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Value-at-Risk Optimization with Gaussian Processes


May 13, 2021
Quoc Phong Nguyen, Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet


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Convolutional Normalizing Flows for Deep Gaussian Processes


Apr 23, 2021
Haibin Yu, Dapeng Liu, Bryan Kian Hsiang Low, Patrick Jaillet

* To appear in Proceedings of the International Joint Conference on Neural Networks 2021 (IJCNN'21). arXiv admin note: text overlap with arXiv:1910.11998 

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An Information-Theoretic Framework for Unifying Active Learning Problems


Dec 19, 2020
Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet

* 35th AAAI Conference on Artificial Intelligence (AAAI 2021), Extended version with derivations, 12 pages 

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Top-$k$ Ranking Bayesian Optimization


Dec 19, 2020
Quoc Phong Nguyen, Sebastian Tay, Bryan Kian Hsiang Low, Patrick Jaillet

* 35th AAAI Conference on Artificial Intelligence (AAAI 2021), Extended version with derivations, 13 pages 

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Variational Bayesian Unlearning


Oct 24, 2020
Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet

* 34th Annual Conference on Neural Information Processing Systems (NeurIPS 2020), Extended version with proofs, 22 pages 

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Federated Bayesian Optimization via Thompson Sampling


Oct 22, 2020
Zhongxiang Dai, Kian Hsiang Low, Patrick Jaillet

* Accepted to 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Extended version with proofs and additional experimental details and results, 25 pages 

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Competitive Ratios for Online Multi-capacity Ridesharing


Sep 16, 2020
Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet

* 28 pages, 4 Figures 

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Zone pAth Construction (ZAC) based Approaches for Effective Real-Time Ridesharing


Sep 13, 2020
Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet

* 48 pages, 22 figures 

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Learning Structure in Nested Logit Models


Aug 18, 2020
Youssef M. Aboutaleb, Moshe Ben-Akiva, Patrick Jaillet


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A Relation Analysis of Markov Decision Process Frameworks


Aug 18, 2020
Tien Mai, Patrick Jaillet


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R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games


Jun 30, 2020
Zhongxiang Dai, Yizhou Chen, Kian Hsiang Low, Patrick Jaillet, Teck-Hua Ho

* Accepted to 37th International Conference on Machine Learning (ICML 2020), Extended version with proofs and additional experimental details and results, 27 pages 

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Inverse Reinforcement Learning with Missing Data


Nov 16, 2019
Tien Mai, Quoc Phong Nguyen, Kian Hsiang Low, Patrick Jaillet


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Generalized Maximum Causal Entropy for Inverse Reinforcement Learning


Nov 16, 2019
Tien Mai, Kennard Chan, Patrick Jaillet


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Incentive-aware Contextual Pricing with Non-parametric Market Noise


Nov 08, 2019
Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang


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Implicit Posterior Variational Inference for Deep Gaussian Processes


Oct 26, 2019
Haibin Yu, Yizhou Chen, Zhongxiang Dai, Kian Hsiang Low, Patrick Jaillet

* 33rd Annual Conference on Neural Information Processing Systems (NeurIPS-19) 

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Optimal Explanations of Linear Models


Jul 08, 2019
Dimitris Bertsimas, Arthur Delarue, Patrick Jaillet, Sebastien Martin

* arXiv admin note: substantial text overlap with arXiv:1907.03419 

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The Price of Interpretability


Jul 08, 2019
Dimitris Bertsimas, Arthur Delarue, Patrick Jaillet, Sebastien Martin


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Stochastic Variational Inference for Fully Bayesian Sparse Gaussian Process Regression Models


Nov 01, 2017
Haibin Yu, Trong Nghia Hoang, Kian Hsiang Low, Patrick Jaillet


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Structured Prediction by Conditional Risk Minimization


Feb 26, 2017
Chong Yang Goh, Patrick Jaillet

* 19 pages, with supplements 

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No-Regret Learnability for Piecewise Linear Losses


Sep 17, 2016
Arthur Flajolet, Patrick Jaillet


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Solving Combinatorial Games using Products, Projections and Lexicographically Optimal Bases


Mar 01, 2016
Swati Gupta, Michel Goemans, Patrick Jaillet


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Gaussian Process Planning with Lipschitz Continuous Reward Functions: Towards Unifying Bayesian Optimization, Active Learning, and Beyond


Nov 21, 2015
Chun Kai Ling, Kian Hsiang Low, Patrick Jaillet

* 30th AAAI Conference on Artificial Intelligence (AAAI 2016), Extended version with proofs, 17 pages 

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Parallel Gaussian Process Regression for Big Data: Low-Rank Representation Meets Markov Approximation


Nov 17, 2014
Kian Hsiang Low, Jiangbo Yu, Jie Chen, Patrick Jaillet

* 29th AAAI Conference on Artificial Intelligence (AAAI 2015), Extended version with proofs, 10 pages 

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Parallel Gaussian Process Regression with Low-Rank Covariance Matrix Approximations


Aug 09, 2014
Jie Chen, Nannan Cao, Kian Hsiang Low, Ruofei Ouyang, Colin Keng-Yan Tan, Patrick Jaillet

* Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI2013) 

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Decentralized Data Fusion and Active Sensing with Mobile Sensors for Modeling and Predicting Spatiotemporal Traffic Phenomena


Aug 09, 2014
Jie Chen, Kian Hsiang Low, Colin Keng-Yan Tan, Ali Oran, Patrick Jaillet, John Dolan, Gaurav Sukhatme

* Appears in Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (UAI2012) 

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