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Paul Weng
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2020 – today
- 2024
- [j13]Claire Glanois, Paul Weng
, Matthieu Zimmer, Dong Li, Tianpei Yang, Jianye Hao, Wulong Liu:
A survey on interpretable reinforcement learning. Mach. Learn. 113(8): 5847-5890 (2024) - [j12]Wenbin Ouyang, Yisen Wang, Paul Weng
Generalization in Deep RL for TSP Problems via Equivariance and Local Search. SN Comput. Sci. 5(4): 369 (2024) - [c52]Jianshu Hu
Revisiting Data Augmentation in Deep Reinforcement Learning. ICLR 2024 - [c51]Han Fang, Zhihao Song, Paul Weng, Yutong Ban:
INViT: A Generalizable Routing Problem Solver with Invariant Nested View Transformer. ICML 2024 - [i35]Han Fang, Zhihao Song, Paul Weng, Yutong Ban:
INViT: A Generalizable Routing Problem Solver with Invariant Nested View Transformer. CoRR abs/2402.02317 (2024) - [i34]Zhaohui Jiang, Paul Weng:
Unsupervised Salient Patch Selection for Data-Efficient Reinforcement Learning. CoRR abs/2402.03329 (2024) - [i33]Jianshu Hu, Yunpeng Jiang, Paul Weng:
Revisiting Data Augmentation in Deep Reinforcement Learning. CoRR abs/2402.12181 (2024) - [i32]Yunpeng Jiang, Paul Weng, Yutong Ban:
Enhancing Class Fairness in Classification with A Two-Player Game Approach. CoRR abs/2407.03146 (2024) - [i31]Jianshu Hu, Paul Weng, Yutong Ban:
State-Novelty Guided Action Persistence in Deep Reinforcement Learning. CoRR abs/2409.05433 (2024) - [i30]Zhaohui Jiang, Xuening Feng, Paul Weng, Yifei Zhu, Yan Song, Tianze Zhou, Yujing Hu, Tangjie Lv, Changjie Fan:
Reinforcement Learning From Imperfect Corrective Actions And Proxy Rewards. CoRR abs/2410.05782 (2024) - 2023
- [j11]Ruibin Bai
Analytics and machine learning in vehicle routing research. Int. J. Prod. Res. 61(1): 4-30 (2023) - [j10]Matthieu Zimmer, Xuening Feng, Claire Glanois, Zhaohui Jiang, Jianyi Zhang, Paul Weng, Dong Li, Jianye Hao, Wulong Liu:
Differentiable Logic Machines. Trans. Mach. Learn. Res. 2023 (2023) - [c50]Guanbao Yu, Umer Siddique, Paul Weng
Fair Deep Reinforcement Learning with Generalized Gini Welfare Functions. AAMAS Workshops 2023: 3-29 - [c49]Junqi Qian, Paul Weng, Chenmien Tan:
Learning Rewards to Optimize Global Performance Metrics in Deep Reinforcement Learning. AAMAS 2023: 1951-1960 - [c48]Guanbao Yu, Umer Siddique, Paul Weng
Fair Deep Reinforcement Learning with Preferential Treatment. ECAI 2023: 2922-2929 - [c47]Thi Quynh Trang Vo, Mourad Baïou, Viet Hung Nguyen, Paul Weng:
Improving Subtour Elimination Constraint Generation in Branch-and-Cut Algorithms for the TSP with Machine Learning. LION 2023: 537-551 - [c46]Zhaohui Jiang
Unsupervised Salient Patch Selection for Data-Efficient Reinforcement Learning. ECML/PKDD (4) 2023: 556-572 - [i29]Junqi Qian, Paul Weng, Chenmien Tan:
Learning Rewards to Optimize Global Performance Metrics in Deep Reinforcement Learning. CoRR abs/2303.09027 (2023) - [i28]Timo Kaufmann, Paul Weng, Viktor Bengs, Eyke Hüllermeier:
A Survey of Reinforcement Learning from Human Feedback. CoRR abs/2312.14925 (2023) - 2022
- [c45]Chenmien Tan, Paul Weng:
CVaR-Regret Bounds for Multi-armed Bandits. ACML 2022: 974-989 - [c44]Jianshu Hu
Solving Complex Manipulation Tasks with Model-Assisted Model-Free Reinforcement Learning. CoRL 2022: 1299-1308 - [c43]Hejun Lei, Paul Weng, Juan Rojas
Planning with Q-Values in Sparse Reward Reinforcement Learning. ICIRA (1) 2022: 603-614 - [c42]Claire Glanois, Zhaohui Jiang, Xuening Feng, Paul Weng, Matthieu Zimmer, Dong Li, Wulong Liu, Jianye Hao:
Neuro-Symbolic Hierarchical Rule Induction. ICML 2022: 7583-7615 - [c41]Thi Quynh Trang Vo, Mourad Baïou, Viet Hung Nguyen, Paul Weng:
A comparative study of linearization methods for Ordered Weighted Average. RNDM 2022: 1-7 - 2021
- [j9]Jiancong Huang
Hyperparameter Auto-Tuning in Self-Supervised Robotic Learning. IEEE Robotics Autom. Lett. 6(2): 3537-3544 (2021) - [c40]Jianyi Zhang, Paul Weng:
Safe Distributional Reinforcement Learning. DAI 2021: 107-128 - [c39]Matthieu Zimmer, Claire Glanois, Umer Siddique, Paul Weng:
Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement Learning. ICML 2021: 12967-12978 - [c38]Wenbin Ouyang, Yisen Wang, Shaochen Han, Zhejian Jin, Paul Weng:
Improving Generalization of Deep Reinforcement Learning-based TSP Solvers. SSCI 2021: 1-8 - [i27]Ruibin Bai, Xinan Chen, Zhi-Long Chen, Tianxiang Cui, Shuhui Gong, Wentao He, Xiaoping Jiang, Huan Jin, Jiahuan Jin, Graham Kendall, Jiawei Li, Zheng Lu, Jianfeng Ren, Paul Weng, Ning Xue, Huayan Zhang:
Analytics and Machine Learning in Vehicle Routing Research. CoRR abs/2102.10012 (2021) - [i26]Matthieu Zimmer, Xuening Feng, Claire Glanois
Differentiable Logic Machines. CoRR abs/2102.11529 (2021) - [i25]Jianyi Zhang, Paul Weng:
Safe Distributional Reinforcement Learning. CoRR abs/2102.13446 (2021) - [i24]Zhihao Ma, Yuzheng Zhuang, Paul Weng, Hankz Hankui Zhuo, Dong Li, Wulong Liu, Jianye Hao:
Learning Symbolic Rules for Interpretable Deep Reinforcement Learning. CoRR abs/2103.08228 (2021) - [i23]Wenbin Ouyang, Yisen Wang, Shaochen Han, Zhejian Jin, Paul Weng:
Improving Generalization of Deep Reinforcement Learning-based TSP Solvers. CoRR abs/2110.02843 (2021) - [i22]Wenbin Ouyang, Yisen Wang, Paul Weng, Shaochen Han:
Generalization in Deep RL for TSP Problems via Equivariance and Local Search. CoRR abs/2110.03595 (2021) - [i21]Claire Glanois, Paul Weng, Matthieu Zimmer, Dong Li, Tianpei Yang, Jianye Hao, Wulong Liu:
A Survey on Interpretable Reinforcement Learning. CoRR abs/2112.13112 (2021) - [i20]Claire Glanois, Xuening Feng, Zhaohui Jiang, Paul Weng, Matthieu Zimmer, Dong Li, Wulong Liu:
Neuro-Symbolic Hierarchical Rule Induction. CoRR abs/2112.13418 (2021) - 2020
- [j8]Yijiong Lin
Invariant Transform Experience Replay: Data Augmentation for Deep Reinforcement Learning. IEEE Robotics Autom. Lett. 5(4): 6615-6622 (2020) - [c37]Yinzhao Dong, Chao Yu, Paul Weng, Ahmed Maustafa, Hui Cheng, Hongwei Ge:
Decomposed Deep Reinforcement Learning for Robotic Control. AAMAS 2020: 1834-1836 - [c36]Umer Siddique, Paul Weng, Matthieu Zimmer
Learning Fair Policies in Multi-Objective (Deep) Reinforcement Learning with Average and Discounted Rewards. ICML 2020: 8905-8915 - [p1]Olivier Buffet, Olivier Pietquin, Paul Weng:
Reinforcement Learning. A Guided Tour of Artificial Intelligence Research (1) (I) 2020: 389-414 - [i19]Olivier Buffer, Olivier Pietquin, Paul Weng:
Reinforcement Learning. CoRR abs/2005.14419 (2020) - [i18]Umer Siddique, Paul Weng, Matthieu Zimmer:
Learning Fair Policies in Multiobjective (Deep) Reinforcement Learning with Average and Discounted Rewards. CoRR abs/2008.07773 (2020) - [i17]Jiancong Huang, Juan Rojas, Matthieu Zimmer, Hongmin Wu, Yisheng Guan, Paul Weng:
Hyperparameter Auto-tuning in Self-Supervised Robotic Learning. CoRR abs/2010.08252 (2020) - [i16]Matthieu Zimmer, Umer Siddique, Paul Weng:
Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement Learning. CoRR abs/2012.09421 (2020)
2010 – 2019
- 2019
- [c35]Matthieu Zimmer, Paul Weng:
An efficient reinforcement learning algorithm for learning deterministic policies in continuous domains. DAI 2019: 4:1-4:7 - [c34]Matthieu Zimmer
Exploiting the Sign of the Advantage Function to Learn Deterministic Policies in Continuous Domains. IJCAI 2019: 4496-4502 - [c33]Qitian Wu, Yirui Gao, Xiaofeng Gao, Paul Weng, Guihai Chen
Dual Sequential Prediction Models Linking Sequential Recommendation and Information Dissemination. KDD 2019: 447-457 - [c32]Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Peng He, Paul Weng, Han Gao, Guihai Chen
Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems. WWW 2019: 2091-2102 - [i15]Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Peng He, Paul Weng, Han Gao, Guihai Chen:
Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems. CoRR abs/1903.10433 (2019) - [i14]Matthieu Zimmer, Paul Weng:
Exploiting the Sign of the Advantage Function to Learn Deterministic Policies in Continuous Domains. CoRR abs/1906.04556 (2019) - [i13]Paul Weng:
Fairness in Reinforcement Learning. CoRR abs/1907.10323 (2019) - [i12]Yijiong Lin, Jiancong Huang, Matthieu Zimmer, Juan Rojas, Paul Weng:
Invariant Transform Experience Replay. CoRR abs/1909.10707 (2019) - [i11]Yijiong Lin, Jiancong Huang, Matthieu Zimmer, Juan Rojas, Paul Weng:
Towards More Sample Efficiency in Reinforcement Learning with Data Augmentation. CoRR abs/1910.09959 (2019) - 2018
- [j7]M. Hadi Amini, Paul McNamara, Paul Weng, Orkun Karabasoglu
Hierarchical Electric Vehicle Charging Aggregator Strategy Using Dantzig-Wolfe Decomposition. IEEE Des. Test 35(6): 25-36 (2018) - [c31]Qitian Wu, Chaoqi Yang, Hengrui Zhang, Xiaofeng Gao, Paul Weng, Guihai Chen
Adversarial Training Model Unifying Feature Driven and Point Process Perspectives for Event Popularity Prediction. CIKM 2018: 517-526 - [c30]Emmanuel Hadoux, Aurélie Beynier, Nicolas Maudet, Paul Weng:
Mediation of Debates with Dynamic Argumentative Behaviors. COMMA 2018: 249-256 - [c29]Marc T. Law, Paul Weng:
Representing Relative Visual Attributes with a Reference-Point-Based Decision Model. ICPR 2018: 435-440 - [i10]Viet Hung Nguyen, Paul Weng:
An Efficient Primal-Dual Algorithm for Fair Combinatorial Optimization Problems. CoRR abs/1801.07544 (2018) - 2017
- [j6]Paul Weng
Functional Reward Markov Decision Processes: Theory and Applications. Int. J. Artif. Intell. Tools 26(3): 1760014:1-1760014:20 (2017) - [c28]Hugo Gilbert, Paul Weng, Yan Xu:
Optimizing Quantiles in Preference-Based Markov Decision Processes. AAAI 2017: 3569-3575 - [c27]Viet Hung Nguyen, Paul Weng:
An Efficient Primal-Dual Algorithm for Fair Combinatorial Optimization Problems. COCOA (1) 2017: 324-339 - [c26]Róbert Busa-Fekete, Balázs Szörényi, Paul Weng, Shie Mannor:
Multi-objective Bandits: Optimizing the Generalized Gini Index. ICML 2017: 625-634 - [i9]Dajian Li, Paul Weng, Orkun Karabasoglu:
Finding Risk-Averse Shortest Path with Time-dependent Stochastic Costs. CoRR abs/1701.00642 (2017) - [i8]Paul Weng:
From Preference-Based to Multiobjective Sequential Decision-Making. CoRR abs/1701.00646 (2017) - [i7]Róbert Busa-Fekete, Balázs Szörényi, Paul Weng, Shie Mannor:
Multi-objective Bandits: Optimizing the Generalized Gini Index. CoRR abs/1706.04933 (2017) - [i6]Paul Weng, Zeqi Qiu, John A. W. B. Costanzo, Xiaoqi Yin, Bruno Sinopoli:
Optimal Threshold Policies for Robust Data Center Control. CoRR abs/1708.07036 (2017) - 2016
- [c25]Dajian Li, Paul Weng, Orkun Karabasoglu
Finding Risk-Averse Shortest Path with Time-Dependent Stochastic Costs. MIWAI 2016: 99-111 - [c24]Paul Weng:
From Preference-Based to Multiobjective Sequential Decision-Making. MIWAI 2016: 231-242 - [c23]Hugo Gilbert, Bruno Zanuttini, Paul Weng, Paolo Viappiani, Esther Nicart:
Model-Free Reinforcement Learning with Skew-Symmetric Bilinear Utilities. UAI 2016 - [i5]Hugo Gilbert, Paul Weng:
Quantile Reinforcement Learning. CoRR abs/1611.00862 (2016) - [i4]Hugo Gilbert, Paul Weng, Yan Xu:
Optimizing Quantiles in Preference-based Markov Decision Processes. CoRR abs/1612.00094 (2016) - 2015
- [j5]Stefano V. Albrecht, André da Motta Salles Barreto, Darius Braziunas, David L. Buckeridge, Heriberto Cuayáhuitl, Nina Dethlefs, Markus Endres
Reports of the AAAI 2014 Conference Workshops. AI Mag. 36(1): 87-98 (2015) - [c22]Hugo Gilbert, Olivier Spanjaard, Paolo Viappiani
Reducing the Number of Queries in Interactive Value Iteration. ADT 2015: 139-152 - [c21]Balázs Szörényi, Róbert Busa-Fekete, Paul Weng, Eyke Hüllermeier:
Qualitative Multi-Armed Bandits: A Quantile-Based Approach. ICML 2015: 1660-1668 - [c20]Hugo Gilbert, Olivier Spanjaard, Paolo Viappiani, Paul Weng:
Solving MDPs with Skew Symmetric Bilinear Utility Functions. IJCAI 2015: 1989-1995 - [c19]Emmanuel Hadoux, Aurélie Beynier, Nicolas Maudet, Paul Weng, Anthony Hunter:
Optimization of Probabilistic Argumentation with Markov Decision Models. IJCAI 2015: 2004-2010 - 2014
- [j4]Róbert Busa-Fekete, Balázs Szörényi, Paul Weng, Weiwei Cheng, Eyke Hüllermeier:
Preference-based reinforcement learning: evolutionary direct policy search using a preference-based racing algorithm. Mach. Learn. 97(3): 327-351 (2014) - [c18]Darius Braziunas, Markus Endres, Kristen Brent Venable, Paul Weng, Lirong Xia:
Preface. MPREF@AAAI 2014 - [c17]Emmanuel Hadoux
Solving Hidden-Semi-Markov-Mode Markov Decision Problems. SUM 2014: 176-189 - [e2]Darius Braziunas, Markus Endres, Kristen Brent Venable, Paul Weng, Lirong Xia:
Multidisciplinary Workshop on Advances in Preference Handling, Papers from the 2014 AAAI Workshop, MPREF@AAAI, July 28, 2014, Quebec City, Canada. AAAI Technical Report WS-14-10, AAAI Press 2014, ISBN 978-1-57735-671-4 [contents] - [e1]M. Narasimha Murty, Xiangjian He
Multi-disciplinary Trends in Artificial Intelligence - 8th International Workshop, MIWAI 2014, Bangalore, India, December 8-10, 2014. Proceedings. Lecture Notes in Computer Science 8875, Springer 2014, ISBN 978-3-319-13364-5 [contents] - 2013
- [j3]Wlodzimierz Ogryczak
A Compromise Programming Approach to multiobjective Markov Decision Processes. Int. J. Inf. Technol. Decis. Mak. 12(5): 1021-1054 (2013) - [c16]Patrice Perny, Paul Weng, Judy Goldsmith, Josiah Hanna:
Approximation of Lorenz-Optimal Solutions in Multiobjective Markov Decision Processes. AAAI (Late-Breaking Developments) 2013 - [c15]Róbert Busa-Fekete, Balázs Szörényi, Weiwei Cheng, Paul Weng, Eyke Hüllermeier:
Top-k Selection based on Adaptive Sampling of Noisy Preferences. ICML (3) 2013: 1094-1102 - [c14]Paul Weng, Bruno Zanuttini:
Interactive Value Iteration for Markov Decision Processes with Unknown Rewards. IJCAI 2013: 2415-2421 - [c13]Olivier Spanjaard, Paul Weng:
Markov Decision Processes with Functional Rewards. MIWAI 2013: 269-280 - [c12]Paul Weng:
Axiomatic Foundations of Generalized Qualitative Utility. MIWAI 2013: 305-316 - [c11]Patrice Perny, Paul Weng, Judy Goldsmith, Josiah Hanna:
Approximation of Lorenz-Optimal Solutions in Multiobjective Markov Decision Processes. UAI 2013 - [i3]Patrice Perny, Paul Weng, Judy Goldsmith, Josiah Hanna:
Approximation of Lorenz-Optimal Solutions in Multiobjective Markov Decision Processes. CoRR abs/1309.6856 (2013) - 2012
- [c10]Paul Weng:
Ordinal Decision Models for Markov Decision Processes. ECAI 2012: 828-833 - [c9]Wlodzimierz Ogryczak
On WOWA Rank Reversal. MDAI 2012: 66-77 - [i2]Paul Weng:
Axiomatic Foundations for a Class of Generalized Expected Utility: Algebraic Expected Utility. CoRR abs/1206.6867 (2012) - [i1]Paul Weng:
Qualitative Decision Making Under Possibilistic Uncertainty: Toward more discriminating criteria. CoRR abs/1207.1425 (2012) - 2011
- [c8]Paul Weng:
Markov Decision Processes with Ordinal Rewards: Reference Point-Based Preferences. ICAPS 2011 - [c7]Charles Delort, Olivier Spanjaard, Paul Weng:
Committee Selection with a Weight Constraint Based on a Pairwise Dominance Relation. ADT 2011: 28-41 - [c6]Wlodzimierz Ogryczak
On Minimizing Ordered Weighted Regrets in Multiobjective Markov Decision Processes. ADT 2011: 190-204 - 2010
- [c5]Patrice Perny, Paul Weng:
On Finding Compromise Solutions in Multiobjective Markov Decision Processes. ECAI 2010: 969-970
2000 – 2009
- 2007
- [j2]Paul Weng:
Conditions générales pour l'admissibilité de la programmation dynamique dans la décision séquentielle possibiliste. Rev. d'Intelligence Artif. 21(1): 129-143 (2007) - 2006
- [j1]Paul Weng:
Processus de décision markoviens et préférences non classiques. Rev. d'Intelligence Artif. 20(2-3): 411-432 (2006) - [c4]Paul Weng:
An Axiomatic Approach in Qualitative Decision Theory with Binary Possibilistic Utility. ECAI 2006: 467-471 - [c3]Paul Weng:
Axiomatic Foundations for a Class of Generalized Expected Utility: Algebraic Expected Utility. UAI 2006 - 2005
- [c2]Patrice Perny, Olivier Spanjaard, Paul Weng:
Algebraic Markov Decision Processes. IJCAI 2005: 1372-1377 - [c1]Paul Weng:
Qualitative Decision Making Under Possibilistic Uncertainty: Toward more Discriminating Criteria. UAI 2005: 615-622
Coauthor Index
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last updated on 2024-11-19 20:45 CET by the dblp team
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