no code implementations • 19 Mar 2024 • Baoyu Jing, Yansen Wang, Guoxin Sui, Jing Hong, Jingrui He, Yuqing Yang, Dongsheng Li, Kan Ren
In recent years, Contrastive Learning (CL) has become a predominant representation learning paradigm for time series.
no code implementations • 18 Mar 2024 • Baoyu Jing, Dawei Zhou, Kan Ren, Carl Yang
Based on the results of the frontdoor adjustment, we introduce a novel Causality-Aware SPatiotEmpoRal graph neural network (CASPER), which contains a novel Spatiotemporal Causal Attention (SCA) and a Prompt Based Decoder (PBD).
1 code implementation • 27 Feb 2024 • Yuge Zhang, Qiyang Jiang, Xingyu Han, Nan Chen, Yuqing Yang, Kan Ren
In this paper, we introduce DSEval -- a novel evaluation paradigm, as well as a series of innovative benchmarks tailored for assessing the performance of these agents throughout the entire data science lifecycle.
1 code implementation • NeurIPS 2023 • Yuqi Chen, Kan Ren, Yansen Wang, Yuchen Fang, Weiwei Sun, Dongsheng Li
A wide range of experiments on both synthetic and real-world datasets have illustrated the superior modeling capacities and prediction performance of ContiFormer on irregular time series data.
no code implementations • 11 Jan 2024 • Yuqi Chen, Kan Ren, Kaitao Song, Yansen Wang, Yifan Wang, Dongsheng Li, Lili Qiu
Self-supervised learning has emerged as a highly effective approach in the fields of natural language processing and computer vision.
1 code implementation • 30 Nov 2023 • Yongliang Shen, Kaitao Song, Xu Tan, Wenqi Zhang, Kan Ren, Siyu Yuan, Weiming Lu, Dongsheng Li, Yueting Zhuang
To this end, we introduce TaskBench to evaluate the capability of LLMs in task automation.
no code implementations • 27 Jul 2023 • Yu-Ting Lan, Kan Ren, Yansen Wang, Wei-Long Zheng, Dongsheng Li, Bao-liang Lu, Lili Qiu
Seeing is believing, however, the underlying mechanism of how human visual perceptions are intertwined with our cognitions is still a mystery.
no code implementations • 6 Jul 2023 • Yuchen Fang, Zhenggang Tang, Kan Ren, Weiqing Liu, Li Zhao, Jiang Bian, Dongsheng Li, Weinan Zhang, Yong Yu, Tie-Yan Liu
Order execution is a fundamental task in quantitative finance, aiming at finishing acquisition or liquidation for a number of trading orders of the specific assets.
no code implementations • 2 Jul 2023 • Ruiwen Zhou, Minghuan Liu, Kan Ren, Xufang Luo, Weinan Zhang, Dongsheng Li
Due to the nature of risk management in learning applicable policies, risk-sensitive reinforcement learning (RSRL) has been realized as an important direction.
no code implementations • 2 Jul 2023 • Ziyue Li, Yuchen Fang, You Li, Kan Ren, Yansen Wang, Xufang Luo, Juanyong Duan, Congrui Huang, Dongsheng Li, Lili Qiu
A timely detection of seizures for newborn infants with electroencephalogram (EEG) has been a common yet life-saving practice in the Neonatal Intensive Care Unit (NICU).
1 code implementation • International Conference on Learning Representations 2023 • Ziyue Li, Kan Ren, Xinyang Jiang, Yifei Shen, Haipeng Zhang, Dongsheng Li
Moreover, our method is highly efficient and achieves more than 1000 times training speedup compared to the conventional DG methods with fine-tuning a pretrained model.
Ranked #1 on Domain Generalization on PACS
1 code implementation • 28 Apr 2023 • Lei Zhang, Yuge Zhang, Kan Ren, Dongsheng Li, Yuqing Yang
In contrast, though human engineers have the incredible ability to understand tasks and reason about solutions, their experience and knowledge are often sparse and difficult to utilize by quantitative approaches.
no code implementations • 18 Apr 2023 • Yang Liu, Shen Yan, Yuge Zhang, Kan Ren, Quanlu Zhang, Zebin Ren, Deng Cai, Mi Zhang
Vision Transformers have shown great performance in single tasks such as classification and segmentation.
no code implementations • 29 Jan 2023 • Ziyue Li, Kan Ren, Yifan Yang, Xinyang Jiang, Yuqing Yang, Dongsheng Li
Ensemble methods can deliver surprising performance gains but also bring significantly higher computational costs, e. g., can be up to 2048X in large-scale ensemble tasks.
no code implementations • 12 Oct 2022 • Tairan He, Yuge Zhang, Kan Ren, Minghuan Liu, Che Wang, Weinan Zhang, Yuqing Yang, Dongsheng Li
A good state representation is crucial to solving complicated reinforcement learning (RL) challenges.
no code implementations • 17 Jun 2022 • Kerong Wang, Hanye Zhao, Xufang Luo, Kan Ren, Weinan Zhang, Dongsheng Li
Offline reinforcement learning (RL) aims at learning policies from previously collected static trajectory data without interacting with the real environment.
no code implementations • 19 May 2022 • Zhengyu Yang, Kan Ren, Xufang Luo, Minghuan Liu, Weiqing Liu, Jiang Bian, Weinan Zhang, Dongsheng Li
Considering the great performance of ensemble methods on both accuracy and generalization in supervised learning (SL), we design a robust and applicable method named Ensemble Proximal Policy Optimization (EPPO), which learns ensemble policies in an end-to-end manner.
no code implementations • 9 Mar 2022 • Ziyue Li, Kan Ren, Xinyang Jiang, Bo Li, Haipeng Zhang, Dongsheng Li
Fine-tuning pretrained models is a common practice in domain generalization (DG) tasks.
Ranked #8 on Domain Generalization on TerraIncognita
1 code implementation • 16 Nov 2021 • Hengzhi Pei, Kan Ren, Yuqing Yang, Chang Liu, Tao Qin, Dongsheng Li
In this paper, we propose a novel generative framework for RTS data - RTSGAN to tackle the aforementioned challenges.
no code implementations • 29 Sep 2021 • Zhengyu Yang, Kan Ren, Xufang Luo, Weiqing Liu, Jiang Bian, Weinan Zhang, Dongsheng Li
Ensemble learning, which can consistently improve the prediction performance in supervised learning, has drawn increasing attentions in reinforcement learning (RL).
no code implementations • 29 Sep 2021 • Ziyue Li, Kan Ren, Xinyang Jiang, Mingzhe Han, Haipeng Zhang, Dongsheng Li
Real-world data is often generated by some complex distribution, which can be approximated by a composition of multiple simpler distributions.
no code implementations • 29 Sep 2021 • Tairan He, Yuge Zhang, Kan Ren, Che Wang, Weinan Zhang, Dongsheng Li, Yuqing Yang
A good state representation is crucial to reinforcement learning (RL) while an ideal representation is hard to learn only with signals from the RL objective.
no code implementations • 28 Jan 2021 • Yuchen Fang, Kan Ren, Weiqing Liu, Dong Zhou, Weinan Zhang, Jiang Bian, Yong Yu, Tie-Yan Liu
As a fundamental problem in algorithmic trading, order execution aims at fulfilling a specific trading order, either liquidation or acquirement, for a given instrument.
no code implementations • 11 Oct 2020 • Chao Ma, Guohua Gu, Xin Miao, Minjie Wan, Weixian Qian, Kan Ren, Qian Chen
Infrared target tracking plays an important role in both civil and military fields.
no code implementations • 18 Jun 2020 • Sijin Zhou, Xinyi Dai, Haokun Chen, Wei-Nan Zhang, Kan Ren, Ruiming Tang, Xiuqiang He, Yong Yu
Interactive recommender system (IRS) has drawn huge attention because of its flexible recommendation strategy and the consideration of optimal long-term user experiences.
1 code implementation • 30 Apr 2020 • Jiarui Jin, Yuchen Fang, Wei-Nan Zhang, Kan Ren, Guorui Zhou, Jian Xu, Yong Yu, Jun Wang, Xiaoqiang Zhu, Kun Gai
Position bias is a critical problem in information retrieval when dealing with implicit yet biased user feedback data.
1 code implementation • 10 Nov 2019 • Jiarui Qin, Kan Ren, Yuchen Fang, Wei-Nan Zhang, Yong Yu
Various sequential recommendation methods are proposed to model the dynamic user behaviors.
2 code implementations • 7 May 2019 • Kan Ren, Jiarui Qin, Lei Zheng, Zhengyu Yang, Wei-Nan Zhang, Yong Yu
The problem is formulated as to forecast the probability distribution of market price for each ad auction.
1 code implementation • 2 May 2019 • Kan Ren, Jiarui Qin, Yuchen Fang, Wei-Nan Zhang, Lei Zheng, Weijie Bian, Guorui Zhou, Jian Xu, Yong Yu, Xiaoqiang Zhu, Kun Gai
In order to tackle these challenges, in this paper, we propose a Hierarchical Periodic Memory Network for lifelong sequential modeling with personalized memorization of sequential patterns for each user.
no code implementations • 15 Nov 2018 • Guansong Lu, Zhiming Zhou, Yuxuan Song, Kan Ren, Yong Yu
CycleGAN is capable of learning a one-to-one mapping between two data distributions without paired examples, achieving the task of unsupervised data translation.
1 code implementation • 7 Sep 2018 • Kan Ren, Jiarui Qin, Lei Zheng, Zhengyu Yang, Wei-Nan Zhang, Lin Qiu, Yong Yu
By capturing the time dependency through modeling the conditional probability of the event for each sample, our method predicts the likelihood of the true event occurrence and estimates the survival rate over time, i. e., the probability of the non-occurrence of the event, for the censored data.
1 code implementation • 11 Aug 2018 • Kan Ren, Yuchen Fang, Wei-Nan Zhang, Shuhao Liu, Jiajun Li, Ya zhang, Yong Yu, Jun Wang
To achieve this, we utilize sequence-to-sequence prediction for user clicks, and combine both post-view and post-click attribution patterns together for the final conversion estimation.
no code implementations • 1 Mar 2018 • Kan Ren, Wei-Nan Zhang, Ke Chang, Yifei Rong, Yong Yu, Jun Wang
From the learning perspective, we show that the bidding machine can be updated smoothly with both offline periodical batch or online sequential training schemes.
2 code implementations • ICLR 2018 • Zhiming Zhou, Han Cai, Shu Rong, Yuxuan Song, Kan Ren, Wei-Nan Zhang, Yong Yu, Jun Wang
Our proposed model also outperforms the baseline methods in the new metric.
1 code implementation • 10 Jan 2017 • Han Cai, Kan Ren, Wei-Nan Zhang, Kleanthis Malialis, Jun Wang, Yong Yu, Defeng Guo
In this paper, we formulate the bid decision process as a reinforcement learning problem, where the state space is represented by the auction information and the campaign's real-time parameters, while an action is the bid price to set.
11 code implementations • 1 Nov 2016 • Yanru Qu, Han Cai, Kan Ren, Wei-Nan Zhang, Yong Yu, Ying Wen, Jun Wang
Predicting user responses, such as clicks and conversions, is of great importance and has found its usage in many Web applications including recommender systems, web search and online advertising.
Ranked #1 on Click-Through Rate Prediction on iPinYou
no code implementations • 16 Oct 2015 • Chenhao Zhu, Kan Ren, Xuan Liu, Haofen Wang, Yiding Tian, Yong Yu
We present a question answering system over DBpedia, filling the gap between user information needs expressed in natural language and a structured query interface expressed in SPARQL over the underlying knowledge base (KB).