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ORCID34th UAI 2018: Monterey, California, USA
- Amir Globerson, Ricardo Silva:
Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, UAI 2018, Monterey, California, USA, August 6-10, 2018. AUAI Press 2018 - Ze Jin, Xiaohan Yan, David S. Matteson:
Testing for Conditional Mean Independence with Covariates through Martingale Difference Divergence. 1-12 - Fang Liu, Zizhan Zheng, Ness B. Shroff:
Analysis of Thompson Sampling for Graphical Bandits Without the Graphs. 13-22 - Magda Gregorova
, Alexandros Kalousis, Stéphane Marchand-Maillet:
Structured nonlinear variable selection. 23-32 - Jose M. Peña:
Identification of Strong Edges in AMP Chain Graphs. 33-42 - Siwei Lyu, Yiming Ying:
A Univariate Bound of Area Under ROC. 43-52 - Christian Donner, Manfred Opper:
Efficient Bayesian Inference for a Gaussian Process Density Model. 53-62 - Craig Sherstan, Dylan R. Ashley, Brendan Bennett, Kenny Young, Adam White, Martha White, Richard S. Sutton:
Comparing Direct and Indirect Temporal-Difference Methods for Estimating the Variance of the Return. 63-72 - Ryan Turner, Brady Neal:
How well does your sampler really work? 73-82 - Yisen Wang, Bo Dai, Lingkai Kong, Sarah Monazam Erfani, James Bailey, Hongyuan Zha:
Learning Deep Hidden Nonlinear Dynamics from Aggregate Data. 83-92 - Yu-Xiang Wang:
Revisiting differentially private linear regression: optimal and adaptive prediction & estimation in unbounded domain. 93-103 - Sabina Marchetti, Alessandro Antonucci:
Imaginary Kinematics. 104-113 - Paul K. Rubenstein, Stephan Bongers, Joris M. Mooij, Bernhard Schölkopf:
From Deterministic ODEs to Dynamic Structural Causal Models. 114-123 - Han Zhao, Geoffrey J. Gordon:
Frank-Wolfe Optimization for Symmetric-NMF under Simplicial Constraint. 124-134 - Roy Adams, Benjamin M. Marlin:
Learning Time Series Segmentation Models from Temporally Imprecise Labels. 135-144 - Alistair Shilton, Santu Rana, Sunil Gupta, Svetha Venkatesh:
Multi-Target Optimisation via Bayesian Optimisation and Linear Programming. 145-155 - Sinong Geng, Zhaobin Kuang, Jie Liu, Stephen J. Wright, David Page:
Stochastic Learning for Sparse Discrete Markov Random Fields with Controlled Gradient Approximation Error. 156-166 - Shuran Zheng, Bo Waggoner, Yang Liu, Yiling Chen:
Active Information Acquisition for Linear Optimization. 167-176 - Bingyi Kang, Jiashi Feng:
Transferable Meta Learning Across Domains. 177-187 - Ruifei Cui, Perry Groot, Moritz Schauer, Tom Heskes:
Learning the Causal Structure of Copula Models with Latent Variables. 188-197 - Fajie Yuan, Xin Xin, Xiangnan He, Guibing Guo, Weinan Zhang, Tat-Seng Chua, Joemon M. Jose:
fBGD: Learning Embeddings From Positive Unlabeled Data with BGD. 198-207 - Esther Derman, Daniel J. Mankowitz, Timothy A. Mann, Shie Mannor:
Soft-Robust Actor-Critic Policy-Gradient. 208-218 - Dmitry Babichev, Francis R. Bach:
Constant Step Size Stochastic Gradient Descent for Probabilistic Modeling. 219-228 - Alkis Gotovos, S. Hamed Hassani, Andreas Krause, Stefanie Jegelka:
Discrete Sampling using Semigradient-based Product Mixtures. 229-237 - Somak Aditya, Yezhou Yang, Chitta Baral, Yiannis Aloimonos:
Combining Knowledge and Reasoning through Probabilistic Soft Logic for Image Puzzle Solving. 238-248 - Tom Rainforth:
Nesting Probabilistic Programs. 249-258 - Zilong Tan, Kimberly Roche, Xiang Zhou, Sayan Mukherjee:
Scalable Algorithms for Learning High-Dimensional Linear Mixed Models. 259-268 - Patrick Forré, Joris M. Mooij:
Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders. 269-278 - Tatiana Shpakova, Francis R. Bach, Anton Osokin:
Marginal Weighted Maximum Log-likelihood for Efficient Learning of Perturb-and-Map models. 279-289 - Hongyi Ding, Young Lee, Issei Sato, Masashi Sugiyama:
Variational Inference for Gaussian Processes with Panel Count Data. 290-299 - Ricardo Pio Monti, Aapo Hyvärinen:
A unified probabilistic model for learning latent factors and their connectivities from high-dimensional data . 300-309 - Jack K. Fitzsimons, Michael A. Osborne, Stephen J. Roberts, Joseph Francis Fitzsimons:
Improved Stochastic Trace Estimation using Mutually Unbiased Bases. 310-318 - Jiaming Huang, Zhao Li, Vincent W. Zheng, Wen Wen, Yifan Yang, Yuanmi Chen:
Unsupervised Multi-view Nonlinear Graph Embedding. 319-328 - Rafael Pinot, Anne Morvan, Florian Yger, Cédric Gouy-Pailler, Jamal Atif:
Graph-based Clustering under Differential Privacy. 329-338 - Jiani Zhang, Xingjian Shi, Junyuan Xie, Hao Ma, Irwin King, Dit-Yan Yeung:
GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs. 339-349 - Søren Wengel Mogensen, Daniel Malinsky, Niels Richard Hansen:
Causal Learning for Partially Observed Stochastic Dynamical Systems. 350-360 - Pashupati Hegde, Markus Heinonen, Samuel Kaski:
Variational zero-inflated Gaussian processes with sparse kernels. 361-371 - Alberto García-Durán, Mathias Niepert:
KBlrn: End-to-End Learning of Knowledge Base Representations with Latent, Relational, and Numerical Features. 372-381 - Yuval Atzmon, Gal Chechik:
Probabilistic AND-OR Attribute Grouping for Zero-Shot Learning. 382-392 - Rianne van den Berg, Leonard Hasenclever, Jakub M. Tomczak, Max Welling:
Sylvester Normalizing Flows for Variational Inference. 393-402 - Yunpu Ma, Marcel Hildebrandt, Volker Tresp, Stephan Baier:
Holistic Representations for Memorization and Inference. 403-413 - Anastasios Kyrillidis:
Simple and practical algorithms for 𝓁p-norm low-rank approximation. 414-424 - Arghya Roy Chaudhuri, Shivaram Kalyanakrishnan:
Quantile-Regret Minimisation in Infinitely Many-Armed Bandits. 425-434 - Minyoung Kim, Vladimir Pavlovic:
Variational Inference for Gaussian Process Models for Survival Analysis. 435-445 - Zhibing Zhao, Haoming Li, Junming Wang, Jeffrey O. Kephart, Nicholas Mattei, Hui Su, Lirong Xia:
A Cost-Effective Framework for Preference Elicitation and Aggregation. 446-456 - Giulia Denevi, Carlo Ciliberto, Dimitris Stamos, Massimiliano Pontil:
Incremental Learning-to-Learn with Statistical Guarantees. 457-466 - Rémy Degenne, Evrard Garcelon, Vianney Perchet:
Bandits with Side Observations: Bounded vs. Logarithmic Regret. 467-476 - Benjamin Bloem-Reddy, Adam Foster, Emile Mathieu, Yee Whye Teh:
Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks. 477-486 - Yizhi Zhu, Oluwasanmi Koyejo:
Clustered Fused Graphical Lasso. 487-496 - David Zheng, Vinson Luo, Jiajun Wu, Joshua B. Tenenbaum:
Unsupervised Learning of Latent Physical Properties Using Perception-Prediction Networks. 497-507 - Difan Zou, Pan Xu, Quanquan Gu:
Subsampled Stochastic Variance-Reduced Gradient Langevin Dynamics. 508-518 - Sebastian Junges, Nils Jansen, Ralf Wimmer, Tim Quatmann, Leonore Winterer, Joost-Pieter Katoen, Bernd Becker:
Finite-State Controllers of POMDPs using Parameter Synthesis. 519-529 - Ilya Shpitser, Eli Sherman:
Identification of Personalized Effects Associated With Causal Pathways. 530-539 - Subhadeep Karan, Matthew Eichhorn, Blake Hurlburt, Grant Iraci, Jaroslaw Zola:
Fast Counting in Machine Learning Applications. 540-549 - Krishnamurthy Dvijotham, Robert Stanforth, Sven Gowal, Timothy A. Mann, Pushmeet Kohli:
A Dual Approach to Scalable Verification of Deep Networks. 550-559 - Lewis Smith, Yarin Gal:
Understanding Measures of Uncertainty for Adversarial Example Detection. 560-569 - Tineke Blom, Anna Klimovskaia, Sara Magliacane, Joris M. Mooij:
Causal Discovery in the Presence of Measurement Error. 570-579 - Xin Wang, Yujia Luo, Daniel Crankshaw, Alexey Tumanov, Fisher Yu, Joseph E. Gonzalez:
IDK Cascades: Fast Deep Learning by Learning not to Overthink. 580-590 - Vinod Nair, Dj Dvijotham, Iain Dunning, Oriol Vinyals:
Learning Fast Optimizers for Contextual Stochastic Integer Programs. 591-600 - Min Ren, Dabao Zhang:
Differential Analysis of Directed Networks. 601-610 - Reid Bixler, Bert Huang:
Sparse-Matrix Belief Propagation. 611-620 - Amirhossein Meisami, Henry Lam, Chen Dong, Abhishek Pani:
Sequential Learning under Probabilistic Constraints. 621-631 - Filjor Broka, Rina Dechter, Alexander Ihler, Kalev Kask:
Abstraction Sampling in Graphical Models. 632-641 - Steindór Sæmundsson, Katja Hofmann, Marc Peter Deisenroth:
Meta Reinforcement Learning with Latent Variable Gaussian Processes. 642-652 - Junzhe Zhang, Elias Bareinboim:
Non-Parametric Path Analysis in Structural Causal Models. 653-662 - Griffin Lacey, Graham W. Taylor, Shawki Areibi:
Stochastic Layer-Wise Precision in Deep Neural Networks. 663-672 - Razieh Nabi, Phyllis Kanki, Ilya Shpitser:
Estimation of Personalized Effects Associated With Causal Pathways. 673-682 - Touqir Sajed, Wesley Chung, Martha White:
High-confidence error estimates for learned value functions. 683-692 - Tanner Fiez, Shreyas Sekar, Liyuan Zheng, Lillian J. Ratliff:
Combinatorial Bandits for Incentivizing Agents with Dynamic Preferences. 693-703 - Vikas K. Garg, Lin Xiao, Ofer Dekel:
Sparse Multi-Prototype Classification. 704-714 - Nico Piatkowski, Katharina Morik:
Fast Stochastic Quadrature for Approximate Maximum-Likelihood Estimation. 715-724 - Qi Lou, Rina Dechter, Alexander Ihler:
Finite-sample Bounds for Marginal MAP. 725-734 - Ilya Shpitser, Robin J. Evans, Thomas S. Richardson:
Acyclic Linear SEMs Obey the Nested Markov Property. 735-745 - Changyou Chen, Ruiyi Zhang, Wenlin Wang, Bai Li, Liqun Chen:
A Unified Particle-Optimization Framework for Scalable Bayesian Sampling. 746-755 - Travis Moore, Weng-Keen Wong:
An Efficient Quantile Spatial Scan Statistic for Finding Unusual Regions in Continuous Spatial Data with Covariates. 756-765 - Yingxue Zhou, Sheng Chen, Arindam Banerjee:
Stable Gradient Descent. 766-775 - Frederick Callaway, Sayan Gul, Paul M. Krueger, Thomas L. Griffiths, Falk Lieder:
Learning to select computations. 776-785 - Kristopher De Asis, Richard S. Sutton:
Per-decision Multi-step Temporal Difference Learning with Control Variates. 786-794 - Xi Tan, Vinayak A. Rao, Jennifer Neville:
The Indian Buffet Hawkes Process to Model Evolving Latent Influences. 795-804 - Aadirupa Saha, Aditya Gopalan:
Battle of Bandits. 805-814 - Rémi Le Priol, Alexandre Piché, Simon Lacoste-Julien:
Adaptive Stochastic Dual Coordinate Ascent for Conditional Random Fields. 815-824 - Ashish Sabharwal, Yexiang Xue:
Adaptive Stratified Sampling for Precision-Recall Estimation. 825-834 - Cristian Guarnizo, Mauricio A. Álvarez:
Fast Kernel Approximations for Latent Force Models and Convolved Multiple-Output Gaussian processes. 835-844 - Ching-An Cheng, Xinyan Yan, Nolan Wagener, Byron Boots:
Fast Policy Learning through Imitation and Reinforcement. 845-855 - Tim R. Davidson, Luca Falorsi, Nicola De Cao, Thomas Kipf, Jakub M. Tomczak:
Hyperspherical Variational Auto-Encoders. 856-865 - Li Chou, Wolfgang Gatterbauer, Vibhav Gogate:
Dissociation-Based Oblivious Bounds for Weighted Model Counting. 866-875 - Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson:
Averaging Weights Leads to Wider Optima and Better Generalization. 876-885 - Gagan Madan, Ankit Anand, Mausam, Parag Singla:
Block-Value Symmetries in Probabilistic Graphical Models. 886-895 - Rahul G. Krishnan, Arjun Khandelwal, Rajesh Ranganath, David A. Sontag:
Max-margin learning with the Bayes factor. 896-905 - Beidi Chen, Anshumali Shrivastava:
Densified Winner Take All (WTA) Hashing for Sparse Datasets. 906-916 - Vishal Sharma, Noman Ahmed Sheikh, Happy Mittal, Vibhav Gogate, Parag Singla:
Lifted Marginal MAP Inference. 917-926 - Ondrej Kuzelka, Yuyi Wang, Jesse Davis, Steven Schockaert:
PAC-Reasoning in Relational Domains. 927-936 - Xiaotian Yu, Han Shao, Michael R. Lyu, Irwin King:
Pure Exploration of Multi-Armed Bandits with Heavy-Tailed Payoffs. 937-946 - Adarsh Subbaswamy, Suchi Saria:
Counterfactual Normalization: Proactively Addressing Dataset Shift Using Causal Mechanisms. 947-957 - Pritee Agrawal, Pradeep Varakantham, William Yeoh:
Decentralized Planning for Non-dedicated Agent Teams with Submodular Rewards in Uncertain Environments. 958-967 - Neal Lawton, Greg Ver Steeg, Aram Galstyan:
A Forest Mixture Bound for Block-Free Parallel Inference. 968-977 - Amin Jaber, Jiji Zhang, Elias Bareinboim:
Causal Identification under Markov Equivalence. 978-987 - Luke B. Hewitt, Maxwell I. Nye, Andreea Gane, Tommi S. Jaakkola, Joshua B. Tenenbaum:
The Variational Homoencoder: Learning to learn high capacity generative models from few examples. 988-997 - Aghiles Salah, Hady W. Lauw:
Probabilistic Collaborative Representation Learning for Personalized Item Recommendation. 998-1008 - Aaron Palmer, Dipak K. Dey, Jinbo Bi:
Reforming Generative Autoencoders via Goodness-of-Fit Hypothesis Testing. 1009-1019 - Sihyeon Seong, Yegang Lee, Youngwook Kee, Dongyoon Han, Junmo Kim:
Towards Flatter Loss Surface via Nonmonotonic Learning Rate Scheduling. 1020-1030 - Shengjia Zhao, Jiaming Song, Stefano Ermon:
A Lagrangian Perspective on Latent Variable Generative Models. 1031-1041 - Stephan Eismann, Daniel Levy, Rui Shu, Stefan Bartzsch, Stefano Ermon:
Bayesian optimization and attribute adjustment. 1042-1052 - Junkyu Lee, Alexander Ihler, Rina Dechter:
Join Graph Decomposition Bounds for Influence Diagrams. 1053-1062 - Kun Zhang, Mingming Gong, Joseph D. Ramsey, Kayhan Batmanghelich, Peter Spirtes, Clark Glymour:
Causal Discovery with Linear Non-Gaussian Models under Measurement Error: Structural Identifiability Results. 1063-1072
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