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AISTATS 2016 Poster Sessions

Accepted papers

All accepted papers are available here.

Poster Format

The poster board is 0.98m (wide) x 2.54m (high). We recommend A0 portrait as the poster size.

Poster Session 1 (May 9)

Poster 1: Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures
Mario Lucic, Olivier Bachem, Andreas Krause

Poster 2: Revealing Graph Bandits for Maximizing Local Influence
Alexandra Carpentier, Michal Valko

Poster 3: Convex block-sparse linear regression with expanders, provably
Anastasios Kyrillidis, Bubacarr Bah, Rouzbeh Hasheminezhad, Quoc Tran Dinh, Luca Baldassarre, Volkan Cevher

Poster 4: Clamping Improves TRW and Mean Field Approximations
Adrian Weller, Justin Domke

Poster 5: Control Functionals for Quasi-Monte Carlo Integration
Chris Oates, Mark Girolami

Poster 6: Sparse Representation of Multivariate Extremes with Applications to Anomaly Ranking
Nicolas Goix, Anne Sabourin, Stéphan Clémençon

Poster 7: A Robust-Equitable Copula Dependence Measure for Feature Selection
Yale Chang, Yi Li, Adam Ding, Jennifer Dy

Poster 8: Random Forest for the Contextual Bandit Problem
Raphaël Féraud, Robin Allesiardo, Tanguy Urvoy, Fabrice Clérot

Poster 9: Learning Sparse Additive Models with Interactions in High Dimensions
Hemant Tyagi, Anastasios Kyrillidis, Bernd Gärtner, Andreas Krause

Poster 10: Bipartite Correlation Clustering - Maximizing Agreements
Megasthenis Asteris, Anastasios Kyrillidis, Dimitris Papailiopoulos, Alexandros Dimakis

Poster 11: Breaking Sticks and Ambiguities with Adaptive Skip-gram
Sergey Bartunov, Dmitry Kondrashkin, Anton Osokin, Dmitry Vetrov

Poster 12: Top Arm Identification in Multi-Armed Bandits with Batch Arm Pulls
Kwang-Sung Jun, Kevin Jamieson, Robert Nowak, Xiaojin Zhu

Poster 13: Limits on Sparse Support Recovery via Linear Sketching with Random Expander Matrices
Jonathan Scarlett, Volkan Cevher

Poster 14: Maximum Likelihood for Variance Estimation in High-Dimensional Linear Models
Lee Dicker, Murat Erdogdu

Poster 15: Scalable Gaussian Process Classification via Expectation Propagation
Daniel Hernandez-Lobato, Jose Miguel Hernandez-Lobato

Poster 16: Precision Matrix Estimation in High Dimensional Gaussian Graphical Models with Faster Rates
Lingxiao Wang, Quanquan Gu

Poster 17: On the Reducibility of Submodular Functions
Jincheng Mei, Hao Zhang, Bao-Liang Lu

Poster 18: Accelerated Stochastic Gradient Descent for Minimizing Finite Sums
Atsushi Nitanda

Poster 19: Fast Convergence of Online Pairwise Learning Algorithms
Martin Boissier, Siwei Lyu, Yiming Ying, Ding-Xuan Zhou

Poster 20: Computationally Efficient Bayesian Learning of Gaussian Process State Space Models
Andreas Svensson, Arno Solin, Simo Särkkä, Thomas Schön

Poster 21: Generalized Ideal Parent (GIP): Discovering non-Gaussian Hidden Variables
Yaniv Tenzer, Gal Elidan

Poster 22: On Sparse Variational Methods and the Kullback-Leibler Divergence between Stochastic Processes
Alex Matthews, James Hensman, Richard Turner, Zoubin Ghahramani

Poster 23: Non-stochastic Best Arm Identification and Hyperparameter Optimization
Kevin Jamieson Ameet Tawalkar

Poster 24: A Linearly-Convergent Stochastic L-BFGS Algorithm
Philipp Moritz, Robert Nishihara, Michael Jordan

Poster 25: No Regret Bound for Extreme Bandits
Robert Nishihara, David Lopez-Paz, Leon Bottou

Poster 26: Online Learning to Rank with Feedback at the Top
Sougata Chaudhuri, Ambuj Tewari

Poster 27: Score Permutation Based Finite Sample Inference for Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) Models
Balazs Csaji

Poster 28: CRAFT: ClusteR-specific Assorted Feature selecTion
Vikas Garg, Cynthia Rudin, Tommi Jaakkola

Poster 29: Time-Varying Gaussian Process Bandit Optimization
Ilija Bogunovic, Jonathan Scarlett, Volkan Cevher

Poster 30: Bayes-Optimal Effort Allocation in Crowdsourcing: Bounds and Index Policie
Weici Hu, Peter Frazier

Poster 31: Bayesian Markov Blanket Estimation
Dinu Kaufmann, Sonali Parbhoo, Aleksander Wieczorek, Sebastian Keller, David Adametz, Volker Roth

Poster 32: Unsupervised Ensemble Learning with Dependent Classifiers
Ethan Fetaya, Boaz Nadler, Ariel Jaffe, Ting Ting Jiang, Yuval Kluger

Poster 33: Multi-Level Cause-Effect Systems
Krzysztof Chalupka, Frederick Eberhardt, Pietro Perona

Poster 34: Deep Kernel Learning
Andrew Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric Xing

Poster 35: Latent Point Process Allocation
Chris Lloyd, Tom Gunter, Michael Osborne, Stephen Roberts, Tom Nickson

Poster 36: Bayesian generalised ensemble Markov chain Monte Carlo
Jes Frellsen, Ole Winther, Zoubin Ghahramani, Jesper Ferkinghoff-Borg

Poster 37: A Lasso-based Sparse Knowledge Gradient Policy for Sequential Optimal Learning
Yan Li, Han Liu, Warren Powell

Poster 38: Optimal Statistical and Computational Rates for One Bit Matrix Completion
Quanquan Gu, Renkun Ni

Poster 39: PAC-Bayesian Bounds based on the Rényi Divergence
Luc Bégin, Pascal Germain, François Laviolette, Jean-Francis Roy

Poster 40: Simple and Scalable Constrained Clustering: A Generalized Spectral Method
Mihai Cucuringu, Ioannis Koutis, Gary Miller, Richard Peng, Sanjay Chawla

Poster 41: Geometry Aware Mappings for High Dimensional Sparse Factors
Avradeep Bhowmik, Nathan Liu, Erheng Zhong, Badri Bhaskar, Suju Rajan

Poster 42: Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree
Chen-Yu Lee, Patrick Gallagher, Zhuowen Tu

Poster 43: Rivalry of Two Families of Algorithms for Memory-Restricted Streaming PCA
Chun-Liang Li, Hsuan-Tien Lin, Chi-Jen Lu

Poster 44: Quantization based Fast Inner Product Search
Ruiqi Guo, Sanjiv Kumar, Krzysztof Choromanski, David Simcha

Poster 45: An Improved Convergence Analysis of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization
Tuo Zhao, Xingguo Li, Raman Arora, Han Liu, Mingyi Hong

Poster 46: Learning Structured Low-Rank Representation via Matrix Factorization
Jie Shen,Ping Li

Poster 47: A PAC RL Algorithm for Episodic POMDPs
Zhaohan Guo, Shayan Doroudi, Emma Brunskill

Poster 48: Large-Scale Optimization Algorithms for Sparse Conditional Gaussian Graphical Models
Calvin McCarter, Seyoung Kim

Poster 49: Graph Connectivity in Noisy Sparse Subspace Clustering
Yining Wang, Yu-Xiang Wang, Aarti Singh

Poster 50: The Nonparametric Kernel Bayes Smoother
Yu Nishiyama, Amir Afsharinejad, Shunsuke Naruse, Byron Boots, Le Song

Poster 51: Universal Models of Multivariate Temporal Point Processes
Asela Gunawardana, Chris Meek

Poster 52: Nonparametric Budgeted Stochastic Gradient Descent
Trung Le, Vu Nguyen, Dinh Phung

Poster 53: Online Relative Entropy Policy Search using Reproducing Kernel Hilbert Space Embeddings
Zhitang Chen, Pascal Poupart, Yanhui Geng

Poster 54: Relationship between PreTraining and Maximum Likelihood Estimation in Deep Boltzmann Machines
Muneki Yasuda

Poster 55: Enumerating Equivalence Classes of Bayesian Networks using CPDAG Graphs
Eunice Chen, Arthur Choi, Adnan Darwiche

Poster 56: NuC-MKL: A Convex Approach to Non Linear Multiple Kernel Learning
Eli Meirom, Pavel Kisilev

Poster 57: Improper Deep Kernels
Uri Heinemann, Roi Livni, Elad Eban, Gal Elidan, Amir Globerson

Poster 58: Randomization and The Pernicious Effects of Limited Budgets on Auction Experiments
Guillaume Basse, Hossein Azari, Diane Lambert

Poster 59: Mondrian Forests for Large-Scale Regression when Uncertainty Matters
Balaji Lakshminarayanan, Dan Roy, Yee Whye Teh

Poster 60: Provable Tensor Methods for Learning Mixtures of Generalized Linear Models
Hanie Sedghi, Majid Janzamin, Anima Anandkumar

Poster Session 2 (May 10)

Poster 1: C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching
Daniel Ritchie, Andreas Stuhlmüller, Noah Goodman

Poster 2: Tightness of LP Relaxations for Almost Balanced Models
Adrian Weller, David Sontag

Poster 3: Inverse Reinforcement Learning with Simultaneous Estimation of Rewards and Dynamics
Michael Herman, Tobias Gindele, Jörg Wagner, Felix Schmitt, Wolfram Burgard

Poster 4: Survey Propagation beyond Constraint Satisfaction Problems
Christopher Srinivasa, Siamak Ravanbakhsh, Brendan Frey

Poster 5: Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation
Søren Hauberg, Oren Freifeld, Anders Boesen Lindbo Larsen, John Fisher, Lars Hansen

Poster 6: K2-ABC: Approximate Bayesian Computation with Kernel Embeddings
Mijung Park, Wittawat Jitkrittum, Dino Sejdinovic

Poster 7: Fast Dictionary Learning with a Smoothed Wasserstein Loss
Antoine Rolet, Marco Cuturi, Gabriel Peyré

Poster 8: New Resistance Distances with Global Information on Large Graphs
Canh Hao Nguyen, Hiroshi Mamitsuka

Poster 9: Batch Bayesian Optimization via Local Penalization
Javier Gonzalez, Zhenwen Dai, Philipp Hennig, Neil Lawrence

Poster 10: Learning relationships between data obtained independently
Alexandra Carpentier, Teresa Schlueter

Poster 11: Fast and Scalable Structural SVM with Slack Rescaling
Heejin Choi, Ofer Meshi, Nathan Srebro

Poster 12: Probabilistic Approximate Least-Squares
Simon Bartels, Philipp Hennig

Poster 13: Approximate Inference Using DC Programming For Collective Graphical Models
Thien Nguyen, Akshat Kumar, Hoong Chuin Lau, Daniel Sheldon

Poster 14: Sequential Inference for Deep Gaussian Process
Yali Wang, Marcus Brubaker, Brahim Chaib-draa, Raquel Urtasun

Poster 15: Variational Tempering
Stephan Mandt, James McInerney, Farhan Abrol, Rajesh Ranganath, David Blei

Poster 16: On Convergence of Model Parallel Proximal Gradient Algorithm for Stale Synchronous Parallel System
Yi Zhou, Yaoliang Yu, Wei Dai, Yingbin Liang, Eric Xing

Poster 17: Scalable MCMC for Mixed Membership Stochastic Blockmodels
Wenzhe Li, Max Welling, Sungjin Ahn

Poster 18: Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo
Markus Heinonen, Henrik Mannerström, Juho Rousu, Samuel Kaski, Harri Lähdesmäki

Poster 19: A Deep Generative Deconvolutional Image Model
Yunchen Pu, Xin Yuan, Andrew Stevens, Chunyuan Li, Lawrence Carin

Poster 20: Distributed Multi-Task Learning
Jialei Wang, Mladen Kolar, Nathan Srebro

Poster 21: A Fixed-Point Operator for Inference in Variational Bayesian Latent Gaussian Models
Rishit Sheth, Roni Khardon

Poster 22: Learning Probabilistic Submodular Diversity Models Via Noise Contrastive Estimation
Sebastian Tschiatschek, Josip Djolonga, Andreas Krause

Poster 23: Fast Saddle-Point Algorithm for Generalized Dantzig Selector and FDR Control with the Ordered $\ell_1$-Norm
Sangkyun Lee, Damian Brzyski, Malgorzata Bogdan

Poster 24: GLASSES: Relieving The Myopia Of Bayesian Optimisation
Javier Gonzalez, Michael Osborne, Neil Lawrence

Poster 25: Stochastic Variational Inference for the HDP-HMM
Aonan Zhang, San Gultekin, John Paisley

Poster 26: Stochastic Neural Networks with Monotonic Activation Functions
Siamak Ravanbakhsh, Barnabas Poczos, Jeff Schneider, Dale Schuurmans, Russell Greiner

Poster 27: (Bandit) Convex Optimization with Biased Noisy Gradient Oracles
Xiaowei Hu, Prashanth L.A., András György, Csaba Szepesvari

Poster 28: Variational Gaussian Copula Inference
Shaobo Han, Xuejun Liao, David Dunson, Lawrence Carin,

Poster 29: Low-Rank Approximation of Weighted Tree Automata
Guillaume Rabusseau, Borja Balle, Shay Cohen

Poster 30: Accelerating Optimization via Adaptive Prediction
Scott Yang, Mehryar Mohri

Poster 31: Model-based Co-clustering for High Dimensional Sparse Data
Aghiles Salah, Nicoleta Rogovschi, Mohamed Nadif

Poster 32: DUAL-LOCO: Distributing Statistical Estimation Using Random Projections
Christina Heinze, Brian McWilliams, Nicolai Meinshausen

Poster 33: High Dimensional Bayesian Optimization via Restricted Projection Pursuit Models
Chun-Liang Li, Barnabas Poczos, Jeff Schneider, Kirthevasan Kandasamy

Poster 34: On the Use of Non-Stationary Strategies for Solving Two-Player Zero-Sum Markov Games
Julien Perolat, Bilal Piot, Bruno Scherrer, Olivier Pietquin

Poster 35: Manifold Learning with Adaptive Spectral Transform
Hanxiao Liu, Yiming Yang

Poster 36: Pseudo-Marginal Slice Sampling
Iain Murray, Matthew Graham

Poster 37: How to learn a graph from smooth signals
Vassilis Kalofolias

Poster 38: Pareto Front Identification from Stochastic Bandit Feedback
Peter Auer, Chao-Kai Chiang, Ronald Ortner, Madalina Drugan

Poster 39: Sketching, embedding and dimensionality reduction in information theoretic spaces
Amir Ali Abdullah, Suresh Venkatasubramanian, Ravi Kumar, Sergei Vassilvitskii, Andrew McGregor

Poster 40: AdaDelay: Delay Adaptive Distributed Stochastic Optimization
Suvrit Sra, Adams Wei Yu, Mu Li, Alex Smola

Poster 41: Exponential Stochastic Cellular Automata for Massively Parallel Inference
Manzil Zaheer, Michael Wick, Jean-Baptiste Tristan, Alex Smola, Guy Steele

Poster 42: Globally Sparse Probabilistic PCA
Pierre-Alexandre Mattei, Charles Bouveyron, Pierre Latouche

(Best paper award)
Poster 43: Provable Bayesian Inference via Particle Mirror Descent
Bo Dai, Niao He, Hanjun Dai, Le Song

Poster 44: Unsupervised Feature Selection by Preserving Stochastic Neighbors
Xiaokai Wei, Philip S. Yu

Poster 45: Improved Learning Complexity in Combinatorial Pure Exploration Bandits
Victor Gabillon, Alessandro Lazaric, Mohammad Ghavamzadeh, Ronald Ortner, Peter Bartlett

Poster 46: Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces
William Herlands, Andrew Wilson, Seth Flaxman, Daniel Neill, Wilbert Van Panhuis, Eric Xing, Hannes Nickisch

Poster 47: Optimization as Estimation with Gaussian Processes in Bandit Settings
Zi Wang, Bolei Zhou, Stefanie Jegelka

Poster 48: Inference for High-dimensional Exponential Family Graphical Models
Jialei Wang, Mladen Kolar

Poster 49: Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization
Changyou Chen, David Carlson, Zhe Gan, Chunyuan Li, Lawrence Carin

Poster 50: Fitting Spectral Decay with the $k$-Support Norm
Andrew McDonald, Massimiliano Pontil, Dimitris Stamos

Poster 51: Early Stopping as Nonparametric Variational Inference
David Duvenaud, Dougal Maclaurin, Ryan Adams

Poster 52: Bayesian Nonparametric Kernel-Learning
Junier B. Oliva, Avinava Dubey, Andrew Wilson, Barnabas Poczos, Jeff Schneider, Eric Xing

Poster 53: Tight Variational Bounds via Random Projections and I-Projections
Lun-Kai Hsu, Tudor Achim, Stefano Ermon

Poster 54: Bethe Learning of Graphical Models via MAP Decoding
Kui Tang, Nicholas Ruozzi, David Belanger, Tony Jebara

Poster 55: DREVS: Determinantal Regularization for Ensemble Variable Selection
Veronika Rockova, Gemma Moran, Edward George

Poster 56: Scalable and Sound Low-Rank Tensor Learning
Hao Cheng, Yaoliang Yu, Xinhua Zhang, Eric Xing, Dale Schuurmans

Poster 57: Efficient Non-negative Matrix Factorization for Discrete Data with Structural Side-Information
Changwei Hu, Piyush Rai, Lawrence Carin

Poster 58: Scalable Bilinear Non-negative Latent Factor Models for Multi-Relational Data
Changwei Hu, Piyush Rai, Lawrence Carin

Poster 59: Consistently Estimating Markov Chains with Noisy Aggregate Data
Garrett Bernstein, Daniel Sheldon

Poster 60: Unwrapping ADMM: Efficient Distributed Computing via Transpose Reduction
Gavin Taylor, Tom Goldstein

Poster 61: Unbounded Bayesian Optimization via Regularization
Bobak Shahriari, Alexandre Bouchard-Cote, Nando de Freitas

Poster 62: Non-Gaussian Component Analysis with Log-Density Gradient Estimation
Hiroaki Sasaki, Gang Niu, Masashi Sugiyama

Poster 63: Parallel Markov Chain Monte Carlo via Spectral Clustering
Guillaume Basse, Aaron Smith, Natesh Pillai

Poster Session 3 (May 11)

In addition to AISTATS 2016 posters, MLSS posters will be displayed in this session.

Poster 1: Probability Inequalities for Kernel Embeddings in Sampling without Replacement
Markus Schneider

Poster 2: Tensor vs Matrix Methods: Robust Tensor Decomposition under Block Sparse Perturbations
Anima Anandkumar, Prateek Jain, Yang Shi, Niranjan Uma Naresh

Poster 3: Nearly optimal classification for semimetrics
Lee-Ad Gottlieb, Aryeh Kontorovich, Pinhas Nisnevitch

Poster 4: Large-Scale Semi-Supervised Learning Using Streaming Approximation
Sujith Ravi, Qiming Diao

Poster 5: Low-Rank and Sparse Structure Pursuit via Alternating Minimization
Quanquan Gu, Zhaoran Wang, Han Liu

Poster 6: Tractable and Scalable Schatten Quasi-Norm Approximations for Rank Minimization
Fanhua Shang, Yuanyuan Liu, James Cheng

(Best paper award)
Poster 7: Scalable geometric density estimation
Ye Wang, Antonio Canale, David Dunson

Poster 8: Ordered Weighted l1 Regularized Regression with Strongly Correlated Covariates: Theoretical Aspects
Mario Figueiredo, Robert Nowak

Poster 9: A Convex Surrogate Operator for General Non-Modular Loss Functions
Jiaqian Yu, Matthew Blaschko

Poster 10: Online learning with noisy side observations
Tomáš Kocák, Gergely Neu, Michal Valko

Poster 11: Black-Box Policy Search with Probabilistic Programs
Jan-Willem Vandemeent, Brooks Paige, David Tolpin, Frank Wood

Poster 12: Efficient Bregman Projections onto the Generalized Permutahedron
Cong Han Lim, Stephen Wright

Poster 13: Searching for Generalized Instrumental Variables
Benito Van der Zander, Maciej Liśkiewicz

Poster 14: Controlling Bias in Adaptive Data Analysis Using Information Theory
Daniel Russo, James Zou

Poster 15: A Column Generation Bound Minimization Approach with PAC-Bayesian Generalization Guarantees
François Laviolette, Mario Marchand, Jean-Francis Roy

Poster 16: Graph Sparsification Approaches for Laplacian Smoothing Problems
Veeru Sadhanala, Yu-Xiang Wang, Ryan Tibshirani, Alex Smola

Poster 17: Scalable Exemplar Clustering and Facility Location via Augmented Block Coordinate Descent with Column Generation
Ian En-Hsu Yen, Dmitry Malioutov, Abhishek Kumar

Poster 18: Robust Covariate Shift Regression
Xiangli Chen, Brian Ziebart, Mathew Monfort, Anqi Liu

Poster 19: On Lloyd's algorithm: new theoretical insights for clustering in practice
Cheng Tang, Claire Monteleoni

Poster 20: Towards stability and optimality in stochastic gradient descent
Panos Toulis, Dustin Tran, Edo Airoldi

Poster 21: Communication Efficient Distributed Agnostic Boosting
Shang-Tse Chen, Maria-Florina Balcan, Duen Horng Chau

Poster 22: Differentially Private Causal Inference
Matt Kusner, Yu Sun, Karthik Sridharan, Kilian Weinberger

Poster 23: Efficient Sampling for k-Determinantal Point Processes
Chengtao Li, Stefanie Jegelka,Suvrit Sra

Poster 24: A Fast and Reliable Policy Improvement Algorithm
Yasin Abbasi-Yadkori, Peter Bartlett, Stephen Wright

Poster 25: Learning Sigmoid Belief Networks via Monte Carlo Expectation Maximization
Zhao Song, Ricardo Henao, David Carlson, Lawrence Carin

Poster 26: Active Learning Algorithms for Graphical Model Selection
Gautamd Dasarathy, Aarti Singh, Maria-Florina Balcan, Jong Park

Poster 27: Streaming Kernel Principal Component Analysis
Jeff Phillips, Mina Ghashami, Daniel Perry

Poster 28: Back to the future: Radial Basis Function networks revisited
Qichao Que, Mikhail Belkin

Poster 29: Cut Pursuit: fast algorithms to learn piecewise constant functions
Loic Landrieu, Guillaume Obozinski

Poster 30: Loss Bounds and Time Complexity for Speed Priors
Daniel Filan, Jan Leike, Marcus Hutter

Poster 31: NYTRO: When Subsampling Meets Early Stopping
Raffaello Camoriano, Lorenzo Rosasco, Alessandro Rudi, Tomás M. Angles L.

Poster 32: Spectral M-estimation
Dustin Tran, Minjae Kim, Finale Doshi-Velez

Poster 33: Chained Gaussian Processes
Alan Saul, James Hensman, Aki Vehtari, Neil Lawrence

(Best paper award)
Poster 34: Multiresolution Matrix Compression
Nedelina Teneva, Pramod Kaushik Mudrakarta, Risi Kondor

Poster 35: Supervised neighborhoods for distributed nonparametric regression
Adam Bloniarz, Ameet Tawalkar, Bin Yu, Christopher Wu

Poster 36: Global Convergence of a Grassmannian Gradient Descent Algorithm for Subspace Estimation
Laura Balzano, Dejiao Zhang

Poster 37: Online and Distributed Bayesian Moment Matching for SPNs
Abdullah Rashwan, Pascal Poupart, Han Zhao

Poster 38: Online (and Offline) Robust PCA: Novel Algorithms and Correctness Results
Jinchun Zhan, Brian Lois, Han Guo, Namrata Vaswani

Poster 39: Parallel Majorization Minimization with Dynamically Restricted Domains for Nonconvex Optimization
Yan Kaganovsky, Ikenna Odinaka, David Carlson, Lawrence Carin

Poster 40: Discriminative Structure Learning of Arithmetic Circuits
Amirmohammad Rooshenas, Daniel Lowd

Poster 41: One Scan 1-Bit Compressed Sensing
Ping Li

MLSS Posters in Session 3 (May 11)

Poster 42: Neural Networks in Sensors
Gilles Backhus

Poster 43: Accurate high-dimensional discrete inference with EP approximations. Applications in digital communications.
Pablo Martinez Olmos

Poster 44: Early identification of patients at risk of drop-out during a 4-week inpatient psychiatric rehabilitation program
Massimiliano Grassi

Poster 45: Active Exploration and Learning of Skill Hierarchies
Sébastien Forestier

Poster 46: Online Active Learning for Linear Regression
Carlos Riquelme

Poster 47: Non-monotone Quadratic Potential Games
Javier Zazo Ruiz

Poster 48: Automated music composition – Bridging time scales in neural networks
Florian Colombo

Poster 49: Semi-Supervised Hidden Markov Jump Processes for Human Activity Recognition
Alfredo Nazábal

Poster 50: Semantic Parsing through Seq2seq Prediction of Canonical Forms
Chunyang Xiao

Poster 51: Effect of running on spatial integration in different classes of neurons of mouse visual cortex
Mario Dipoppa

Poster 52: HIV Therapy Selection with infinite POMDPs
Sonali Parbhoo

Poster 53: Modeling the human-algorithm interaction in recommendation systems
Sven Schmit

Poster 54: SGD-Trust: Stabilizing Stochastic Gradients
Arturo Fernandez

Poster 55: Extreme Bandits with Graph Side Information
Andrea Locatelli

Poster 56: Bandits with Knapsacks for Interactive Education Software
Ciara Pike-Burke

Poster 57: Predicting Heart Failure Deterioration using Physical Activity Recordings
Johanna Ernst

Poster 58: Electronic deep neural networks for ultra-efficient data processing
Jonathan Binas

Poster 59: Deep learning methods for semantic parsing on WikiData
Daniil Sorokin

Poster 60: Simplifying Regularizing and Strenghtening Sum-Product Networks Structure Learning
Antonio Vergari

Poster 61: Artificial neuron meets real neuron: pattern selectivity in V4
Reza Abbasi Asl

Poster 62: Efficient Bayesian regression with the Laplacian kernel using the Mondrian process
Matej Balog

Poster 63: Acceleration of convolutional neural networks
Aizhan Ibraimova

Poster 64: Neural Network Interpolators for the Large Scale Structure of the Universe
Joseph Faulkner

Poster 65: Automated quantitative analyses of collectively migrating malaria parasites
Sabrina Rossberger

Poster 66: Using cloud computing to study the Extended Spring Indices.
Emma Izquierdo-Verdiguier

Poster 67: 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
Katherine Baquero

Poster 68: Hidden semi-Markov Models comparison with GMM and HMM and applications to audio processing
Lilian Besson

Poster 69: Modeling the Dynamics of Online Learning Activity
Charalampos Mavroforakis

Poster 70: Generic Properties of Scattering Networks
Thomas Wiatowski

Poster 71: Causal Information Bottleneck
Aleksander Wieczorek