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AISTATS 2019

AISTATS 2018 Poster Sessions

Accepted papers

All accepted papers are available here.

Poster Format

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

Poster Session 1 (April 9)

Poster 1: Submodularity on Hypergraphs: From Sets to Sequences
Marko Mitrovic

Poster 2: Regional Multi-Armed Bandits
Cong Shen

Poster 3: On the challenges of learning with inference networks on sparse high-dimensional data
Rahul Krishnan

Poster 4: Integral Transforms from Finite Data: An Application of Gaussian Process Regression to Fourier Analysis
Luca Ambrogioni

Poster 5: Combinatorial Preconditioners for Proximal Algorithms on Graphs
Thomas Möllenhoff

Poster 6: Near-Optimal Machine Teaching via Explanatory Teaching Sets
Yuxin Chen

Poster 7: Factorized Recurrent Neural Architectures for Longer Range Dependence
Francois Belletti

Poster 8: Nonparametric Preference Completion
Julian Katz-Samuels

Poster 9: HONES: A Fast and Tuning-free Homotopy Method For Online Newton Step
Yuting Ye

Poster 10: Robustness of classifiers to uniform \ell_p and Gaussian noise
Alhussein Fawzi

Poster 11: Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in Distributed SGD
Sanghamitra Dutta

Poster 12: Comparison Based Learning from Weak Oracles
Ehsan Kazemi

Poster 13: Teacher Improves Learning by Selecting a Training Subset
Xiaojin Zhu

Poster 14: Probability–Revealing Samples
Krzysztof Onak

Poster 15: Topic Compositional Neural Language Model
Wenlin Wang

Poster 16: Reducing Crowdsourcing to Graphon Estimation Statistically
Christina Lee

Poster 17: Nonparametric Bayesian sparse graph linear dynamical systems
Mingyuan Zhou

Poster 18: Learning Structural Weight Uncertainty with Stein Gradient Flows
Chunyuan Li

Poster 19: The Binary Space Partitioning-Tree Process
Xuhui Fan

Poster 20: Robust Maximization of Non-Submodular Objectives
Ilija Bogunovic

Poster 21: FLAG n’ FLARE: Fast Linearly-Coupled Adaptive Gradient Methods
Fred Roosta

Poster 22: Cheap Checking for Cloud Computing: Statistical Analysis via Annotated Data Streams
Chris Hickey

Poster 23: Proximity Variational Inference
Jaan Altosaar

Poster 24: An Analysis of Categorical Distributional Reinforcement Learning
Mark Rowland

Poster 25: Parallel and Distributed MCMC via Shepherding Distributions
Arkabandhu Chowdhury

Poster 26: Inference in Sparse Graphs with Pairwise Measurements and Side Information
Dylan Foster

Poster 27: IHT dies hard: Provable accelerated Iterative Hard Thresholding
Anastasios Kyrillidis

Poster 28: High-dimensional Bayesian optimization via additive models with overlapping groups
Paul Rolland

Poster 29: Learning Hidden Quantum Markov Models
Siddarth Srinivasan

Poster 30: On denoising noisy modulo 1 samples of a function
Mihai Cucuringu

Poster 31: A Generic Approach for Escaping Saddle points
Manzil Zaheer

Poster 32: Nonlinear Weighted Finite Automata
Tianyu Li

Poster 33: Few-shot Generative Modelling with Generative Matching Networks
Sergey Bartunov

Poster 34: A Stochastic Differential Equation Framework for Guiding Online User Activities in Closed Loop
Yichen Wang

Poster 35: Personalized and Private Peer-to-Peer Machine Learning
Aurélien Bellet

Poster 36: Matrix-normal models for fMRI analysis
Michael Shvartsman

Poster 37: A Fast Algorithm for Separated Sparsity via Perturbed Lagrangians
Slobodan Mitrovic

Poster 38: One-shot Coresets: The Case of k-Clustering
Olivier Bachem

Poster 39: Iterative Supervised Principal Components
Juho Piironen

Poster 40: Cause-Effect Inference by Comparing Regression Errors
Patrick Bloebaum

Poster 41: Graphical Models for Non-Negative Data Using Generalized Score Matching
Shiqing Yu

Poster 42: Best arm identification in multi-armed bandits with delayed feedback
Aditya Grover

Poster 43: Approximate Bayesian Computation with Kullback-Leibler Divergence as Data Discrepancy
Bai Jiang

Poster 44: A Unified Dynamic Approach to Sparse Model Selection
Chendi Huang

Poster 45: On Statistical Optimality of Variational Bayes
Debdeep Pati

Poster 46: Stochastic algorithms for entropy-regularized optimal transport problems
Brahim Khalil Abid

Poster 47: Can clustering scale sublinearly with its clusters? A variational EM acceleration of GMMs and k-means
Dennis Forster

Poster 48: Learning Generative Models with Sinkhorn Divergences
Aude Genevay

Poster 49: Product Kernel Interpolation for Scalable Gaussian Processes
Jacob Gardner

Poster 50: Solving lp-norm regularization with tensor kernels
Saverio Salzo

Poster 51: Statistically Efficient Estimation for Non-Smooth Probability Densities
Masaaki Imaizumi, Takanori Maehara, Yuichi Yoshida

Poster 52: Stochastic Zeroth-order Optimization in High Dimensions
Yining Wang, Arindam Banerjee, Simon Du, Sivaraman Balakrishnan, Aarti Singh

Poster 53: Sparse Linear Isotonic Models
Sheng Chen, Arindam Banerjee

Poster 54: Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs
Lawrence Murray, Daniel Lundén, Jan Kudlicka, David Broman, Thomas Schön

Poster Session 2 (April 10)

Poster 1: Structured Factored Inference for Probabilistic Programming
Alison OConnor

Poster 2: Weighted Tensor Decomposition for Learning Latent Variables with Partial Data
Omer Gottesman

Poster 3: Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach
Satoshi Hara

Poster 4: Finding Global Optima in Nonconvex Stochastic Semidefinite Optimization with Variance Reduction
Jinshan ZENG

Poster 5: Plug-in Estimators for Conditional Expectations and Probabilities
Steffen Grunewalder

Poster 6: Policy Evaluation and Optimization with Continuous Treatments
Nathan Kallus

Poster 7: Tensor Regression Meets Gaussian Processes
Rose Yu

Poster 8: Robust Locally-Linear Controllable Embedding
Ershad Banijamali

Poster 9: Data-Efficient Reinforcement Learning with \\Probabilistic Model Predictive Control
Marc Deisenroth

Poster 10: Smooth and Sparse Optimal Transport
Mathieu Blondel

Poster 11: The Power Mean Laplacian for Multilayer Graph Clustering
Pedro Mercado

Poster 12: Gauged Mini-Bucket Elimination for Approximate Inference
Adrian Weller

Poster 13: Variational Inference based on Robust Divergences
Futoshi Futami

Poster 14: Benefits from Superposed Hawkes Processes
Hongteng Xu

Poster 15: Boosting Variational Inference: an Optimization Perspective
Francesco Locatello

Poster 16: Tree-based Bayesian Mixture Model for Competing Risks
Alexis Bellot

Poster 17: Quotient Normalized Maximum Likelihood Criterion for Learning Bayesian Network Structures
Tomi Silander

Poster 18: Conditional Gradient Method for Stochastic Submodular Maximization: Closing the Gap
Aryan Mokhtari

Poster 19: Medoids in Almost-Linear Time via Multi-Armed Bandits
David Tse

Poster 20: On the Truly Block Eigensolvers via First-Order Riemannian Optimization
Zhiqiang Xu

Poster 21: Efficient Weight Learning in High-Dimensional Untied MLNs
Khan Mohammad Al Farabi

Poster 22: Beating Monte Carlo Integration: a Nonasymptotic Study of Kernel Smoothing Methods
Stephan Clémençon

Poster 23: On how complexity effects the stability of a predictor
Joel Ratsaby

Poster 24: Contextual Bandits with Stochastic Experts
Rajat Sen

Poster 25: Online Learning with Non-Convex Losses and Non-Stationary Regret
Xiaobo Li

Poster 26: Non-parametric estimation of Jensen-Shannon Divergence in Generative Adversarial Network training
Mathieu Sinn

Poster 27: Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure
Beilun Wang

Poster 28: Zeroth-Order Online Alternating Direction Method of Multipliers: Convergence Analysis and Applications
Sijia Liu

Poster 29: Robust Vertex Enumeration for Convex Hulls in High Dimensions
Pranjal Awasthi

Poster 30: Turing: Composable inference for probabilistic programming
Hong Ge

Poster 31: Combinatorial Penalties: Which structures are preserved by convex relaxations?
Marwa El Halabi

Poster 32: Metrics for Deep Generative Models
Nutan Chen

Poster 33: Spectral Algorithms for Computing Fair Support Vector Machines
Mahbod Olfat

Poster 34: Optimal Submodular Extensions for Marginal Estimation
Pankaj Pansari

Poster 35: Iterative Spectral Method for Alternative Clustering
Chieh Wu

Poster 36: Differentially Private Regression with Gaussian Processes
Michael Smith

Poster 37: Reconstruction Risk of Convolutional Sparse Dictionary Learning
Shashank Singh

Poster 38: Learning to Round for Discrete Labeling Problems
Pritish Mohapatra

Poster 39: Direct Learning to Rank And Rerank
Cynthia Rudin

Poster 40: Linear Stochastic Approximation: Constant Step-Size and Iterate Averaging
Chandrashekar Lakshmi-Narayanan

Poster 41: Approximate ranking from pairwise comparisons
Reinhard Heckel

Poster 42: Stochastic Multi-armed Bandits in Constant Space
Ger Yang

Poster 43: Multi-objective Contextual Bandit Problem with Similarity Information
Cem Tekin

Poster 44: Scalable Generalized Dynamic Topic Models
Patrick Jähnichen

Poster 45: Growth-Optimal Portfolio Selection under CVaR Constraints
Guy Uziel

Poster 46: Statistical Sparse Online Regression: A Diffusion Approximation Perspective
Junchi Li

Poster 47: Combinatorial Semi-Bandits with Knapsacks
Karthik Abinav Sankararaman, Aleksandrs Slivkins

Poster 48: Online Continuous Submodular Maximization
Lin Chen, Hamed Hassani, Amin Karbasi

Poster 49: Convergence of Value Aggregation for Imitation Learning
Ching-An Cheng, Byron Boots

Poster 50: Competing with Automata-based Expert Sequences
Scott Yang, Mehryar Mohri

Poster 51: A Simple Analysis for Exp-concave Empirical Minimization with Arbitrary Convex Regularizer
Tianbao Yang, Zhe Li, Lijun Zhang

Poster 52: Learning linear structural equation models in polynomial time and sample complexity
Asish Ghoshal, Jean Honorio

Poster 53: Consistent Algorithms for Classification under Complex Losses and Constraints
Harikrishna Narasimhan

Poster 54: Subsampling for Ridge Regression via Regularized Volume Sampling
Michal Derezinski, Manfred Warmuth

Poster Session 3 (April 10)

Poster 1: Group invariance principles for causal generative models
Michel Besserve

Poster 2: Learning Priors for Invariance
Eric Nalisnick

Poster 3: Catalyst for Gradient-based Nonconvex Optimization
Courtney Paquette

Poster 4: Dropout as a Low-Rank Regularizer for Matrix Factorization
Jacopo Cavazza

Poster 5: Practical Bayesian optimization in the presence of outliers
Ruben Martinez-Cantin

Poster 6: Fast generalization error bound of deep learning from a kernel perspective
Taiji Suzuki

Poster 7: Asynchronous Doubly Stochastic Group Regularized Learning
Bin Gu

Poster 8: The emergence of spectral universality in deep networks
Jeffrey Pennington

Poster 9: Post Selection Inference with Kernels
Makoto Yamada

Poster 10: Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information
Jakob Runge

Poster 11: Nonparametric Sharpe Ratio Function Estimation in Heteroscedastic Regression Models via Convex Optimization
Joong-Ho Won

Poster 12: SDCA-Powered Inexact Dual Augmented Lagrangian Method for Fast CRF Learning
Xu Hu

Poster 13: Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models
Hugh Salimbeni

Poster 14: Semi-Supervised Learning with Competitive Infection Models
Nir Rosenfeld

Poster 15: Random Subspace with Trees for Feature Selection Under Memory Constraints
Antonio Sutera

Poster 16: Bayesian Structure Learning for Dynamic Brain Connectivity
Michael Andersen

Poster 17: Riemannian stochastic quasi-Newton algorithm with variance reduction and its convergence analysis
Hiroyuki Kasai

Poster 18: Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling
Hongyi Ding

Poster 19: Efficient Bandit Combinatorial Optimization Algorithm with Zero-suppressed Binary Decision Diagrams
Shinsaku Sakaue

Poster 20: Online Boosting Algorithms for Multi-label Ranking
Young Hun Jung

Poster 21: Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization
Fanhua Shang

Poster 22: Community Detection in Hypergraphs: Optimal Statistical Limit and Efficient Algorithms
Chung-Yi Lin

Poster 23: Matrix completability analysis via graph k-connectivity
Dehua Cheng

Poster 24: A Nonconvex Proximal Splitting Algorithm under Moreau-Yosida Regularization
Emanuel Laude

Poster 25: Reducing optimization to repeated classification
Tatsunori Hashimoto

Poster 26: Online Ensemble Multi-kernel Learning Adaptive to Non-stationary and Adversarial Environments
Tianyi Chen

Poster 27: Transfer Learning on fMRI Datasets
Hejia Zhang

Poster 28: Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data
William Herlands

Poster 29: Nearly second-order optimality of online joint detection and estimation via one-sample update schemes
Yang Cao

Poster 30: Outlier Detection and Robust Estimation in Nonparametric Regression
Weining Shen

Poster 31: Dimensionality Reduced $\ell^{0}$-Sparse Subspace Clustering
Yingzhen Yang

Poster 32: Sum-Product-Quotient Networks
Or Sharir

Poster 33: Nonlinear Structured Signal Estimation in High Dimensions via Iterative Hard Thresholding
Zhuoran Yang

Poster 34: Efficient Bayesian Methods for Counting Processes in Partially Observable Environments
Ferdian Jovan

Poster 35: Stochastic Three-Composite Convex Minimization with a Linear Operator
Renbo Zhao

Poster 36: Gradient Layer: Enhancing the Convergence of Adversarial Training for Generative Models
Atsushi Nitanda

Poster 37: Bootstrapping EM via Power EM and Convergence in the Naive Bayes Model
Christos Tzamos

Poster 38: Kernel Conditional Exponential Family
Michael Arbel

Poster 39: Achieving the time of 1-NN but the accuracy of k-NN
Lirong Xue

Poster 40: Bayesian Approaches to Distribution Regression
Ho Chung Leon Law

Poster 41: Nested CRP with Hawkes-Gaussian Processes
Xi Tan

Poster 42: Mixed Membership Word Embeddings for Computational Social Science
James Foulds

Poster 43: Learning Determinantal Point Processes in Sublinear Time
Christophe Dupuy

Poster 44: Fully adaptive algorithm for pure exploration in linear bandits
Liyuan Xu

Poster 45: Variational inference for the multi-armed contextual bandit
Iñigo Urteaga

Poster 46: A Provable Algorithm for Learning Interpretable Scoring Systems
Nataliya Sokolovska

Poster 47: An Optimization Approach to Learning Falling Rule Lists
Chaofan Chen

Poster 48: Fast Threshold Tests for Detecting Discrimination
Emma Pierson, Sam Corbett-Davies, Sharad Goel

Poster 49: Parallelised Bayesian Optimisation via Thompson Sampling
Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, Barnabas Poczos

Poster 50: Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition
Pavel Izmailov, Dmitry Kropotov, Alexander Novikov

Poster 51: Factorial HMM with Collapsed Gibbs Sampling for optimizing long-term HIV Therapy
Amit Gruber, Chen Yanover, Tal El-Hay, Yaara Goldschmidt, Anders Sönnerborg, Vanni Borghi, Francesca Incardona

Poster 52: Sketching for Kronecker Product Regression and P-splines
Huaian Diao, Zhao Song, Wen Sun, David Woodruff

Poster 53: Towards Provable Learning of Polynomial Neural Networks Using Low-Rank Matrix Estimation
Mohammadreza Soltani, Chinmay Hegde

Poster 54: Convergence diagnostics for stochastic gradient descent
Jerry Chee, Panos Toulis

Poster 55: Minimax-Optimal Privacy-Preserving Sparse PCA in Distributed Systems
Jason Ge

Poster Session 4 (April 11)

Poster 1: Optimal Cooperative Inference
Scott Cheng-Hsin Yang

Poster 2: Human Interaction with Recommendation Systems
Sven Schmit

Poster 3: Convex optimization over intersection of simple sets: improved convergence rate guarantees via exact penalty approach
Achintya Kundu

Poster 4: Towards Memory-Friendly Deterministic Incremental Gradient Method
Jiahao Xie

Poster 5: Independently Interpretable Lasso: A New Regularizer for Sparse Regression with Uncorrelated Variables
Masaaki Takada

Poster 6: AdaGeo: Adaptive Geometric Learning for Optimization and Sampling
Gabriele Abbati

Poster 7: Large Scale Empirical Risk Minimization via Truncated Adaptive Newton Method
Mark Eisen

Poster 8: Labeled Graph Clustering via Projected Gradient Descent
Shiau Hong Lim

Poster 9: Discriminative Learning of Prediction Intervals
Nir Rosenfeld

Poster 10: Accelerated Stochastic Power Iteration
Peng Xu

Poster 11: A Bayesian Nonparametric Method for Clustering Imputation and Forecasting in Multivariate Time Series
FERAS SAAD

Poster 12: Bayesian Multi-label Learning with Sparse Features and Labels
He Zhao

Poster 13: Robust Active Label Correction
Christian Igel

Poster 14: Factor Analysis on a Graph
Masayuki Karasuyama

Poster 15: Reparameterizing the Birkhoff Polytope for Variational Permutation Inference
Gonzalo Mena

Poster 16: Provable Estimation of the Number of Blocks in Block Models
BOWEI YAN

Poster 17: Batched Large-scale Bayesian Optimization in High-dimensional Spaces
Zi Wang

Poster 18: Actor-Critic Fictitious Play in Simultaneous Move Multistage Games
Julien Perolat

Poster 19: Online Regression with Partial Information: Generalization and Linear Projection
Shinji Ito

Poster 20: Alpha-expansion is Exact on Stable Instances
Hunter Lang

Poster 21: Adaptive Sampling for Clustered Ranking
Sumeet Katariya

Poster 22: Multiphase MCMC Sampling for Parameter Inference in Nonlinear Ordinary Differential Equations
Alan Lazarus

Poster 23: This poster has been moved

Poster 24: The Geometry of Random Features
Adrian Weller

Poster 25: Symmetric Variational Autoencoder and Connections to Adversarial Learning
Liqun Chen

Poster 26: Learning Sparse Polymatrix Games in Polynomial Time and Sample Complexity
Asish Ghoshal

Poster 27: A Unified Framework for Nonconvex Low-Rank plus Sparse Matrix Recovery
Xiao Zhang

Poster 28: Communication-Avoiding Optimization Methods for Distributed Massive-Scale Sparse Inverse Covariance Estimation
Penporn Koanantakool

Poster 29: Accelerated Stochastic Mirror Descent: From Continuous-time Dynamics to Discrete-time Algorithms
Pan Xu

Poster 30: On the Statistical Efficiency of Compositional Nonparametric Prediction
Yixi Xu

Poster 31: Exploiting Strategy-Space Diversity for Batch Bayesian Optimization
Sunil Gupta

Poster 32: Variational Rejection Sampling
Aditya Grover

Poster 33: Why adaptively collected data have negative bias and how to correct for it.
Xinkun Nie

Poster 34: Generalized Binary Search For Split-Neighborly Problems
Stephen Mussmann

Poster 35: Scalable Hash-Based Estimation of Divergence Measures
Morteza Noshad Iranzad

Poster 36: Semi-Supervised Prediction-Constrained Topic Models
Michael Hughes

Poster 37: Crowdclustering with Partition Labels
Junxiang Chen

Poster 38: Generalized Concomitant Multi-Task Lasso for sparse multimodal regression
Mathurin Massias

Poster 39: Gradient Diversity: a Key Ingredient for Scalable Distributed Learning
Dong Yin

Poster 40: Layerwise Systematic Scan: Deep Boltzmann Machines and Beyond
Heng Guo

Poster 41: Multi-view Metric Learning in Vector-valued Kernel Spaces
Riikka Huusari

Poster 42: Intersection-Validation: A Method for Evaluating Structure Learning without Ground Truth
Jussi Viinikka

Poster 43: Variational Sequential Monte Carlo
Christian Naesseth, Scott Linderman, Rajesh Ranganath

Poster 44: VAE with a VampPrior
Jakub Tomczak, Max Welling

Poster 45: Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes
Hyunjik Kim , Yee Whye Teh

Poster 46: Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models
Ardavan Saeedi, Matthew Hoffman, Matthew Hoffman, Stephen DiVerdi, Asma Ghandeharioun, Matthew Johnson, Ryan Adams

Poster 47: Random Warping Series: A Random Features Method for Time-Series Embedding
Lingfei Wu, Ian En-Hsu Yen, Jinfeng Yi, Fangli Xu, Qi Lei, Michael Witbrock

Poster 48: Efficient and principled score estimation with Nyström kernel exponential families
Dougal Sutherland, Heiko Strathmann, Michael Arbel, Arthur Gretton

Poster 49: Multi-scale Nystrom Method
Woosang Lim, Rundong Du, Bo Dai, Kyomin Jung, Le Song

Poster 50: Batch-Expansion Training: An Efficient Optimization Framework
Michal Derezinski, Dhruv Mahajan, Sathiya Keerthi, S. V. N. Vishwanathan, Markus Weimer

Poster 51: Adaptive balancing of gradient and update computation times using global geometry and approximate subproblems
Sai Praneeth Reddy Karimireddy, Sebastian Stich, Martin Jaggi

Poster 52: Frank-Wolfe Splitting via Augmented Lagrangian Method
Gauthier Gidel, Fabian Pedregosa, Simon Lacoste-Julien,

Poster 53: Structured Optimal Transport
David Alvarez Melis, Tommi Jaakkola, Stefanie Jegelka

Poster 54: Tracking the gradients using the Hessian: A new look at variance reducing stochastic methods
Robert Gower, Nicolas Le Roux, Francis Bach