UAI 2018 Program Schedule

For videos, go to the UAI2018 YouTube channel.

August 6th: Tutorials

Time	Event
09:00am - 10:30am	Anima Anandkumar, Zach Lipton: Tackling Data Scarcity in Deep Learning [Video 1] [Video 2]
10:30am - 11:00am	Coffee break
11:00am - 12:30pm	Matt Hoffman: Bayesian Optimisation [Video]
12:30pm - 02:00pm	Lunch break
02:00pm - 03:30pm	Sebastian Riedel, Johannes Welbl, Dirk Weissenborn: Machine Reading [Video 1] [Video 2]
03:30pm - 04:00pm	Coffee break
04:00pm - 05:30pm	Tengyu Ma: Recent Progress in the Theory of Deep Learning [Video]

August 7th: Main conference

Time	Event
07:30am - 09:00am	Registration
08:45am - 09:00am	Opening words
09:00am - 10:00am	Raquel Urtasun - Invited talk [No video]
10:00am - 10:10am	Short break
10:10am - 11:10am	Oral session 1: Deep Learning
11:10am - 11:40am	Coffee break
11:40pm - 12:40pm	Oral session 2: Inference in Graphical Models
12:40pm - 02:30pm	Lunch break
02:30pm - 03:30pm	Oral session 3: Optimization
03:30pm - 04:00pm	Coffee break
04:00pm - 05:00pm	Michael C. Frank - Invited talk [Video]
05:00pm - 07:00pm	Poster session

August 8th: Main conference

Time	Event
09:00am - 10:00am	Stuart Russell - Invited talk [Video]
10:00am - 10:10am	Short break
10:10am - 11:10am	Oral Session 4: Reinforcement Learning
11:10am - 11:40am	Coffee break
11:40am - 12:40pm	Oral Session 5: Bayesian Nonparametrics
12:40pm - 02:30pm	Lunch break
02:30pm - 03:30pm	Oral Session 6: Sampling
03:30pm - 05:30pm	Poster session
05:30pm - 06:10pm	Oral Session 7: Causality 1
07:00pm - 10:30pm	Banquet at the Monterey Aquarium

August 9th: Main conference

Time	Event
09:00am - 10:00am	Joelle Pineau - Invited talk [Video]
10:00am - 10:10am	Short break
10:10am - 11:10am	Oral Session 8: Causality 2
11:10am - 11:40am	Coffee break
11:40am - 12:40pm	Oral Session 9: Learning Theory
12:40pm - 02:30pm	Lunch break
02:30pm - 04:30pm	Poster session
04:30pm - 05:30pm	Oral Session 10: Latent Variable Models
05:30pm - 06:30pm	Business Meeting

August 10: Workshops*

Time	Event
09:00am - 06:00pm	Safety, Risk and Uncertainty in RL
09:00am - 06:00pm	Causal Inference Workshop
09:00am - 06:00pm	Uncertainty in Deep Learning

*Coffee to be served at 10:30am - 11:00am and 03:30pm - 04:00pm

Oral Session 1 - Deep Learning

	The Variational Homoencoder: Learning to learn high capacity generative models from few examples [Video]
	Luke B. Hewitt, Maxwell I. Nye, Andreea Gane, Tommi Jaakkola, Joshua B. Tenenbaum
	Sylvester Normalizing Flows for Variational Inference [Video]
	Rianne van den Berg, Leonard Hasenclever, Jakub Tomczak, Max Welling
	Hyperspherical Variational Auto-Encoders [Video]
	Tim Davidson, Luca Falorsi, Nicola De Cao, Thomas Kipf, Jakub M. Tomczak

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Oral Session 2 - Inference in Graphical Models

	A Forest Mixture Bound for Block-Free Parallel Inference [Video]
	Neal Lawton, Greg Ver Steeg, Aram Galstyan
	Learning Fast Optimizers for Contextual Stochastic Integer Programs [Video]
	Vinod Nair, Dj Dvijotham, Iain Dunning, Oriol Vinyals
	Abstraction Sampling in Graphical Models [Video]
	Filjor Broka, Rina Dechter, Alexander Ihler, Kalev Kask

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Oral Session 3 - Optimization

	Adaptive Stratified Sampling for Precision-Recall Estimation [Video]
	Ashish Sabharwal, Yexiang Xue
	Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders* [Video]
	Patrick Forré, Joris M. Mooij
	A Dual Approach to Scalable Verification of Deep Networks (Best Paper Award) [Video]
	Krishnamurthy Dvijotham, Robert Stanforth, Sven Gowal, Timothy Mann, Pushmeet Kohli

*Kindly agreed to be moved from Session 10, "Latent variable models", to accommodate speaker's travel constraints.

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Oral Session 4 - Reinforcement Learning

	Fast Policy Learning through Imitation and Reinforcement [Video]
	Ching-An Cheng, Xinyan Yan, Nolan Wagener, Byron Boots
	Comparing Direct and Indirect Temporal-Difference Methods for Estimating the Variance of the Return [Video]
	Craig Sherstan, Dylan R. Ashley, Brendan Bennett, Kenny Young, Adam White, Martha White, Richard S. Sutton
	Finite-State Controllers of POMDPs using Parameter Synthesis [Video]
	Sebastian Junges, Nils Jansen, Ralf Wimmer, Tim Quatmann, Leonore Winterer, Joost-Pieter Katoen, Bernd Becker

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Oral Session 5 - Bayesian Nonparametrics

	Efficient Bayesian Inference for a Gaussian Process Density Model [Video]
	Christian Donner, Manfred Opper
	Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks [Video]
	Benjamin Bloem-Reddy, Adam Foster, Emile Mathieu, Yee Whye Teh
	Variational zero-inflated Gaussian processes with sparse kernels [Video]
	Pashupati Hegde, Markus Heinonen, Samuel Kaski

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Oral Session 6 - Sampling

	Lifted Marginal MAP Inference [Video]
	Vishal Sharma, Noman Ahmed Sheikh, Happy Mittal, Vibhav Gogate, Parag Singla
	A Unified Particle-Optimization Framework for Scalable Bayesian Sampling [Video]
	Changyou Chen, Ruiyi Zhang, Wenlin Wang, Bai Li, Liqun Chen
	Discrete Sampling using Semigradient-based Product Mixtures [Video]
	Alkis Gotovos, Hamed Hassani, Andreas Krause, Stefanie Jegelka

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Oral Session 7 - Causality 1

	Causal Discovery with Linear Non-Gaussian Models under Measurement Error: Structural Identifiability Results [Video]
	Kun Zhang, Mingming Gong, Joseph Ramsey, Kayhan Batmanghelich, Peter Spirtes, Clark Glymour
	Causal Identification under Markov Equivalence (Best Student Paper Award) [Video]
	Amin Jaber, Jiji Zhang, Elias Bareinboim

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Oral Session 8 - Causality 2

	Causal Learning for Partially Observed Stochastic Dynamical Systems [Video]
	Søren Wengel Mogensen, Daniel Malinsky, Niels Richard Hansen
	Identification of Personalized Effects Associated With Causal Pathways [Video]
	Ilya Shpitser, Eli Sherman
	Non-Parametric Path Analysis in Structural Causal Models [Video]
	Junzhe Zhang, Elias Bareinboim

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Oral Session 9 - Learning Theory

	Towards Flatter Loss Surface via Nonmonotonic Learning Rate Scheduling [Video]
	Sihyeon Seong, Yegang Lee, Youngwook Kee, Dongyoon Han, Junmo Kim
	Averaging Weights Leads to Wider Optima and Better Generalization [Video]
	Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, Andrew Gordon Wilson
	Revisiting differentially private linear regression: optimal and adaptive prediction & estimation in unbounded domain [Video]
	Yu-Xiang Wang

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Oral Session 10 - Latent Variable Models

	Unsupervised Learning of Latent Physical Properties Using Perception-Prediction Networks [Video]
	David Zheng, Vinson Luo, Jiajun Wu, Joshua Tenenbaum
	A Lagrangian Perspective on Latent Variable Generative Models [Video]
	Shengjia Zhao, Jiaming Song, Stefano Ermon
	Constant Step Size Stochastic Gradient Descent for Probabilistic Modeling* [Video]
	Dmitry Babichev, Francis Bach

*Moved from Session 3, "Optimization", to accommodate speaker's travel constraints.

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Poster Session - August 7th - Tuesday

	Sparse Multi-Prototype Classification
	Vikas K. Garg, Lin Xiao, Ofer Dekel
	A Univariate Bound of Area Under ROC
	Siwei Lyu, Yiming Ying
	How well does your sampler really work?
	Ryan Turner, Brady Neal
	From Deterministic ODEs to Dynamic Structural Causal Models
	Paul K. Rubenstein, Stephan Bongers, Joris M. Mooij, Bernhard Schoelkopf
	Learning Time Series Segmentation Models from Temporally Imprecise Labels
	Roy Adams, Benjamin M. Marlin
	Stochastic Learning for Sparse Discrete Markov Random Fields with Controlled Gradient Approximation Error
	Sinong Geng, Zhaobin Kuang, Jie Liu, Stephen Wright, David Page
	Active Information Acquisition for Linear Optimization
	Shuran Zheng, Bo Waggoner, Yang Liu, Yiling Chen
	Learning the Causal Structure of Copula Models with Latent Variables
	Ruifei Cui, Perry Groot, Moritz Schauer, Tom Heskes
	Constant Step Size Stochastic Gradient Descent for Probabilistic Modeling
	Dmitry Babichev, Francis Bach
	Variational Inference for Gaussian Processes with Panel Count Data
	Hongyi Ding, Young Lee, Issei Sato, Masashi Sugiyama
	Unsupervised Multi-view Nonlinear Graph Embedding
	Jiaming Huang, Zhao Li, Vincent W. Zheng, Wen Wen, Yifan Yang, Yuanmi Chen
	GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs
	Jiani Zhang, Xingjian Shi, Junyuan Xie, Hao Ma, Irwin King, Dit-yan Yeung
	Sylvester Normalizing Flows for Variational Inference
	Rianne van den Berg, Leonard Hasenclever, Jakub Tomczak, Max Welling
	Holistic Representations for Memorization and Inference
	Yunpu Ma, Marcel Hildebrandt, Volker Tresp, Stephan Baier
	Variational Inference for Gaussian Process Models for Survival Analysis
	Minyoung Kim, Vladimir Pavlovic
	A Cost-Effective Framework for Preference Elicitation and Aggregation
	Zhibing Zhao, Haoming Li, Junming Wang, Jeffrey O. Kephart, Nicholas Mattei, Hui Su, Lirong Xia
	Clustered Fused Graphical Lasso
	Yizhi Zhu, Oluwasanmi Koyejo
	A Dual Approach to Scalable Verification of Deep Networks
	Krishnamurthy Dvijotham, Robert Stanforth, Sven Gowal, Timothy Mann, Pushmeet Kohli
	Causal Discovery in the Presence of Measurement Error
	Tineke Blom, Anna Klimovskaia, Sara Magliacane, Joris M. Mooij
	Learning Fast Optimizers for Contextual Stochastic Integer Programs
	Vinod Nair, Dj Dvijotham, Iain Dunning, Oriol Vinyals
	High-confidence error estimates for learned value functions
	Touqir Sajed, Wesley Chung, Martha White
	Stable Gradient Descent
	Yingxue Zhou, Sheng Chen, Arindam Banerjee
	Learning to select computations
	Frederick Callaway, Sayan Gul, Paul M. Krueger, Thomas L. Griffiths, Falk Lieder
	Per-decision Multi-step Temporal Difference Learning with Control Variates
	Kristopher De Asis, Richard S. Sutton
	The Indian Buffet Hawkes Process to Model Evolving Latent Influences
	Xi Tan, Vinayak Rao, Jennifer Neville
	Adaptive Stratified Sampling for Precision-Recall Estimation
	Ashish Sabharwal, Yexiang Xue
	Hyperspherical Variational Auto-Encoders
	Tim Davidson, Luca Falorsi, Nicola De Cao, Thomas Kipf, Jakub M. Tomczak
	Max-margin learning with the Bayes factor
	Rahul G. Krishnan, Arjun Khandelwal, Rajesh Ranganath, David Sontag
	Densified Winner Take All (WTA) Hashing for Sparse Datasets
	Beidi Chen, Anshumali Shrivastava
	Lifted Marginal MAP Inference
	Vishal Sharma, Noman Ahmed Sheikh, Happy Mittal, Vibhav Gogate, Parag Singla
	Pure Exploration of Multi-Armed Bandits with Heavy-Tailed Payoffs
	Xiaotian Yu, Han Shao, Michael R. Lyu, Irwin King
	Decentralized Planning for Non-dedicated Agent Teams with Submodular Rewards in Uncertain Environments
	Pritee Agrawal, Pradeep Varakantham, William Yeoh
	A Forest Mixture Bound for Block-Free Parallel Inference
	Neal Lawton, Greg Ver Steeg, Aram Galstyan
	The Variational Homoencoder: Learning to learn high capacity generative models from few examples
	Luke B. Hewitt, Maxwell I. Nye, Andreea Gane, Tommi Jaakkola, Joshua B. Tenenbaum
	Bayesian optimization and attribute adjustment
	Stephan Eismann, Daniel Levy, Rui Shu, Stefan Bartzsch, Stefano Ermon
	Join Graph Decomposition Bounds for Influence Diagrams
	Junkyu Lee, Alexander Ihler, Rina Dechter
	Soft-Robust Actor-Critic Policy-Gradient
	Esther Derman, Daniel J Mankowitz, Timothy A Mann, Shie Mannor

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Poster Session - August 8th - Wednesday

	Identification of Strong Edges in AMP Chain Graphs
	Jose M. Peña
	Efficient Bayesian Inference for a Gaussian Process Density Model
	Christian Donner, Manfred Opper
	Comparing Direct and Indirect Temporal-Difference Methods for Estimating the Variance of the Return
	Craig Sherstan, Dylan R. Ashley, Brendan Bennett, Kenny Young, Adam White, Martha White, Richard S. Sutton
	Multi-Target Optimisation via Bayesian Optimisation and Linear Programming
	Alistair Shilton, Santu Rana, Sunil Gupta, Svetha Venkatesh
	$f_{BGD}$: Learning Embeddings From Positive Unlabeled Data with BGD
	Fajie YUAN, Xin Xin, Xiangnan He, Guibing Guo, Weinan Zhang, CHUA Tat-Seng, Joemon Jose
	Discrete Sampling using Semigradient-based Product Mixtures
	Alkis Gotovos, Hamed Hassani, Andreas Krause, Stefanie Jegelka
	Combining Knowledge and Reasoning through Probabilistic Soft Logic for Image Puzzle Solving
	Somak Aditya, Yezhou Yang, Chitta Baral, Yiannis Aloimonos
	Marginal Weighted Maximum Log-likelihood for Efficient Learning of Perturb-and-Map models
	Tatiana Shpakova, Francis Bach, Anton Osokin
	Variational zero-inflated Gaussian processes with sparse kernels
	Pashupati Hegde, Markus Heinonen, Samuel Kaski
	KBlrn: End-to-End Learning of Knowledge Base Representations with Latent, Relational, and Numerical Features
	Alberto Garcia-Duran, Mathias Niepert
	Probabilistic AND-OR Attribute Grouping for Zero-Shot Learning
	Yuval Atzmon, Gal Chechik
	Quantile-Regret Minimisation in Infinitely Many-Armed Bandits
	Arghya Roy Chaudhuri, Shivaram Kalyanakrishnan
	Incremental Learning-to-Learn with Statistical Guarantees
	Giulia Denevi, Carlo Ciliberto, Dimitris Stamos, Massimiliano Pontil
	Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks
	Benjamin Bloem-Reddy, Adam Foster, Emile Mathieu, Yee Whye Teh
	Subsampled Stochastic Variance-Reduced Gradient Langevin Dynamics
	Difan Zou, Pan Xu, Quanquan Gu
	Finite-State Controllers of POMDPs using Parameter Synthesis
	Sebastian Junges, Nils Jansen, Ralf Wimmer, Tim Quatmann, Leonore Winterer, Joost-Pieter Katoen, Bernd Becker
	Fast Counting in Machine Learning Applications
	Subhadeep Karan, Matthew Eichhorn, Blake Hurlburt, Grant Iraci, Jaroslaw Zola
	Understanding Measures of Uncertainty for Adversarial Example Detection
	Lewis Smith, Yarin Gal
	IDK Cascades: Fast Deep Learning by Learning not to Overthink
	Xin Wang, Yujia Luo, Daniel Crankshaw, Alexey Tumanov, Fisher Yu, Joseph E. Gonzalez
	Differential Analysis of Directed Networks
	Min Ren, Dabao Zhang
	Sequential Learning under Probabilistic Constraints
	Amirhossein Meisami, Henry Lam, Chen Dong, Abhishek Pani
	Abstraction Sampling in Graphical Models
	Filjor Broka, Rina Dechter, Alexander Ihler, Kalev Kask
	Fast Stochastic Quadrature for Approximate Maximum-Likelihood Estimation
	Nico Piatkowski, Katharina Morik
	A Unified Particle-Optimization Framework for Scalable Bayesian Sampling
	Changyou Chen, Ruiyi Zhang, Wenlin Wang, Bai Li, Liqun Chen
	An Efficient Quantile Spatial Scan Statistic for Finding Unusual Regions in Continuous Spatial Data with Covariates
	Travis Moore, Weng-Keen Wong
	Battle of Bandits
	Aadirupa Saha, Aditya Gopalan
	Fast Kernel Approximations for Latent Force Models and Convolved Multiple-Output Gaussian processes
	Cristian Guarnizo, Mauricio Álvarez
	Fast Policy Learning through Imitation and Reinforcement
	Ching-An Cheng, Xinyan Yan, Nolan Wagener, Byron Boots
	Dissociation-Based Oblivious Bounds for Weighted Model Counting
	Li Chou, Wolfgang Gatterbauer, Vibhav Gogate
	Block-Value Symmetries in Probabilistic Graphical Models
	Gagan Madan, Ankit Anand, Mausam, Parag Singla
	PAC-Reasoning in Relational Domains
	Ondrej Kuzelka, Yuyi Wang, Jesse Davis, Steven Schockaert
	Causal Identification under Markov Equivalence
	Amin Jaber, Jiji Zhang, Elias Bareinboim
	Reforming Generative Autoencoders via Goodness-of-Fit Hypothesis Testing
	Aaron Palmer, Dipak Dey, Jinbo Bi
	Causal Discovery with Linear Non-Gaussian Models under Measurement Error: Structural Identifiability Results
	Kun Zhang, Mingming Gong, Joseph Ramsey, Kayhan Batmanghelich, Peter Spirtes, Clark Glymour

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Poster Session - August 9th - Thursday

	Testing for Conditional Mean Independence with Covariates through Martingale Difference Divergence
	Ze Jin, Xiaohan Yan, David S. Matteson
	Analysis of Thompson Sampling for Graphical Bandits Without the Graphs
	Fang Liu, Zizhan Zheng, Ness Shroff
	Structured nonlinear variable selection
	Magda Gregorova, Alexandros Kalousis, Stephane Marchand-Maillet
	Learning Deep Hidden Nonlinear Dynamics from Aggregate Data
	Yisen Wang, Bo Dai, Lingkai Kong, Sarah Monazam Erfani, James Bailey, Hongyuan Zha
	Revisiting differentially private linear regression: optimal and adaptive prediction & estimation in unbounded domain
	Yu-Xiang Wang
	Imaginary Kinematics
	Sabina Marchetti, Alessandro Antonucci
	Frank-Wolfe Optimization for Symmetric-NMF under Simplicial Constraint
	Han Zhao, Geoff Gordon
	Transferable Meta Learning Across Domains
	Bingyi Kang, Jiashi Feng
	Nesting Probabilistic Programs
	Tom Rainforth
	Scalable Algorithms for Learning High-Dimensional Linear Mixed Models
	Zilong Tan, Kimberly Roche, Xiang Zhou, Sayan Mukherjee
	Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders
	Patrick Forré, Joris M. Mooij
	A unified probabilistic model for learning latent factors and their connectivities from high-dimensional data
	Ricardo Pio Monti, Aapo Hyvarinen
	Improved Stochastic Trace Estimation using Mutually Unbiased Bases
	JK Fitzsimons, MA Osborne, SJ Roberts, JF Fitzsimons
	Graph-based Clustering under Differential Privacy
	Rafael Pinot, Anne Morvan, Florian Yger, Cedric Gouy-Pailler, Jamal Atif
	Causal Learning for Partially Observed Stochastic Dynamical Systems
	Søren Wengel Mogensen, Daniel Malinsky, Niels Richard Hansen
	Simple and practical algorithms for $\ell_p$-norm low-rank approximation
	Anastasios Kyrillidis
	Bandits with Side Observations: Bounded vs. Logarithmic Regret
	Rémy Degenne, Evrard Garcelon, Vianney Perchet
	Unsupervised Learning of Latent Physical Properties Using Perception-Prediction Networks
	David Zheng, Vinson Luo, Jiajun Wu, Joshua Tenenbaum
	Identification of Personalized Effects Associated With Causal Pathways
	Ilya Shpitser, Eli Sherman
	Sparse-Matrix Belief Propagation
	Reid Bixler, Bert Huang
	Meta Reinforcement Learning with Latent Variable Gaussian Processes
	Steindor Saemundsson, Katja Hofmann, Marc Peter Deisenroth
	Non-Parametric Path Analysis in Structural Causal Models
	Junzhe Zhang, Elias Bareinboim
	Stochastic Layer-Wise Precision in Deep Neural Networks
	Griffin Lacey, Graham W. Taylor, Shawki Areibi
	Estimation of Personalized Effects Associated With Causal Pathways
	Razieh Nabi, Phyllis Kanki, Ilya Shpitser
	Combinatorial Bandits for Incentivizing Agents with Dynamic Preferences
	Tanner Fiez, Shreyas Sekar, Liyuan Zheng, Lillian Ratliff
	Finite-sample Bounds for Marginal MAP
	Qi Lou, Rina Dechter, Alexander Ihler
	Acyclic Linear SEMs Obey the Nested Markov Property
	Ilya Shpitser, Robin Evans, Thomas S. Richardson
	Adaptive Stochastic Dual Coordinate Ascent for Conditional Random Fields
	Rémi Le Priol, Alexandre Piché, Simon Lacoste-Julien
	Averaging Weights Leads to Wider Optima and Better Generalization
	Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, Andrew Gordon Wilson
	Counterfactual Normalization: Proactively Addressing Dataset Shift Using Causal Mechanisms
	Adarsh Subbaswamy, Suchi Saria
	Probabilistic Collaborative Representation Learning for Personalized Item Recommendation
	Aghiles Salah, Hady W. Lauw
	Towards Flatter Loss Surface via Nonmonotonic Learning Rate Scheduling
	Sihyeon Seong, Yegang Lee, Youngwook Kee, Dongyoon Han, Junmo Kim
	A Lagrangian Perspective on Latent Variable Generative Models
	Shengjia Zhao, Jiaming Song, Stefano Ermon

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