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