1 code implementation • 1 Apr 2017 • Yihui He, Xiaobo Ma, Xiapu Luo, Jianfeng Li, Mengchen Zhao, Bo An, Xiaohong Guan
Security surveillance is one of the most important issues in smart cities, especially in an era of terrorism.
no code implementations • NAACL 2018 • Bo An, Bo Chen, Xianpei Han, Le Sun
Previous representation learning techniques for knowledge graph representation usually represent the same entity or relation in different triples with the same representation, without considering the ambiguity of relations and entities.
no code implementations • COLING 2018 • Bo An, Xianpei Han, Le Sun
Word composition is a promising technique for representation learning of large linguistic units (e. g., phrases, sentences and documents).
no code implementations • COLING 2018 • Bo Chen, Bo An, Le Sun, Xianpei Han
Semantic parsers critically rely on accurate and high-coverage lexicons.
no code implementations • ACL 2016 • Bo Chen, Le Sun, Xianpei Han, Bo An
A major challenge of semantic parsing is the vocabulary mismatch problem between natural language and target ontology.
no code implementations • 8 Feb 2019 • Lei Feng, Bo An, Shuo He
It is well-known that exploiting label correlations is crucially important to multi-label learning.
no code implementations • 8 Feb 2019 • Lei Feng, Bo An
We show that optimizing this convex-concave problem is equivalent to solving a set of quadratic programming (QP) problems.
no code implementations • 3 Mar 2019 • Jiang Rong, Tao Qin, Bo An
Second, based on the analysis of the impact of other players' unknown cards on one's final rewards, we design two neural networks to deal with imperfect information, the first one inferring the cards of the partner and the second one taking the outputs of the first one as part of its input to select a bid.
no code implementations • NeurIPS 2019 • Jiarui Gan, Qingyu Guo, Long Tran-Thanh, Bo An, Michael Wooldridge
We then apply a game-theoretic framework at a higher level to counteract such manipulation, in which the defender commits to a policy that specifies her strategy commitment according to the learned information.
no code implementations • IJCNLP 2019 • Bo An, Chen Bo, Xianpei Han, Le Sun
Semantic parsing aims to map natural language utterances into structured meaning representations.
no code implementations • ICML 2020 • Rundong Wang, Xu He, Runsheng Yu, Wei Qiu, Bo An, Zinovi Rabinovich
Under the limited bandwidth constraint, a communication protocol is required to generate informative messages.
no code implementations • 18 Nov 2019 • Runsheng Yu, Zhenyu Shi, Xinrun Wang, Rundong Wang, Buhong Liu, Xinwen Hou, Hanjiang Lai, Bo An
Existing value-factorized based Multi-Agent deep Reinforce-ment Learning (MARL) approaches are well-performing invarious multi-agent cooperative environment under thecen-tralized training and decentralized execution(CTDE) scheme, where all agents are trained together by the centralized valuenetwork and each agent execute its policy independently.
no code implementations • ICML 2020 • Lei Feng, Takuo Kaneko, Bo Han, Gang Niu, Bo An, Masashi Sugiyama
In this paper, we propose a novel problem setting to allow MCLs for each example and two ways for learning with MCLs.
2 code implementations • CVPR 2020 • Hongxin Wei, Lei Feng, Xiangyu Chen, Bo An
The state-of-the-art approaches "Decoupling" and "Co-teaching+" claim that the "disagreement" strategy is crucial for alleviating the problem of learning with noisy labels.
Ranked #10 on Learning with noisy labels on CIFAR-10N-Random3
no code implementations • ICLR 2020 • Zhenyu Shi*, Runsheng Yu*, Xinrun Wang*, Rundong Wang, Youzhi Zhang, Hanjiang Lai, Bo An
The main difficulties of expensive coordination are that i) the leader has to consider the long-term effect and predict the followers' behaviors when assigning bonuses and ii) the complex interactions between followers make the training process hard to converge, especially when the leader's policy changes with time.
1 code implementation • 7 Jun 2020 • Xu He, Haipeng Chen, Bo An
However, previous works rarely consider the uncertainty when humans provide feedback, especially in cases that the optimal actions are not obvious to the trainers.
no code implementations • NeurIPS 2020 • Lei Feng, Jiaqi Lv, Bo Han, Miao Xu, Gang Niu, Xin Geng, Bo An, Masashi Sugiyama
Partial-label learning (PLL) is a multi-class classification problem, where each training example is associated with a set of candidate labels.
no code implementations • 21 Aug 2020 • Xu He, Bo An, Yanghua Li, Haikai Chen, Qingyu Guo, Xin Li, Zhirong Wang
First, since we concern the reward of a set of recommended items, we model the online recommendation as a contextual combinatorial bandit problem and define the reward of a recommended set.
no code implementations • 21 Aug 2020 • Xu He, Bo An, Yanghua Li, Haikai Chen, Rundong Wang, Xinrun Wang, Runsheng Yu, Xin Li, Zhirong Wang
Thus, the global policy of the whole page could be sub-optimal.
Multi-agent Reinforcement Learning Reinforcement Learning (RL)
no code implementations • 30 Sep 2020 • David Milec, Jakub Černý, Viliam Lisý, Bo An
This paper aims to analyze and propose scalable algorithms for computing effective and robust strategies against a quantal opponent in normal-form and extensive-form games.
no code implementations • 5 Oct 2020 • Lei Feng, Senlin Shu, Nan Lu, Bo Han, Miao Xu, Gang Niu, Bo An, Masashi Sugiyama
To alleviate the data requirement for training effective binary classifiers in binary classification, many weakly supervised learning settings have been proposed.
1 code implementation • COLING 2020 • Rong Zhang, Qifei Zhou, Bo An, Weiping Li, Tong Mo, Bo Wu
2) There is no previous work considering adversarial attack to improve the performance of NLSM tasks.
no code implementations • 2 Dec 2020 • Zhuowei Wang, Jing Jiang, Bo Han, Lei Feng, Bo An, Gang Niu, Guodong Long
We also instantiate our framework with different combinations, which set the new state of the art on benchmark-simulated and real-world datasets with noisy labels.
no code implementations • 7 Dec 2020 • Yan Li, Bo An, Junming Ma, Donggang Cao, Yasha Wang, Hong Mei
Hyper-parameter tuning (HPT) is crucial for many machine learning (ML) algorithms.
no code implementations • 7 Dec 2020 • Xinrun Wang, Tarun Nair, Haoyang Li, Yuh Sheng Reuben Wong, Nachiket Kelkar, Srinivas Vaidyanathan, Rajat Nayak, Bo An, Jagdish Krishnaswamy, Milind Tambe
Dams impact downstream river dynamics through flow regulation and disruption of upstream-downstream linkages.
no code implementations • 9 Dec 2020 • Hongxin Wei, Lei Feng, Rundong Wang, Bo An
Deep neural networks have been shown to easily overfit to biased training data with label noise or class imbalance.
no code implementations • 22 Dec 2020 • Runsheng Yu, Yu Gong, Xu He, Bo An, Yu Zhu, Qingwen Liu, Wenwu Ou
Recently, many existing studies regard the cold-start personalized preference prediction as a few-shot learning problem, where each user is the task and recommended items are the classes, and the gradient-based meta learning method (MAML) is leveraged to address this challenge.
no code implementations • 23 Dec 2020 • Rundong Wang, Hongxin Wei, Bo An, Zhouyan Feng, Jun Yao
Portfolio management via reinforcement learning is at the forefront of fintech research, which explores how to optimally reallocate a fund into different financial assets over the long term by trial-and-error.
no code implementations • 1 Jan 2021 • Wei Qiu, Xinrun Wang, Runsheng Yu, Xu He, Rundong Wang, Bo An, Svetlana Obraztsova, Zinovi Rabinovich
Centralized training with decentralized execution (CTDE) has become an important paradigm in multi-agent reinforcement learning (MARL).
Multi-agent Reinforcement Learning reinforcement-learning +3
no code implementations • 8 Jan 2021 • Runsheng Yu, Yu Gong, Rundong Wang, Bo An, Qingwen Liu, Wenwu Ou
Firstly, we introduce a novel training scheme with two value functions to maximize the accumulated long-term reward under the safety constraint.
no code implementations • 13 Feb 2021 • Yuzhou Cao, Lei Feng, Yitian Xu, Bo An, Gang Niu, Masashi Sugiyama
Weakly supervised learning has drawn considerable attention recently to reduce the expensive time and labor consumption of labeling massive data.
no code implementations • 16 Feb 2021 • Wei Qiu, Xinrun Wang, Runsheng Yu, Xu He, Rundong Wang, Bo An, Svetlana Obraztsova, Zinovi Rabinovich
Current value-based multi-agent reinforcement learning methods optimize individual Q values to guide individuals' behaviours via centralized training with decentralized execution (CTDE).
Multi-agent Reinforcement Learning reinforcement-learning +3
no code implementations • CVPR 2022 • Aye Phyu Phyu Aung, Xinrun Wang, Runsheng Yu, Bo An, Senthilnath Jayavelu, XiaoLi Li
In this paper, we propose a new approach to train Generative Adversarial Networks (GANs) where we deploy a double-oracle framework using the generator and discriminator oracles.
no code implementations • 18 Feb 2021 • Zhe Wu, Kai Li, Enmin Zhao, Hang Xu, Meng Zhang, Haobo Fu, Bo An, Junliang Xing
In this work, we propose a novel Learning to Exploit (L2E) framework for implicit opponent modeling.
no code implementations • 18 May 2021 • Shuxin Li, Youzhi Zhang, Xinrun Wang, Wanqi Xue, Bo An
The challenge of solving this type of game is that the team's joint action space grows exponentially with the number of agents, which results in the inefficiency of the existing algorithms, e. g., Counterfactual Regret Minimization (CFR).
no code implementations • 2 Jun 2021 • Wanqi Xue, Youzhi Zhang, Shuxin Li, Xinrun Wang, Bo An, Chai Kiat Yeo
Securing networked infrastructures is important in the real world.
no code implementations • 11 Jun 2021 • Jiaqi Lv, Biao Liu, Lei Feng, Ning Xu, Miao Xu, Bo An, Gang Niu, Xin Geng, Masashi Sugiyama
Partial-label learning (PLL) utilizes instances with PLs, where a PL includes several candidate labels but only one is the true label (TL).
1 code implementation • 13 Jun 2021 • Haipeng Chen, Wei Qiu, Han-Ching Ou, Bo An, Milind Tambe
Empirical results show that our method achieves influence as high as the state-of-the-art methods for contingency-aware IM, while having negligible runtime at test phase.
no code implementations • 16 Jun 2021 • Yuzhou Cao, Lei Feng, Senlin Shu, Yitian Xu, Bo An, Gang Niu, Masashi Sugiyama
We show that without any assumptions on the loss functions, models, and optimizers, we can successfully learn a multi-class classifier from only data of a single class with a rigorous consistency guarantee when confidences (i. e., the class-posterior probabilities for all the classes) are available.
4 code implementations • NeurIPS 2021 • Hongxin Wei, Lue Tao, Renchunzi Xie, Bo An
Learning with noisy labels is a practically challenging problem in weakly supervised learning.
no code implementations • 9 Aug 2021 • Wanqi Xue, Wei Qiu, Bo An, Zinovi Rabinovich, Svetlana Obraztsova, Chai Kiat Yeo
Empirical results demonstrate that many state-of-the-art MACRL methods are vulnerable to message attacks, and our method can significantly improve their robustness.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 28 Sep 2021 • Shuo Sun, Rundong Wang, Bo An
RL's impact is pervasive, recently demonstrating its ability to conquer many challenging QT tasks.
no code implementations • 29 Sep 2021 • Pengjie Gu, Mengchen Zhao, Chen Chen, Dong Li, Jianye Hao, Bo An
Offline reinforcement learning is a promising approach for practical applications since it does not require interactions with real-world environments.
no code implementations • ICLR 2022 • Pengjie Gu, Mengchen Zhao, Jianye Hao, Bo An
Autonomous agents often need to work together as a team to accomplish complex cooperative tasks.
no code implementations • 29 Sep 2021 • Hongxin Wei, Lue Tao, Renchunzi Xie, Lei Feng, Bo An
Deep neural networks usually perform poorly when the training dataset suffers from extreme class imbalance.
no code implementations • NeurIPS 2021 • Wei Qiu, Xinrun Wang, Runsheng Yu, Rundong Wang, Xu He, Bo An, Svetlana Obraztsova, Zinovi Rabinovich
Current value-based multi-agent reinforcement learning methods optimize individual Q values to guide individuals' behaviours via centralized training with decentralized execution (CTDE).
Multi-agent Reinforcement Learning reinforcement-learning +3
1 code implementation • 8 Dec 2021 • Yanchen Deng, Shufeng Kong, Bo An
Our model, GAT-PCM, is then pretrained with optimally labelled data in an offline manner, so as to construct effective heuristics to boost a broad range of DCOP algorithms where evaluating the quality of a partial assignment is critical, such as local search or backtracking search.
no code implementations • 15 Dec 2021 • Shuo Sun, Wanqi Xue, Rundong Wang, Xu He, Junlei Zhu, Jian Li, Bo An
Reinforcement learning (RL) techniques have shown great success in many challenging quantitative trading tasks, such as portfolio management and algorithmic trading.
3 code implementations • 16 Jan 2022 • Renchunzi Xie, Hongxin Wei, Lei Feng, Bo An
Although there have been a few studies on this problem, most of them only exploit unidirectional relationships from the source domain to the target domain.
no code implementations • 17 Jan 2022 • Wanqi Xue, Bo An, Chai Kiat Yeo
Second, we enable neural MCTS with decentralized control, making NSGZero applicable to NSGs with many resources.
2 code implementations • 19 May 2022 • Hongxin Wei, Renchunzi Xie, Hao Cheng, Lei Feng, Bo An, Yixuan Li
Our method is motivated by the analysis that the norm of the logit keeps increasing during training, leading to overconfident output.
no code implementations • 27 May 2022 • Wei Qiu, Weixun Wang, Rundong Wang, Bo An, Yujing Hu, Svetlana Obraztsova, Zinovi Rabinovich, Jianye Hao, Yingfeng Chen, Changjie Fan
During execution durations, the environment changes are influenced by, but not synchronised with, action execution.
Multi-agent Reinforcement Learning reinforcement-learning +3
1 code implementation • 1 Jun 2022 • Wanqi Xue, Qingpeng Cai, Ruohan Zhan, Dong Zheng, Peng Jiang, Kun Gai, Bo An
Meanwhile, reinforcement learning (RL) is widely regarded as a promising framework for optimizing long-term engagement in sequential recommendation.
no code implementations • 7 Jun 2022 • Shuo Sun, Rundong Wang, Bo An
To tackle these two limitations, we first reformulate quantitative investment as a multi-task learning problem.
3 code implementations • 17 Jun 2022 • Hongxin Wei, Lue Tao, Renchunzi Xie, Lei Feng, Bo An
Deep neural networks usually perform poorly when the training dataset suffers from extreme class imbalance.
1 code implementation • 12 Jul 2022 • Shuxin Li, Xinrun Wang, Youzhi Zhang, Jakub Cerny, Pengdeng Li, Hau Chan, Bo An
Extensive experimental results demonstrate the superiority of our approach over offline RL algorithms and the importance of using model-based methods for OEF problems.
no code implementations • 24 Sep 2022 • Yanchen Deng, Shufeng Kong, Caihua Liu, Bo An
Belief Propagation (BP) is an important message-passing algorithm for various reasoning tasks over graphical models, including solving the Constraint Optimization Problems (COPs).
no code implementations • 18 Oct 2022 • Wei Qiu, Xiao Ma, Bo An, Svetlana Obraztsova, Shuicheng Yan, Zhongwen Xu
Despite the recent advancement in multi-agent reinforcement learning (MARL), the MARL agents easily overfit the training environment and perform poorly in the evaluation scenarios where other agents behave differently.
Multi-agent Reinforcement Learning reinforcement-learning +1
1 code implementation • 24 Oct 2022 • Shijie Han, Siyuan Li, Bo An, Wei Zhao, Peng Liu
In this work, we develop a novel identity detection reinforcement learning (IDRL) framework that allows an agent to dynamically infer the identities of nearby agents and select an appropriate policy to accomplish the task.
Multi-agent Reinforcement Learning reinforcement-learning +2
2 code implementations • Conference 2022 • Yuzhou Cao, Tianchi Cai, Lei Feng, Lihong Gu, Jinjie Gu, Bo An, Gang Niu, Masashi Sugiyama
\emph{Classification with rejection} (CwR) refrains from making a prediction to avoid critical misclassification when encountering test samples that are difficult to classify.
1 code implementation • 6 Dec 2022 • Wanqi Xue, Qingpeng Cai, Zhenghai Xue, Shuo Sun, Shuchang Liu, Dong Zheng, Peng Jiang, Kun Gai, Bo An
Though promising, the application of RL heavily relies on well-designed rewards, but designing rewards related to long-term user engagement is quite difficult.
no code implementations • 8 Dec 2022 • Hongxin Wei, Huiping Zhuang, Renchunzi Xie, Lei Feng, Gang Niu, Bo An, Yixuan Li
In the presence of noisy labels, designing robust loss functions is critical for securing the generalization performance of deep neural networks.
no code implementations • 14 Jan 2023 • Shuo Sun, Molei Qin, Xinrun Wang, Bo An
Specifically, i) we propose AlphaMix+ as a strong FinRL baseline, which leverages mixture-of-experts (MoE) and risk-sensitive approaches to make diversified risk-aware investment decisions, ii) we evaluate 8 FinRL methods in 4 long-term real-world datasets of influential financial markets to demonstrate the usage of our PRUDEX-Compass, iii) PRUDEX-Compass together with 4 real-world datasets, standard implementation of 8 FinRL methods and a portfolio management environment is released as public resources to facilitate the design and comparison of new FinRL methods.
no code implementations • 27 Jan 2023 • Wanqi Xue, Bo An, Shuicheng Yan, Zhongwen Xu
The complexity of designing reward functions has been a major obstacle to the wide application of deep reinforcement learning (RL) techniques.
no code implementations • 7 Feb 2023 • Rundong Wang, Longtao Zheng, Wei Qiu, Bowei He, Bo An, Zinovi Rabinovich, Yujing Hu, Yingfeng Chen, Tangjie Lv, Changjie Fan
Despite its success, ACL's applicability is limited by (1) the lack of a general student framework for dealing with the varying number of agents across tasks and the sparse reward problem, and (2) the non-stationarity of the teacher's task due to ever-changing student strategies.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 7 Feb 2023 • Pengdeng Li, Xinrun Wang, Shuxin Li, Hau Chan, Bo An
In this work, we attempt to bridge the two fields of finite-agent and infinite-agent games, by studying how the optimal policies of agents evolve with the number of agents (population size) in mean-field games, an agent-centric perspective in contrast to the existing works focusing typically on the convergence of the empirical distribution of the population.
no code implementations • NeurIPS 2023 • Zhenghai Xue, Qingpeng Cai, Shuchang Liu, Dong Zheng, Peng Jiang, Kun Gai, Bo An
Data with dynamics shift are separated according to their environment parameters to train the corresponding policy.
1 code implementation • 13 Jun 2023 • Longtao Zheng, Rundong Wang, Xinrun Wang, Bo An
To address these challenges, we introduce Synapse, a computer agent featuring three key components: i) state abstraction, which filters out task-irrelevant information from raw states, allowing more exemplars within the limited context, ii) trajectory-as-exemplar prompting, which prompts the LLM with complete trajectories of the abstracted states and actions to improve multi-step decision-making, and iii) exemplar memory, which stores the embeddings of exemplars and retrieves them via similarity search for generalization to novel tasks.
1 code implementation • AAAI 2023 • Xin Cheng, Deng-Bao Wang, Lei Feng, Min-Ling Zhang, Bo An
Our proposed methods are theoretically grounded and can be compatible with any models, optimizers, and losses.
no code implementations • 18 Jun 2023 • Xin Cheng, Yuzhou Cao, Ximing Li, Bo An, Lei Feng
Third, we propose a statistically consistent limiting method for RIT to train the model by limiting the predictions to the interval.
no code implementations • 17 Aug 2023 • Hui Niu, Siyuan Li, Jiahao Zheng, Zhouchi Lin, Jian Li, Jian Guo, Bo An
Market making (MM) has attracted significant attention in financial trading owing to its essential function in ensuring market liquidity.
no code implementations • 22 Aug 2023 • Linjian Meng, Zhenxing Ge, Wenbin Li, Bo An, Yang Gao
Recent works propose a Reward Transformation (RT) framework for MWU, which removes the uniqueness condition and achieves competitive performance with OMWU.
no code implementations • 14 Sep 2023 • Haochong Xia, Shuo Sun, Xinrun Wang, Bo An
Financial simulators play an important role in enhancing forecasting accuracy, managing risks, and fostering strategic financial decision-making.
1 code implementation • 22 Sep 2023 • Molei Qin, Shuo Sun, Wentao Zhang, Haochong Xia, Xinrun Wang, Bo An
In stage II, we construct a pool of diverse RL agents for different market trends, distinguished by return rates, where hundreds of RL agents are trained with different preferences of return rates and only a tiny fraction of them will be selected into the pool based on their profitability.
no code implementations • 6 Oct 2023 • Zhenghai Xue, Qingpeng Cai, Tianyou Zuo, Bin Yang, Lantao Hu, Peng Jiang, Kun Gai, Bo An
One challenge in large-scale online recommendation systems is the constant and complicated changes in users' behavior patterns, such as interaction rates and retention tendencies.
1 code implementation • 17 Nov 2023 • Wentao Zhang, Yilei Zhao, Shuo Sun, Jie Ying, Yonggang Xie, Zitao Song, Xinrun Wang, Bo An
Specifically, the target stock pool of different investors varies dramatically due to their discrepancy on market states and individual investors may temporally adjust stocks they desire to trade (e. g., adding one popular stocks), which lead to customizable stock pools (CSPs).
no code implementations • 31 Dec 2023 • Chaojie Wang, Yishi Xu, Zhong Peng, Chenxi Zhang, Bo Chen, Xinrun Wang, Lei Feng, Bo An
Large language models (LLMs) have exhibited remarkable performance on various natural language processing (NLP) tasks, especially for question answering.
no code implementations • 17 Jan 2024 • Renchunzi Xie, Ambroise Odonnat, Vasilii Feofanov, Ievgen Redko, Jianfeng Zhang, Bo An
Our key idea is that the model should be adjusted with a higher magnitude of gradients when it does not generalize to the test dataset with a distribution shift.
1 code implementation • 24 Jan 2024 • Qi Wei, Lei Feng, Haobo Wang, Bo An
To address this limitation, we propose a noIse-Tolerant Expert Model (ITEM) for debiased learning in sample selection.
1 code implementation • 25 Jan 2024 • Weihao Tan, Wentao Zhang, Shanqi Liu, Longtao Zheng, Xinrun Wang, Bo An
Despite the impressive performance across numerous tasks, large language models (LLMs) often fail in solving simple decision-making tasks due to the misalignment of the knowledge in LLMs with environments.
no code implementations • 28 Feb 2024 • Wentao Zhang, Lingxuan Zhao, Haochong Xia, Shuo Sun, Jiaze Sun, Molei Qin, Xinyi Li, Yuqing Zhao, Yilei Zhao, Xinyu Cai, Longtao Zheng, Xinrun Wang, Bo An
Notably, FinAgent is the first advanced multimodal foundation agent designed for financial trading tasks.
2 code implementations • 5 Mar 2024 • Weihao Tan, Ziluo Ding, Wentao Zhang, Boyu Li, Bohan Zhou, Junpeng Yue, Haochong Xia, Jiechuan Jiang, Longtao Zheng, Xinrun Xu, Yifei Bi, Pengjie Gu, Xinrun Wang, Börje F. Karlsson, Bo An, Zongqing Lu
Despite the success in specific tasks and scenarios, existing foundation agents, empowered by large models (LMs) and advanced tools, still cannot generalize to different scenarios, mainly due to dramatic differences in the observations and actions across scenarios.
no code implementations • 26 Mar 2024 • Longtao Zheng, Zhiyuan Huang, Zhenghai Xue, Xinrun Wang, Bo An, Shuicheng Yan
We have open-sourced the environments, datasets, benchmarks, and interfaces to promote research towards developing general virtual agents for the future.
no code implementations • ICML 2020 • Youzhi Zhang, Bo An
Second, we design an ISG variant for TMEs (ISGT) by exploiting that a TME is an NE maximizing the team’s utility and show that ISGT converges to a TME and the impossibility of relaxing conditions in ISGT.