Search Results for author: Kan Ren

Found 37 papers, 15 papers with code

Automated Contrastive Learning Strategy Search for Time Series

no code implementations19 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.

AutoML Contrastive Learning +3

CASPER: Causality-Aware Spatiotemporal Graph Neural Networks for Spatiotemporal Time Series Imputation

no code implementations18 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).

Imputation Time Series

Benchmarking Data Science Agents

1 code implementation27 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.

Benchmarking Code Generation +1

ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling

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.

Inductive Bias Irregular Time Series +1

EEGFormer: Towards Transferable and Interpretable Large-Scale EEG Foundation Model

no code implementations11 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.

Anomaly Detection EEG +2

Seeing through the Brain: Image Reconstruction of Visual Perception from Human Brain Signals

no code implementations27 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.

EEG Image Reconstruction +1

Learning Multi-Agent Intention-Aware Communication for Optimal Multi-Order Execution in Finance

no code implementations6 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.

Reinforcement Learning (RL)

Is Risk-Sensitive Reinforcement Learning Properly Resolved?

no code implementations2 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.

Distributional Reinforcement Learning Management +2

Protecting the Future: Neonatal Seizure Detection with Spatial-Temporal Modeling

no code implementations2 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).

EEG Seizure Detection

SIMPLE: Specialized Model-Sample Matching for Domain Generalization

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.

Domain Generalization

MLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning Tasks

1 code implementation28 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.

AutoML Code Generation

Towards Inference Efficient Deep Ensemble Learning

no code implementations29 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.

Ensemble Learning

Bootstrapped Transformer for Offline Reinforcement Learning

no code implementations17 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.

Offline RL reinforcement-learning +1

Towards Applicable Reinforcement Learning: Improving the Generalization and Sample Efficiency with Policy Ensemble

no code implementations19 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.

reinforcement-learning Reinforcement Learning (RL)

Towards Generating Real-World Time Series Data

1 code implementation16 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.

Decoder Generative Adversarial Network +2

AARL: Automated Auxiliary Loss for Reinforcement Learning

no code implementations29 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.

reinforcement-learning Reinforcement Learning (RL)

Deep Ensemble Policy Learning

no code implementations29 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).

Ensemble Learning Reinforcement Learning (RL)

SANE: Specialization-Aware Neural Network Ensemble

no code implementations29 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.

Ensemble Learning

Universal Trading for Order Execution with Oracle Policy Distillation

no code implementations28 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.

Algorithmic Trading reinforcement-learning +1

Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning

no code implementations18 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.

Decision Making Recommendation Systems +3

A Deep Recurrent Survival Model for Unbiased Ranking

1 code implementation30 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.

Information Retrieval Position +2

Deep Landscape Forecasting for Real-time Bidding Advertising

2 code implementations7 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.

Survival Analysis

Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction

1 code implementation2 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.


Guiding the One-to-one Mapping in CycleGAN via Optimal Transport

no code implementations15 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.


Deep Recurrent Survival Analysis

1 code implementation7 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.

Survival Analysis

Learning Multi-touch Conversion Attribution with Dual-attention Mechanisms for Online Advertising

1 code implementation11 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.

Bidding Machine: Learning to Bid for Directly Optimizing Profits in Display Advertising

no code implementations1 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.

BIG-bench Machine Learning

Real-Time Bidding by Reinforcement Learning in Display Advertising

1 code implementation10 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.

reinforcement-learning Reinforcement Learning (RL)

Product-based Neural Networks for User Response Prediction

11 code implementations1 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.

Click-Through Rate Prediction Recommendation Systems

A Graph Traversal Based Approach to Answer Non-Aggregation Questions Over DBpedia

no code implementations16 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).

Question Answering

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