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ORCIDPatrick Jaillet
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- affiliation: MIT, USA
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2020 – today
- 2024
[j65]Moïse Blanchard
, Alexandre Jacquillat, Patrick Jaillet:
Probabilistic Bounds on the k-Traveling Salesman Problem and the Traveling Repairman Problem. Math. Oper. Res. 49(2): 1169-1191 (2024)- [c96]Quoc Phong Nguyen, Wan Theng Ruth Chew, Le Song, Bryan Kian Hsiang Low, Patrick Jaillet:
Optimistic Bayesian Optimization with Unknown Constraints. ICLR 2024 - [c95]Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet:
Meta-VBO: Utilizing Prior Tasks in Optimizing Risk Measures with Gaussian Processes. ICLR 2024 - [c94]Xiaoqiang Lin, Zhaoxuan Wu, Zhongxiang Dai, Wenyang Hu, Yao Shu, See-Kiong Ng, Patrick Jaillet, Bryan Kian Hsiang Low:
Use Your INSTINCT: INSTruction optimization for LLMs usIng Neural bandits Coupled with Transformers. ICML 2024 - [c93]Rachael Hwee Ling Sim, Jue Fan, Xiao Tian, Patrick Jaillet, Bryan Kian Hsiang Low:
Deletion-Anticipative Data Selection with a Limited Budget. ICML 2024 - [c92]Behrooz Tahmasebi, Ashkan Soleymani, Dara Bahri, Stefanie Jegelka, Patrick Jaillet:
A Universal Class of Sharpness-Aware Minimization Algorithms. ICML 2024 - [c91]Yuan Deng, Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang, Vahab Mirrokni:
Individual Welfare Guarantees in the Autobidding World with Machine-learned Advice. WWW 2024: 267-275 - [i90]Patrick Jaillet, Chara Podimata, Zijie Zhou:
Grace Period is All You Need: Individual Fairness without Revenue Loss in Revenue Management. CoRR abs/2402.08533 (2024) - [i89]Rachael Hwee Ling Sim, Yehong Zhang, Trong Nghia Hoang, Xinyi Xu, Bryan Kian Hsiang Low, Patrick Jaillet:
Incentives in Private Collaborative Machine Learning. CoRR abs/2404.01676 (2024) - [i88]Zhaoxuan Wu, Xiaoqiang Lin, Zhongxiang Dai, Wenyang Hu, Yao Shu, See-Kiong Ng, Patrick Jaillet, Bryan Kian Hsiang Low:
Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars. CoRR abs/2405.16122 (2024) - [i87]Xiaoqiang Lin, Zhongxiang Dai, Arun Verma, See-Kiong Ng, Patrick Jaillet, Bryan Kian Hsiang Low:
Prompt Optimization with Human Feedback. CoRR abs/2405.17346 (2024) - [i86]Behrooz Tahmasebi, Ashkan Soleymani, Dara Bahri, Stefanie Jegelka, Patrick Jaillet:
A Universal Class of Sharpness-Aware Minimization Algorithms. CoRR abs/2406.03682 (2024) - [i85]Arun Verma, Zhongxiang Dai, Xiaoqiang Lin, Patrick Jaillet, Bryan Kian Hsiang Low:
Neural Dueling Bandits. CoRR abs/2407.17112 (2024) - [i84]Moïse Blanchard, Patrick Jaillet:
Near-Optimal Mechanisms for Resource Allocation Without Monetary Transfers. CoRR abs/2408.10066 (2024) - 2023
- [j64]Itai Ashlagi, Maximilien Burq, Chinmoy Dutta, Patrick Jaillet, Amin Saberi, Chris Sholley:
Edge-Weighted Online Windowed Matching. Math. Oper. Res. 48(2): 999-1016 (2023) - [c90]Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang, Vahab Mirrokni:
Pricing against a Budget and ROI Constrained Buyer. AISTATS 2023: 9282-9307 - [c89]Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang:
Incentive-aware Contextual Pricing with Non-parametric Market Noise. AISTATS 2023: 9331-9361 - [c88]Sohil Shah, Saurabh Amin, Patrick Jaillet:
Information Disclosure About Booster Efficacy in a Non-Stationary Environment. CDC 2023: 5222-5229 - [c87]Moïse Blanchard, Junhui Zhang, Patrick Jaillet:
Quadratic Memory is Necessary for Optimal Query Complexity in Convex Optimization: Center-of-Mass is Pareto-Optimal. COLT 2023: 4696-4736 - [c86]Zhongxiang Dai, Yao Shu, Arun Verma, Flint Xiaofeng Fan, Bryan Kian Hsiang Low, Patrick Jaillet:
Federated Neural Bandits. ICLR 2023 - [c85]Thanh Lam, Arun Verma, Bryan Kian Hsiang Low, Patrick Jaillet:
Risk-Aware Reinforcement Learning with Coherent Risk Measures and Non-linear Function Approximation. ICLR 2023 - [c84]Yao Shu, Zhongxiang Dai, Weicong Sng, Arun Verma, Patrick Jaillet, Bryan Kian Hsiang Low:
Zeroth-Order Optimization with Trajectory-Informed Derivative Estimation. ICLR 2023 - [c83]Yuan Deng, Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang, Vahab Mirrokni:
Multi-channel Autobidding with Budget and ROI Constraints. ICML 2023: 7617-7644 - [c82]Francesco Quinzan, Ashkan Soleymani, Patrick Jaillet, Cristian R. Rojas, Stefan Bauer:
DRCFS: Doubly Robust Causal Feature Selection. ICML 2023: 28468-28491 - [c81]Moïse Blanchard, Junhui Zhang, Patrick Jaillet:
Memory-Constrained Algorithms for Convex Optimization. NeurIPS 2023 - [c80]Zhongxiang Dai, Gregory Kang Ruey Lau, Arun Verma, Yao Shu, Bryan Kian Hsiang Low, Patrick Jaillet:
Quantum Bayesian Optimization. NeurIPS 2023 - [c79]Zhongxiang Dai, Quoc Phong Nguyen, Sebastian Tay, Daisuke Urano, Richalynn Leong, Bryan Kian Hsiang Low, Patrick Jaillet:
Batch Bayesian Optimization For Replicable Experimental Design. NeurIPS 2023 - [c78]Rachael Hwee Ling Sim, Yehong Zhang, Nghia Hoang, Xinyi Xu, Bryan Kian Hsiang Low, Patrick Jaillet:
Incentives in Private Collaborative Machine Learning. NeurIPS 2023 - [i83]Moïse Blanchard, Steve Hanneke, Patrick Jaillet:
Contextual Bandits and Optimistically Universal Learning. CoRR abs/2301.00241 (2023) - [i82]Yuan Deng, Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang, Vahab Mirrokni:
Multi-channel Autobidding with Budget and ROI Constraints. CoRR abs/2302.01523 (2023) - [i81]Moïse Blanchard, Junhui Zhang, Patrick Jaillet:
Quadratic Memory is Necessary for Optimal Query Complexity in Convex Optimization: Center-of-Mass is Pareto-Optimal. CoRR abs/2302.04963 (2023) - [i80]Gauthier Guinet, Saurabh Amin, Patrick Jaillet:
Effective Dimension in Bandit Problems under Censorship. CoRR abs/2302.06916 (2023) - [i79]Moïse Blanchard, Steve Hanneke, Patrick Jaillet:
Non-stationary Contextual Bandits and Universal Learning. CoRR abs/2302.07186 (2023) - [i78]Francesco Quinzan, Ashkan Soleymani, Patrick Jaillet, Cristian R. Rojas, Stefan Bauer:
DRCFS: Doubly Robust Causal Feature Selection. CoRR abs/2306.07024 (2023) - [i77]Moïse Blanchard, Junhui Zhang, Patrick Jaillet:
Memory-Constrained Algorithms for Convex Optimization via Recursive Cutting-Planes. CoRR abs/2306.10096 (2023) - [i76]Negin Golrezaei, Patrick Jaillet, Zijie Zhou:
Online Resource Allocation with Convex-set Machine-Learned Advice. CoRR abs/2306.12282 (2023) - [i75]Saurabh Amin, Patrick Jaillet, Haripriya Pulyassary, Manxi Wu:
Market Design for Dynamic Pricing and Pooling in Capacitated Networks. CoRR abs/2307.03994 (2023) - [i74]Xiaoqiang Lin, Zhaoxuan Wu, Zhongxiang Dai, Wenyang Hu, Yao Shu, See-Kiong Ng, Patrick Jaillet, Bryan Kian Hsiang Low:
Use Your INSTINCT: INSTruction optimization usIng Neural bandits Coupled with Transformers. CoRR abs/2310.02905 (2023) - [i73]Zhongxiang Dai, Gregory Kang Ruey Lau, Arun Verma, Yao Shu, Bryan Kian Hsiang Low, Patrick Jaillet:
Quantum Bayesian Optimization. CoRR abs/2310.05373 (2023) - [i72]Junhui Zhang, Patrick Jaillet:
Secretary Problems with Random Number of Candidates: How Prior Distributional Information Helps. CoRR abs/2310.07884 (2023) - [i71]Zhongxiang Dai, Quoc Phong Nguyen, Sebastian Shenghong Tay, Daisuke Urano, Richalynn Leong, Bryan Kian Hsiang Low, Patrick Jaillet:
Batch Bayesian Optimization for Replicable Experimental Design. CoRR abs/2311.01195 (2023) - [i70]Emmanouil Angelis, Francesco Quinzan, Ashkan Soleymani, Patrick Jaillet, Stefan Bauer:
Doubly Robust Structure Identification from Temporal Data. CoRR abs/2311.06012 (2023) - [i69]Sohil Shah, Saurabh Amin, Patrick Jaillet:
Information Design for Hybrid Work under Infectious Disease Transmission Risk. CoRR abs/2312.04073 (2023) - [i68]Rafael Hanashiro, Patrick Jaillet:
Distribution-Dependent Rates for Multi-Distribution Learning. CoRR abs/2312.13130 (2023) - 2022
- [j63]Supriyo Ghosh, Patrick Jaillet:
An iterative security game for computing robust and adaptive network flows. Comput. Oper. Res. 138: 105558 (2022) - [j62]Patrick Jaillet, Sanjay Dominik Jena, Tsan Sheng Adam Ng, Melvyn Sim:
Satisficing Models Under Uncertainty. INFORMS J. Optim. 4(4): 347-372 (2022) - [j61]Patrick Jaillet, Gar Goei Loke, Melvyn Sim:
Strategic Workforce Planning Under Uncertainty. Oper. Res. 70(2): 1042-1065 (2022) - [j60]Mathieu Dahan, Saurabh Amin, Patrick Jaillet:
Probability Distributions on Partially Ordered Sets and Network Interdiction Games. Math. Oper. Res. 47(1): 458-484 (2022) - [c77]Sohil Shah, Saurabh Amin, Patrick Jaillet:
Optimal Information Provision for Strategic Hybrid Workers. CDC 2022: 3807-3814 - [c76]Zhongxiang Dai, Yao Shu, Bryan Kian Hsiang Low, Patrick Jaillet:
Sample-Then-Optimize Batch Neural Thompson Sampling. NeurIPS 2022 - [c75]Gauthier Guinet, Saurabh Amin, Patrick Jaillet:
Effective Dimension in Bandit Problems under Censorship. NeurIPS 2022 - [c74]Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet:
Trade-off between Payoff and Model Rewards in Shapley-Fair Collaborative Machine Learning. NeurIPS 2022 - [c73]Zhongxiang Dai, Yizhou Chen, Haibin Yu, Bryan Kian Hsiang Low, Patrick Jaillet:
On provably robust meta-Bayesian optimization. UAI 2022: 475-485 - [i67]Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet:
Rectified Max-Value Entropy Search for Bayesian Optimization. CoRR abs/2202.13597 (2022) - [i66]Moïse Blanchard, Patrick Jaillet:
Universal Regression with Adversarial Responses. CoRR abs/2203.05067 (2022) - [i65]Sohil Shah, Saurabh Amin, Patrick Jaillet:
Optimal Information Provision for Strategic Hybrid Workers. CoRR abs/2205.02732 (2022) - [i64]Zhongxiang Dai, Yao Shu, Arun Verma, Flint Xiaofeng Fan, Bryan Kian Hsiang Low, Patrick Jaillet:
Federated Neural Bandit. CoRR abs/2205.14309 (2022) - [i63]Zhongxiang Dai, Yizhou Chen, Haibin Yu, Bryan Kian Hsiang Low, Patrick Jaillet:
On Provably Robust Meta-Bayesian Optimization. CoRR abs/2206.06872 (2022) - [i62]The Viet Bui, Tien Mai, Patrick Jaillet:
Weighted Maximum Entropy Inverse Reinforcement Learning. CoRR abs/2208.09611 (2022) - [i61]Yuan Deng, Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang, Vahab Mirrokni:
Fairness in the Autobidding World with Machine-learned Advice. CoRR abs/2209.04748 (2022) - [i60]Zhongxiang Dai, Yao Shu, Bryan Kian Hsiang Low, Patrick Jaillet:
Sample-Then-Optimize Batch Neural Thompson Sampling. CoRR abs/2210.06850 (2022) - [i59]Moïse Blanchard, Alexandre Jacquillat, Patrick Jaillet:
Probabilistic bounds on the k-Traveling Salesman Problem and the Traveling Repairman Problem. CoRR abs/2211.11063 (2022) - [i58]Moïse Blanchard, Alexandre Jacquillat, Patrick Jaillet:
Additional Results and Extensions for the paper "Probabilistic bounds on the k-Traveling Salesman Problem and the Traveling Repairman Problem". CoRR abs/2211.11065 (2022) - 2021
- [j59]Florian Delavernhe, Patrick Jaillet, André Rossi, Marc Sevaux:
Planning a multi-sensors search for a moving target considering traveling costs. Eur. J. Oper. Res. 292(2): 469-482 (2021) - [j58]Hossein Hashemi Doulabi, Patrick Jaillet, Gilles Pesant, Louis-Martin Rousseau:
Exploiting the Structure of Two-Stage Robust Optimization Models with Exponential Scenarios. INFORMS J. Comput. 33(1): 143-162 (2021) - [j57]Dawsen Hwang, Patrick Jaillet, Vahideh H. Manshadi:
Online Resource Allocation Under Partially Predictable Demand. Oper. Res. 69(3): 895-915 (2021) - [j56]Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet:
Zone pAth Construction (ZAC) based Approaches for Effective Real-Time Ridesharing. J. Artif. Intell. Res. 70: 119-167 (2021) - [j55]Maaike Hoogeboom, Yossiri Adulyasak, Wout Dullaert, Patrick Jaillet:
The Robust Vehicle Routing Problem with Time Window Assignments. Transp. Sci. 55(2): 395-413 (2021) - [c72]Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet:
An Information-Theoretic Framework for Unifying Active Learning Problems. AAAI 2021: 9126-9134 - [c71]Quoc Phong Nguyen, Sebastian Tay, Bryan Kian Hsiang Low, Patrick Jaillet:
Top-k Ranking Bayesian Optimization. AAAI 2021: 9135-9143 - [c70]Thanh Chi Lam, Trong Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet:
Model Fusion for Personalized Learning. ICML 2021: 5948-5958 - [c69]Quoc Phong Nguyen, Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet:
Value-at-Risk Optimization with Gaussian Processes. ICML 2021: 8063-8072 - [c68]Rachael Hwee Ling Sim, Yehong Zhang, Bryan Kian Hsiang Low, Patrick Jaillet:
Collaborative Bayesian Optimization with Fair Regret. ICML 2021: 9691-9701 - [c67]Haibin Yu, Dapeng Liu, Bryan Kian Hsiang Low, Patrick Jaillet:
Convolutional Normalizing Flows for Deep Gaussian Processes. IJCNN 2021: 1-6 - [c66]Quoc Phong Nguyen, Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet:
Optimizing Conditional Value-At-Risk of Black-Box Functions. NeurIPS 2021: 4170-4180 - [c65]Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet:
Differentially Private Federated Bayesian Optimization with Distributed Exploration. NeurIPS 2021: 9125-9139 - [c64]Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet:
Learning to learn with Gaussian processes. UAI 2021: 1466-1475 - [c63]Quoc Phong Nguyen, Zhaoxuan Wu, Bryan Kian Hsiang Low, Patrick Jaillet:
Trusted-maximizers entropy search for efficient Bayesian optimization. UAI 2021: 1486-1495 - [i57]Saurabh Amin, Patrick Jaillet, Manxi Wu:
Efficient Carpooling and Toll Pricing for Autonomous Transportation. CoRR abs/2102.09132 (2021) - [i56]Haibin Yu, Dapeng Liu, Bryan Kian Hsiang Low, Patrick Jaillet:
Convolutional Normalizing Flows for Deep Gaussian Processes. CoRR abs/2104.08472 (2021) - [i55]Quoc Phong Nguyen, Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet:
Value-at-Risk Optimization with Gaussian Processes. CoRR abs/2105.06126 (2021) - [i54]Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang, Vahab S. Mirrokni:
Bidding and Pricing in Budget and ROI Constrained Markets. CoRR abs/2107.07725 (2021) - [i53]Quoc Phong Nguyen, Zhaoxuan Wu, Bryan Kian Hsiang Low, Patrick Jaillet:
Trusted-Maximizers Entropy Search for Efficient Bayesian Optimization. CoRR abs/2107.14465 (2021) - [i52]Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet:
Differentially Private Federated Bayesian Optimization with Distributed Exploration. CoRR abs/2110.14153 (2021) - [i51]Tien Mai, Patrick Jaillet:
Robust Entropy-regularized Markov Decision Processes. CoRR abs/2112.15364 (2021) - 2020
- [j54]Julia Gaudio, Patrick Jaillet:
An improved lower bound for the Traveling Salesman constant. Oper. Res. Lett. 48(1): 67-70 (2020) - [j53]Anatolii Prokhorchuk, Justin Dauwels, Patrick Jaillet:
Estimating Travel Time Distributions by Bayesian Network Inference. IEEE Trans. Intell. Transp. Syst. 21(5): 1867-1876 (2020) - [c62]Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet:
Competitive Ratios for Online Multi-capacity Ridesharing. AAMAS 2020: 771-779 - [c61]Konstantina Mellou, Luke Marshall, Krishna Chintalapudi, Patrick Jaillet, Ishai Menache:
Optimizing Onsite Food Services at Scale. SIGSPATIAL/GIS 2020: 618-629 - [c60]Zhongxiang Dai, Yizhou Chen, Bryan Kian Hsiang Low, Patrick Jaillet, Teck-Hua Ho:
R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games. ICML 2020: 2291-2301 - [c59]Trong Nghia Hoang, Thanh Lam, Bryan Kian Hsiang Low, Patrick Jaillet:
Learning Task-Agnostic Embedding of Multiple Black-Box Experts for Multi-Task Model Fusion. ICML 2020: 4282-4292 - [c58]Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet:
Federated Bayesian Optimization via Thompson Sampling. NeurIPS 2020 - [c57]Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang:
No-regret Learning in Price Competitions under Consumer Reference Effects. NeurIPS 2020 - [c56]Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet:
Variational Bayesian Unlearning. NeurIPS 2020 - [i50]Zhongxiang Dai, Yizhou Chen, Kian Hsiang Low, Patrick Jaillet, Teck-Hua Ho:
R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games. CoRR abs/2006.16679 (2020) - [i49]Tien Mai, Patrick Jaillet:
A Relation Analysis of Markov Decision Process Frameworks. CoRR abs/2008.07820 (2020) - [i48]Youssef M. Aboutaleb, Moshe E. Ben-Akiva, Patrick Jaillet:
Learning Structure in Nested Logit Models. CoRR abs/2008.08048 (2020) - [i47]Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet:
Zone pAth Construction (ZAC) based Approaches for Effective Real-Time Ridesharing. CoRR abs/2009.06051 (2020) - [i46]Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet:
Competitive Ratios for Online Multi-capacity Ridesharing. CoRR abs/2009.07925 (2020) - [i45]Zhongxiang Dai, Kian Hsiang Low, Patrick Jaillet:
Federated Bayesian Optimization via Thompson Sampling. CoRR abs/2010.10154 (2020) - [i44]Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet:
Variational Bayesian Unlearning. CoRR abs/2010.12883 (2020) - [i43]Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang:
No-regret Learning in Price Competitions under Consumer Reference Effects. CoRR abs/2011.03653 (2020) - [i42]Quoc Phong Nguyen, Sebastian Tay, Bryan Kian Hsiang Low, Patrick Jaillet:
Top-k Ranking Bayesian Optimization. CoRR abs/2012.10688 (2020) - [i41]Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet:
An Information-Theoretic Framework for Unifying Active Learning Problems. CoRR abs/2012.10695 (2020)
2010 – 2019
- 2019
- [j52]Dimitris Bertsimas, Patrick Jaillet, Nikita Korolko:
The K-server problem via a modern optimization lens. Eur. J. Oper. Res. 276(1): 65-78 (2019) - [j51]Thibaut Vidal, Daniel Gribel, Patrick Jaillet:
Separable Convex Optimization with Nested Lower and Upper Constraints. INFORMS J. Optim. 1(1): 71-90 (2019) - [j50]Dimitris Bertsimas, Patrick Jaillet, Sébastien Martin:
Online Vehicle Routing: The Edge of Optimization in Large-Scale Applications. Oper. Res. 67(1): 143-162 (2019) - [j49]Dimitris Bertsimas, Arthur Delarue, Patrick Jaillet, Sébastien Martin:
Travel Time Estimation in the Age of Big Data. Oper. Res. 67(2): 498-515 (2019) - [j48]Itai Ashlagi, Maximilien Burq, Patrick Jaillet, Vahideh H. Manshadi:
On Matching and Thickness in Heterogeneous Dynamic Markets. Oper. Res. 67(4): 927-949 (2019) - [j47]Julia Gaudio, Saurabh Amin, Patrick Jaillet:
Exponential convergence rates for stochastically ordered Markov processes under perturbation. Syst. Control. Lett. 133 (2019) - [c55]Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet:
ZAC: A Zone Path Construction Approach for Effective Real-Time Ridesharing. ICAPS 2019: 528-538 - [c54]Itai Ashlagi, Maximilien Burq, Chinmoy Dutta, Patrick Jaillet, Amin Saberi, Chris Sholley:
Edge Weighted Online Windowed Matching. EC 2019: 729-742 - [c53]Jinhong K. Guo, Alexander Karlovitz, Patrick Jaillet, Martin O. Hofmann:
The Price of Anarchy: Centralized versus Distributed Resource Allocation Trade-offs. ICAART (1) 2019: 146-153 - [c52]Zhongxiang Dai, Haibin Yu, Bryan Kian Hsiang Low, Patrick Jaillet:
Bayesian Optimization Meets Bayesian Optimal Stopping. ICML 2019: 1496-1506 - [c51]Supriyo Ghosh, Jing Yu Koh, Patrick Jaillet:
Improving Customer Satisfaction in Bike Sharing Systems through Dynamic Repositioning. IJCAI 2019: 5864-5870 - [c50]Haibin Yu, Trong Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet:
Stochastic Variational Inference for Bayesian Sparse Gaussian Process Regression. IJCNN 2019: 1-8 - [c49]Haibin Yu, Yizhou Chen, Bryan Kian Hsiang Low, Patrick Jaillet, Zhongxiang Dai:
Implicit Posterior Variational Inference for Deep Gaussian Processes. NeurIPS 2019: 14475-14486 - [i40]Dimitris Bertsimas, Arthur Delarue, Patrick Jaillet, Sébastien Martin:
The Price of Interpretability. CoRR abs/1907.03419 (2019) - [i39]Dimitris Bertsimas, Arthur Delarue, Patrick Jaillet, Sébastien Martin:
Optimal Explanations of Linear Models. CoRR abs/1907.04669 (2019) - [i38]