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<h1>AISTATS 2018 Poster Sessions</h1>

<h2>Accepted papers</h2>
    <p>All accepted papers are available <a href="accepted.html">here</a>.</p>

<h2>Poster Format</h2>
<p>The poster board is 0.90m (wide) x 2.10m (high). We recommend <b>A0 portrait</b> as
    the poster size. <b>Please make sure to bring the posters printed to Lanzarote as there are no onsite printing facilities available.</b></p>


<h2><span id=Poster_Session1>Poster Session 1 (April 9)</span></h2><p>Poster 1: <b>Submodularity on Hypergraphs: From Sets to Sequences</b><br> Marko
 Mitrovic</p><p>Poster 2: <b>Regional Multi-Armed Bandits</b><br> Cong
 Shen</p><p>Poster 3: <b>On the challenges of learning with inference networks on sparse  high-dimensional data</b><br> Rahul
 Krishnan</p><p>Poster 4: <b>Integral Transforms from Finite Data: An Application of Gaussian Process Regression to Fourier Analysis</b><br> Luca
 Ambrogioni</p><p>Poster 5: <b>Combinatorial Preconditioners for Proximal Algorithms on Graphs</b><br> Thomas
 Möllenhoff</p><p>Poster 6: <b>Near-Optimal Machine Teaching via Explanatory Teaching Sets</b><br> Yuxin
 Chen</p><p>Poster 7: <b>Factorized Recurrent Neural Architectures for Longer Range Dependence</b><br> Francois
 Belletti</p><p>Poster 8: <b>Nonparametric Preference Completion</b><br> Julian
 Katz-Samuels</p><p>Poster 9: <b>HONES: A Fast and Tuning-free Homotopy Method For Online Newton Step</b><br> Yuting
 Ye</p><p>Poster 10: <b>Robustness of classifiers to uniform \ell_p and Gaussian noise</b><br> Alhussein
 Fawzi</p><p>Poster 11: <b>Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in Distributed SGD</b><br> Sanghamitra
 Dutta</p><p>Poster 12: <b>Comparison Based Learning from Weak Oracles</b><br> Ehsan
 Kazemi</p><p>Poster 13: <b>Teacher Improves Learning by Selecting a Training Subset</b><br> Xiaojin
 Zhu</p><p>Poster 14: <b>Probability–Revealing Samples</b><br> Krzysztof
 Onak</p><p>Poster 15: <b>Topic Compositional Neural Language Model</b><br> Wenlin
 Wang</p><p>Poster 16: <b>Reducing Crowdsourcing to Graphon Estimation Statistically</b><br> Christina
 Lee</p><p>Poster 17: <b>Nonparametric Bayesian sparse graph linear dynamical systems</b><br> Mingyuan
 Zhou</p><p>Poster 18: <b>Learning Structural Weight Uncertainty with Stein Gradient Flows</b><br> Chunyuan
 Li</p><p>Poster 19: <b>The Binary Space Partitioning-Tree Process</b><br> Xuhui
 Fan</p><p>Poster 20: <b>Robust Maximization of Non-Submodular Objectives</b><br> Ilija 
 Bogunovic</p><p>Poster 21: <b>FLAG n’ FLARE: Fast Linearly-Coupled Adaptive Gradient Methods</b><br> Fred
     Roosta</p><p>Poster 22: <b>Cheap Checking for Cloud Computing: Statistical Analysis via Annotated Data Streams</b><br> Chris
 Hickey</p><p>Poster 23: <b>Proximity Variational Inference</b><br> Jaan
 Altosaar</p><p>Poster 24: <b>An Analysis of Categorical Distributional Reinforcement Learning</b><br> Mark
 Rowland</p><p>Poster 25: <b>Parallel and Distributed MCMC via Shepherding Distributions</b><br> Arkabandhu
 Chowdhury</p><p>Poster 26: <b>Inference in Sparse Graphs with Pairwise Measurements and Side Information</b><br> Dylan
 Foster</p><p>Poster 27: <b>IHT dies hard: Provable accelerated Iterative Hard Thresholding</b><br> Anastasios
 Kyrillidis</p><p>Poster 28: <b>High-dimensional Bayesian optimization via additive models with overlapping groups</b><br> Paul
 Rolland</p><p>Poster 29: <b>Learning Hidden Quantum Markov Models</b><br> Siddarth
 Srinivasan</p><p>Poster 30: <b>On denoising noisy modulo 1 samples of a function</b><br> Mihai 
 Cucuringu</p><p>Poster 31: <b>A Generic Approach for Escaping Saddle points</b><br> Manzil
 Zaheer</p><p>Poster 32: <b>Nonlinear Weighted Finite Automata</b><br> Tianyu
 Li</p><p>Poster 33: <b>Few-shot Generative Modelling with Generative Matching Networks</b><br> Sergey
 Bartunov</p><p>Poster 34: <b>A Stochastic Differential Equation Framework for Guiding Online User Activities in Closed Loop</b><br> Yichen
 Wang</p><p>Poster 35: <b>Personalized and Private Peer-to-Peer Machine Learning</b><br> Aurélien
 Bellet</p><p>Poster 36: <b>Matrix-normal models for fMRI analysis</b><br> Michael
 Shvartsman</p><p>Poster 37: <b>A Fast Algorithm for Separated Sparsity via Perturbed Lagrangians</b><br> Slobodan
 Mitrovic</p><p>Poster 38: <b>One-shot Coresets: The Case of k-Clustering</b><br> Olivier
 Bachem</p><p>Poster 39: <b>Iterative Supervised Principal Components</b><br> Juho
 Piironen</p><p>Poster 40: <b>Cause-Effect Inference by Comparing Regression Errors</b><br> Patrick
 Bloebaum</p><p>Poster 41: <b>Graphical Models for Non-Negative Data Using Generalized Score Matching</b><br> Shiqing
 Yu</p><p>Poster 42: <b>Best arm identification in multi-armed bandits with delayed feedback</b><br> Aditya
 Grover</p><p>Poster 43: <b>Approximate Bayesian Computation with Kullback-Leibler Divergence as Data Discrepancy</b><br> Bai
 Jiang</p><p>Poster 44: <b>A Unified Dynamic Approach to Sparse Model Selection</b><br> Chendi
 Huang</p><p>Poster 45: <b>On Statistical Optimality of Variational Bayes</b><br> Debdeep
 Pati</p><p>Poster 46: <b>Stochastic algorithms for entropy-regularized optimal transport problems</b><br> Brahim Khalil
 Abid</p><p>Poster 47: <b>Can clustering scale sublinearly with its clusters? A variational EM acceleration of GMMs and k-means</b><br> Dennis
 Forster</p><p>Poster 48: <b>Learning Generative Models with Sinkhorn Divergences</b><br> Aude
 Genevay</p><p>Poster 49: <b>Product Kernel Interpolation for Scalable Gaussian Processes</b><br> Jacob
 Gardner</p><p>Poster 50: <b>Solving lp-norm regularization with tensor kernels</b><br> Saverio
 Salzo</p><p>Poster 51: <b>Statistically Efficient Estimation for Non-Smooth Probability Densities</b><br>Masaaki Imaizumi,
 Takanori Maehara, Yuichi Yoshida</p><p>Poster 52: <b>Stochastic Zeroth-order Optimization in High Dimensions</b><br>Yining Wang, Arindam Banerjee, Simon Du, Sivaraman Balakrishnan,
 Aarti Singh</p><p>Poster 53: <b>Sparse Linear Isotonic Models</b><br>Sheng Chen,
 Arindam Banerjee</p><p>Poster 54: <b>Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs</b><br>Lawrence Murray, Daniel Lundén, Jan Kudlicka, David Broman, 
 Thomas Schön</p><h2><span id=Poster_Session2>Poster Session 2 (April 10)</span></h2><p>Poster 1: <b>Structured Factored Inference for Probabilistic Programming</b><br> Alison
 OConnor</p><p>Poster 2: <b>Weighted Tensor Decomposition for Learning Latent Variables with Partial Data</b><br> Omer
 Gottesman</p><p>Poster 3: <b>Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach</b><br> Satoshi
 Hara</p><p>Poster 4: <b>Finding Global Optima in Nonconvex Stochastic Semidefinite Optimization with Variance Reduction</b><br> Jinshan
 ZENG</p><p>Poster 5: <b>Plug-in Estimators for Conditional Expectations and Probabilities</b><br> Steffen
 Grunewalder</p><p>Poster 6: <b>Policy Evaluation and Optimization with Continuous Treatments</b><br> Nathan
 Kallus</p><p>Poster 7: <b>Tensor Regression Meets Gaussian Processes</b><br> Rose
 Yu</p><p>Poster 8: <b>Robust Locally-Linear Controllable Embedding</b><br> Ershad
 Banijamali</p><p>Poster 9: <b>Data-Efficient Reinforcement Learning with \\Probabilistic Model Predictive Control</b><br> Marc
 Deisenroth</p><p>Poster 10: <b>Smooth and Sparse Optimal Transport</b><br> Mathieu
 Blondel</p><p>Poster 11: <b>The Power Mean Laplacian for Multilayer Graph Clustering</b><br> Pedro
 Mercado</p><p>Poster 12: <b>Gauged Mini-Bucket Elimination for Approximate Inference</b><br> Adrian
 Weller</p><p>Poster 13: <b>Variational Inference based on Robust Divergences</b><br> Futoshi
 Futami</p><p>Poster 14: <b>Benefits from Superposed Hawkes Processes</b><br> Hongteng
 Xu</p><p>Poster 15: <b>Boosting Variational Inference: an Optimization Perspective</b><br> Francesco 
 Locatello</p><p>Poster 16: <b>Tree-based Bayesian Mixture Model for Competing Risks</b><br> Alexis
 Bellot</p><p>Poster 17: <b>Quotient Normalized Maximum Likelihood Criterion for Learning Bayesian Network Structures</b><br> Tomi
 Silander</p><p>Poster 18: <b>Conditional Gradient Method for Stochastic Submodular Maximization: Closing the Gap</b><br> Aryan
 Mokhtari</p><p>Poster 19: <b>Medoids in Almost-Linear Time via Multi-Armed Bandits</b><br> David
 Tse</p><p>Poster 20: <b>On the Truly Block Eigensolvers via First-Order Riemannian Optimization</b><br> Zhiqiang
 Xu</p><p>Poster 21: <b>Efficient Weight Learning in High-Dimensional Untied MLNs</b><br> Khan Mohammad Al
 Farabi</p><p>Poster 22: <b>Beating Monte Carlo Integration: a Nonasymptotic Study of Kernel Smoothing Methods</b><br> Stephan
Clémençon</p><p>Poster 23: <b>On how complexity effects the stability of a predictor</b><br> Joel
 Ratsaby</p><p>Poster 24: <b>Contextual Bandits with Stochastic Experts</b><br> Rajat
 Sen</p><p>Poster 25: <b>Online Learning with Non-Convex Losses and Non-Stationary Regret</b><br> Xiaobo
 Li</p><p>Poster 26: <b>Non-parametric estimation of Jensen-Shannon Divergence in Generative Adversarial Network training</b><br> Mathieu
 Sinn</p><p>Poster 27: <b>Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure</b><br> Beilun
 Wang</p><p>Poster 28: <b>Zeroth-Order Online Alternating Direction Method of Multipliers: Convergence Analysis and Applications</b><br> Sijia
 Liu</p><p>Poster 29: <b>Robust Vertex Enumeration for Convex Hulls in High Dimensions</b><br> Pranjal
 Awasthi</p><p>Poster 30: <b>Turing: Composable inference for probabilistic programming</b><br> Hong
 Ge</p><p>Poster 31: <b>Combinatorial Penalties: Which structures are preserved by convex relaxations?</b><br> Marwa
 El Halabi</p><p>Poster 32: <b>Metrics for Deep Generative Models</b><br> Nutan
 Chen</p><p>Poster 33: <b>Spectral Algorithms for Computing Fair Support Vector Machines</b><br> Mahbod
 Olfat</p><p>Poster 34: <b>Optimal Submodular Extensions for Marginal Estimation</b><br> Pankaj
 Pansari</p><p>Poster 35: <b>Iterative Spectral Method for Alternative Clustering</b><br> Chieh
 Wu</p><p>Poster 36: <b>Differentially Private Regression with Gaussian Processes</b><br> Michael
 Smith</p><p>Poster 37: <b>Reconstruction Risk of Convolutional Sparse Dictionary Learning</b><br> Shashank
 Singh</p><p>Poster 38: <b>Learning to Round for Discrete Labeling Problems</b><br> Pritish
 Mohapatra</p><p>Poster 39: <b>Direct Learning to Rank And Rerank</b><br> Cynthia
 Rudin</p><p>Poster 40: <b>Linear Stochastic Approximation: Constant Step-Size and Iterate Averaging</b><br> Chandrashekar
 Lakshmi-Narayanan</p><p>Poster 41: <b>Approximate ranking from pairwise comparisons</b><br> Reinhard
 Heckel</p><p>Poster 42: <b>Stochastic Multi-armed Bandits in Constant Space</b><br> Ger
 Yang</p><p>Poster 43: <b>Multi-objective Contextual Bandit Problem with Similarity Information</b><br> Cem
 Tekin</p><p>Poster 44: <b>Scalable Generalized Dynamic Topic Models</b><br> Patrick
 Jähnichen</p><p>Poster 45: <b>Growth-Optimal Portfolio Selection under CVaR Constraints</b><br> Guy
 Uziel</p><p>Poster 46: <b>Statistical Sparse Online Regression: A Diffusion Approximation Perspective</b><br> Junchi
 Li</p><p>Poster 47: <b>Combinatorial Semi-Bandits with Knapsacks</b><br>Karthik Abinav Sankararaman,
 Aleksandrs Slivkins</p><p>Poster 48: <b>Online Continuous Submodular Maximization</b><br>Lin Chen, Hamed Hassani,
 Amin Karbasi</p><p>Poster 49: <b>Convergence of Value Aggregation for Imitation Learning</b><br>Ching-An Cheng,
 Byron Boots</p><p>Poster 50: <b>Competing with Automata-based Expert Sequences</b><br>Scott Yang,
 Mehryar Mohri</p><p>Poster 51: <b>A Simple Analysis for Exp-concave Empirical Minimization with Arbitrary Convex Regularizer </b><br>Tianbao Yang, Zhe Li,
 Lijun Zhang</p><p>Poster 52: <b>Learning linear structural equation models in polynomial time and sample complexity</b><br>Asish Ghoshal,
 Jean Honorio</p><p>Poster 53: <b>Consistent Algorithms for Classification under Complex Losses and Constraints</b><br>Harikrishna
 Narasimhan</p><p>Poster 54: <b>Subsampling for Ridge Regression via Regularized Volume Sampling</b><br>Michal Derezinski,
 Manfred Warmuth</p><h2><span id=Poster_Session3>Poster Session 3 (April 10)</span></h2><p>Poster 1: <b>Group invariance principles for causal generative models</b><br> Michel
 Besserve</p><p>Poster 2: <b>Learning Priors for Invariance</b><br> Eric
 Nalisnick</p><p>Poster 3: <b>Catalyst for Gradient-based Nonconvex Optimization</b><br> Courtney
 Paquette</p><p>Poster 4: <b>Dropout as a Low-Rank Regularizer for Matrix Factorization</b><br> Jacopo
 Cavazza</p><p>Poster 5: <b>Practical Bayesian optimization in the presence of outliers</b><br> Ruben
 Martinez-Cantin</p><p>Poster 6: <b>Fast generalization error bound of deep learning from a kernel perspective</b><br> Taiji
 Suzuki</p><p>Poster 7: <b>Asynchronous Doubly Stochastic Group Regularized Learning</b><br> Bin
 Gu</p><p>Poster 8: <b>The emergence of spectral universality in deep networks</b><br> Jeffrey
 Pennington</p><p>Poster 9: <b>Post Selection Inference with Kernels</b><br> Makoto
 Yamada</p><p>Poster 10: <b>Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information</b><br> Jakob
 Runge</p><p>Poster 11: <b>Nonparametric Sharpe Ratio Function Estimation in Heteroscedastic Regression Models via Convex Optimization</b><br> Joong-Ho
 Won</p><p>Poster 12: <b>SDCA-Powered Inexact Dual Augmented Lagrangian Method for Fast CRF Learning</b><br> Xu
 Hu</p><p>Poster 13: <b>Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models</b><br> Hugh
 Salimbeni</p><p>Poster 14: <b>Semi-Supervised Learning with Competitive Infection Models</b><br> Nir
 Rosenfeld</p><p>Poster 15: <b>Random Subspace with Trees for Feature Selection Under Memory Constraints</b><br> Antonio
 Sutera</p><p>Poster 16: <b>Bayesian Structure Learning for Dynamic Brain Connectivity</b><br> Michael
 Andersen</p><p>Poster 17: <b>Riemannian stochastic quasi-Newton algorithm with variance reduction and its convergence analysis</b><br> Hiroyuki
 Kasai</p><p>Poster 18: <b>Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling</b><br> Hongyi
 Ding</p><p>Poster 19: <b>Efficient Bandit Combinatorial Optimization Algorithm with Zero-suppressed Binary Decision Diagrams</b><br> Shinsaku
 Sakaue</p><p>Poster 20: <b>Online Boosting Algorithms for Multi-label Ranking</b><br> Young Hun
 Jung</p><p>Poster 21: <b>Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization</b><br> Fanhua
 Shang</p><p>Poster 22: <b>Community Detection in Hypergraphs: Optimal Statistical Limit and Efficient Algorithms</b><br> Chung-Yi
 Lin</p><p>Poster 23: <b>Matrix completability analysis via graph k-connectivity</b><br> Dehua
 Cheng</p><p>Poster 24: <b>A Nonconvex Proximal Splitting Algorithm under Moreau-Yosida Regularization</b><br> Emanuel
 Laude</p><p>Poster 25: <b>Reducing optimization to repeated classification</b><br> Tatsunori
 Hashimoto</p><p>Poster 26: <b>Online Ensemble Multi-kernel Learning Adaptive to Non-stationary and Adversarial Environments</b><br> Tianyi
 Chen</p><p>Poster 27: <b>Transfer Learning on fMRI Datasets</b><br> Hejia
 Zhang</p><p>Poster 28: <b>Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data</b><br> William
 Herlands</p><p>Poster 29: <b>Nearly second-order optimality of online joint detection and estimation via one-sample update schemes</b><br> Yang
 Cao</p><p>Poster 30: <b>Outlier Detection and Robust Estimation in Nonparametric Regression</b><br> Weining
 Shen</p><p>Poster 31: <b>Dimensionality Reduced $\ell^{0}$-Sparse Subspace Clustering</b><br> Yingzhen
 Yang</p><p>Poster 32: <b>Sum-Product-Quotient Networks</b><br> Or
 Sharir</p><p>Poster 33: <b>Nonlinear Structured Signal Estimation in High Dimensions via Iterative Hard Thresholding</b><br> Zhuoran
 Yang</p><p>Poster 34: <b>Efficient Bayesian Methods for Counting Processes in Partially Observable Environments</b><br> Ferdian
 Jovan</p><p>Poster 35: <b>Stochastic Three-Composite Convex Minimization with a Linear Operator</b><br> Renbo
 Zhao</p><p>Poster 36: <b>Gradient Layer: Enhancing the Convergence of Adversarial Training for Generative Models</b><br> Atsushi
 Nitanda</p><p>Poster 37: <b>Bootstrapping EM via Power EM and Convergence in the Naive Bayes Model</b><br> Christos
 Tzamos</p><p>Poster 38: <b>Kernel Conditional Exponential Family</b><br> Michael
 Arbel</p><p>Poster 39: <b>Achieving the time of 1-NN but the accuracy of k-NN</b><br> Lirong
 Xue</p><p>Poster 40: <b>Bayesian Approaches to Distribution Regression</b><br> Ho Chung Leon
 Law</p><p>Poster 41: <b>Nested CRP with Hawkes-Gaussian Processes</b><br> Xi
 Tan</p><p>Poster 42: <b>Mixed Membership Word Embeddings for Computational Social Science</b><br> James
 Foulds</p><p>Poster 43: <b>Learning Determinantal Point Processes in Sublinear Time</b><br> Christophe
 Dupuy</p><p>Poster 44: <b>Fully adaptive algorithm for pure exploration in linear bandits</b><br> Liyuan
 Xu</p><p>Poster 45: <b>Variational inference for the multi-armed contextual bandit</b><br> Iñigo
 Urteaga</p><p>Poster 46: <b>A Provable Algorithm for Learning Interpretable Scoring Systems</b><br> Nataliya
 Sokolovska</p><p>Poster 47: <b>An Optimization Approach to Learning Falling Rule Lists</b><br> Chaofan
 Chen</p><p>Poster 48: <b>Fast Threshold Tests for Detecting Discrimination</b><br>Emma Pierson, Sam Corbett-Davies, 
 Sharad Goel</p><p>Poster 49: <b>Parallelised Bayesian Optimisation via Thompson Sampling</b><br>Kirthevasan Kandasamy, Akshay Krishnamurthy,
 Jeff Schneider, Barnabas Poczos</p><p>Poster 50: <b>Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition</b><br>Pavel Izmailov, Dmitry Kropotov,
 Alexander Novikov</p><p>Poster 51: <b>Factorial HMM with Collapsed Gibbs Sampling for optimizing long-term HIV Therapy</b><br>Amit Gruber, Chen Yanover, Tal El-Hay, Yaara Goldschmidt, Anders Sönnerborg,
 Vanni Borghi, Francesca Incardona</p><p>Poster 52: <b>Sketching for Kronecker Product Regression and P-splines</b><br>Huaian Diao, Zhao Song,
 Wen Sun, David Woodruff</p><p>Poster 53: <b>Towards Provable Learning of Polynomial Neural Networks Using Low-Rank Matrix Estimation</b><br>Mohammadreza Soltani, 
 Chinmay Hegde</p><p>Poster 54: <b>Convergence diagnostics for stochastic gradient descent</b><br>Jerry Chee,
 Panos Toulis</p><p>Poster 55: <b>Minimax-Optimal Privacy-Preserving Sparse PCA in Distributed Systems</b><br> Jason Ge</p>
<h2><span id=Poster_Session4>Poster Session 4 (April 11)</span></h2><p>Poster 1: <b>Optimal Cooperative Inference</b><br> Scott Cheng-Hsin
 Yang</p><p>Poster 2: <b>Human Interaction with Recommendation Systems</b><br> Sven
 Schmit</p><p>Poster 3: <b>Convex optimization over intersection of simple sets: improved convergence rate guarantees via exact penalty approach</b><br> Achintya
 Kundu</p><p>Poster 4: <b>Towards Memory-Friendly Deterministic Incremental Gradient Method</b><br> Jiahao
 Xie</p><p>Poster 5: <b>Independently Interpretable Lasso: A New Regularizer for Sparse Regression with Uncorrelated Variables</b><br> Masaaki
 Takada</p><p>Poster 6: <b>AdaGeo: Adaptive Geometric Learning for Optimization and Sampling</b><br> Gabriele
 Abbati</p><p>Poster 7: <b>Large Scale Empirical Risk Minimization via Truncated Adaptive Newton Method</b><br> Mark
 Eisen</p><p>Poster 8: <b>Labeled Graph Clustering via Projected Gradient Descent</b><br> Shiau Hong
 Lim</p><p>Poster 9: <b>Discriminative Learning of Prediction Intervals</b><br> Nir
 Rosenfeld</p><p>Poster 10: <b>Accelerated Stochastic Power Iteration</b><br> Peng
 Xu</p><p>Poster 11: <b>A Bayesian Nonparametric Method for Clustering Imputation and Forecasting in Multivariate Time Series</b><br> FERAS
 SAAD</p><p>Poster 12: <b>Bayesian Multi-label Learning with Sparse Features and Labels</b><br> He
 Zhao</p><p>Poster 13: <b>Robust Active Label Correction</b><br> Christian
 Igel</p><p>Poster 14: <b>Factor Analysis on a Graph</b><br> Masayuki
 Karasuyama</p><p>Poster 15: <b>Reparameterizing the Birkhoff Polytope for Variational Permutation Inference</b><br> Gonzalo
 Mena</p><p>Poster 16: <b>Provable Estimation of the Number of Blocks in Block Models</b><br> BOWEI
 YAN</p><p>Poster 17: <b>Batched Large-scale Bayesian Optimization in High-dimensional Spaces</b><br> Zi
 Wang</p><p>Poster 18: <b>Actor-Critic Fictitious Play in Simultaneous Move Multistage Games</b><br> Julien
 Perolat</p><p>Poster 19: <b>Online Regression with Partial Information: Generalization and Linear Projection</b><br> Shinji
 Ito</p><p>Poster 20: <b>Alpha-expansion is Exact on Stable Instances</b><br> Hunter
 Lang</p><p>Poster 21: <b>Adaptive Sampling for Clustered Ranking</b><br> Sumeet
 Katariya</p><p>Poster 22: <b>Multiphase MCMC Sampling for Parameter Inference in Nonlinear Ordinary Differential Equations</b><br> Alan
 Lazarus</p><p>Poster 23: This poster has been moved</p><p>Poster 24: <b>The Geometry of Random Features</b><br> Adrian
 Weller</p><p>Poster 25: <b>Symmetric Variational Autoencoder and Connections to Adversarial Learning</b><br> Liqun
 Chen</p><p>Poster 26: <b>Learning Sparse Polymatrix Games in Polynomial Time and Sample Complexity</b><br> Asish
 Ghoshal</p><p>Poster 27: <b>A Unified Framework for Nonconvex Low-Rank plus Sparse Matrix Recovery</b><br> Xiao
 Zhang</p><p>Poster 28: <b>Communication-Avoiding Optimization Methods for Distributed Massive-Scale Sparse Inverse Covariance Estimation</b><br> Penporn
 Koanantakool</p><p>Poster 29: <b>Accelerated Stochastic Mirror Descent: From Continuous-time Dynamics to Discrete-time Algorithms</b><br> Pan
 Xu</p><p>Poster 30: <b>On the Statistical Efficiency of Compositional Nonparametric Prediction</b><br> Yixi
 Xu</p><p>Poster 31: <b>Exploiting Strategy-Space Diversity for Batch Bayesian Optimization</b><br> Sunil
 Gupta</p><p>Poster 32: <b>Variational Rejection Sampling</b><br> Aditya
 Grover</p><p>Poster 33: <b>Why adaptively collected data have negative bias and how to correct for it.</b><br> Xinkun
 Nie</p><p>Poster 34: <b>Generalized Binary Search For Split-Neighborly Problems</b><br> Stephen
 Mussmann</p><p>Poster 35: <b>Scalable Hash-Based Estimation of Divergence Measures</b><br> Morteza
 Noshad Iranzad</p><p>Poster 36: <b>Semi-Supervised Prediction-Constrained Topic Models</b><br> Michael
 Hughes</p><p>Poster 37: <b>Crowdclustering with Partition Labels</b><br> Junxiang
 Chen</p><p>Poster 38: <b>Generalized Concomitant Multi-Task Lasso for sparse multimodal regression</b><br> Mathurin
 Massias</p><p>Poster 39: <b>Gradient Diversity: a Key Ingredient for Scalable Distributed Learning</b><br> Dong
 Yin</p><p>Poster 40: <b>Layerwise Systematic Scan: Deep Boltzmann Machines and Beyond</b><br> Heng
 Guo</p><p>Poster 41: <b>Multi-view Metric Learning in Vector-valued Kernel Spaces</b><br> Riikka
 Huusari</p><p>Poster 42: <b>Intersection-Validation: A Method for Evaluating Structure Learning without Ground Truth</b><br> Jussi
 Viinikka</p><p>Poster 43: <b>Variational Sequential Monte Carlo</b><br>Christian Naesseth, 
 Scott Linderman, Rajesh Ranganath</p><p>Poster 44: <b>VAE with a VampPrior</b><br>Jakub Tomczak,
 Max Welling</p><p>Poster 45: <b>Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes</b><br>Hyunjik Kim
 , Yee Whye Teh</p><p>Poster 46: <b>Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models</b><br>Ardavan Saeedi, Matthew Hoffman, Matthew Hoffman, Stephen DiVerdi, Asma Ghandeharioun, Matthew Johnson,
 Ryan Adams</p><p>Poster 47: <b>Random Warping Series: A Random Features Method for Time-Series Embedding</b><br>Lingfei Wu, Ian En-Hsu Yen,
 Jinfeng Yi, Fangli Xu, Qi Lei, Michael Witbrock</p><p>Poster 48: <b>Efficient and principled score estimation with Nyström kernel exponential families</b><br>Dougal Sutherland, Heiko Strathmann, 
 Michael Arbel, Arthur Gretton</p><p>Poster 49: <b>Multi-scale Nystrom Method</b><br>Woosang Lim, Rundong Du, Bo Dai,
 Kyomin Jung, Le Song</p><p>Poster 50: <b>Batch-Expansion Training: An Efficient Optimization Framework</b><br>Michal Derezinski, Dhruv Mahajan, Sathiya Keerthi, S. V. N. Vishwanathan,
 Markus Weimer</p><p>Poster 51: <b>Adaptive balancing of gradient and update computation times using global geometry and approximate subproblems</b><br>Sai Praneeth Reddy Karimireddy,
 Sebastian Stich, Martin Jaggi</p><p>Poster 52: <b>Frank-Wolfe Splitting via Augmented Lagrangian Method</b><br>Gauthier Gidel,
 Fabian Pedregosa, Simon Lacoste-Julien,</p><p>Poster 53: <b>Structured Optimal Transport</b><br>David Alvarez Melis, Tommi Jaakkola,
 Stefanie Jegelka</p><p>Poster 54: <b>Tracking the gradients using the Hessian: A new look at variance reducing stochastic methods</b><br>Robert Gower, Nicolas Le Roux,
 Francis Bach</p>
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