming Kernel Principal Component Analysis</b><br>Jeff Phillips, Mina Ghashami, Daniel Perry</p>-->
<!--<p>501: <b>Back to the future: Radial Basis Function networks revisited</b><br>Qichao Que, Mikhail Belkin</p>-->
<!--<p>503: <b>Cut Pursuit: fast algorithms to learn piecewise constant functions</b><br>Loic Landrieu, Guillaume Obozinski</p>-->
<!--<p>505: <b>Loss Bounds and Time Complexity for Speed Priors</b><br>Daniel Filan, Jan Leike, Marcus Hutter</p>-->
<!--<p>512: <b>NYTRO: When Subsampling Meets Early Stopping</b><br>Raffaello Camoriano, Lorenzo Rosasco, Alessandro Rudi, Tomás M.  Angles L.</p>-->
<!--<p>520: <b>Spectral M-estimation</b><br>Dustin Tran, Minjae Kim, Finale Doshi-Velez</p>-->
<!--<p>523: <b>Chained Gaussian Processes</b><br>Alan Saul, James Hensman, Aki Vehtari, Neil Lawrence</p>-->
<!--<p>526: <b>Multiresolution Matrix Compression</b><br>Nedelina Teneva, Pramod Kaushik Mudrakarta, Risi Kondor</p>-->
<!--<p>528: <b>Supervised neighborhoods for distributed nonparametric regression</b><br>Adam Bloniarz, Ameet Tawalkar, Bin Yu, Christopher Wu</p>-->
<!--<p>538: <b>Global Convergence of a Grassmannian Gradient Descent Algorithm for Subspace Estimation</b><br>Laura Balzano, Dejiao Zhang</p>-->
<!--<p>540: <b>Online and Distributed Bayesian Moment Matching for SPNs</b><br>Abdullah Rashwan, Pascal Poupart, Han Zhao</p>-->
<!--<p>548: <b>Online (and Offline) Robust PCA: Novel Algorithms and Correctness Results</b><br>Jinchun Zhan, Brian Lois, Han Guo, Namrata Vaswani</p>-->
<!--<p>551: <b>Parallel Majorization Minimization with Dynamically Restricted Domains for Nonconvex Optimization</b><br>Yan Kaganovsky, Ikenna Odinaka, David Carlson, Lawrence Carin</p>-->
<!--<p>555: <b>Discriminative Structure Learning of Arithmetic Circuits</b><br>Amirmohammad Rooshenas, Daniel Lowd</p>-->
<!--<p>558: <b>One Scan 1-Bit Compressed Sensing</b><br>Ping Li</p>-->

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