Search Results for author: Feiyang Pan

Found 13 papers, 4 papers with code

Generating Synergistic Formulaic Alpha Collections via Reinforcement Learning

1 code implementation25 May 2023 Shuo Yu, Hongyan Xue, Xiang Ao, Feiyang Pan, Jia He, Dandan Tu, Qing He

In practice, a set of formulaic alphas is often used together for better modeling precision, so we need to find synergistic formulaic alpha sets that work well together.

reinforcement-learning Reinforcement Learning (RL)

Style Miner: Find Significant and Stable Explanatory Factors in Time Series with Constrained Reinforcement Learning

no code implementations21 Mar 2023 Dapeng Li, Feiyang Pan, Jia He, Zhiwei Xu, Dandan Tu, Guoliang Fan

In high-dimensional time-series analysis, it is essential to have a set of key factors (namely, the style factors) that explain the change of the observed variable.

Time Series Time Series Analysis

Rethinking Pareto Approaches in Constrained Reinforcement Learning

no code implementations29 Sep 2021 Mengda Huang, Feiyang Pan, Jia He, Xiang Ao, Qing He

Constrained Reinforcement Learning (CRL) burgeons broad interest in recent years, which pursues both goals of maximizing long-term returns and constraining costs.

reinforcement-learning Reinforcement Learning (RL)

Follow the Prophet: Accurate Online Conversion Rate Prediction in the Face of Delayed Feedback

1 code implementation13 Aug 2021 Haoming Li, Feiyang Pan, Xiang Ao, Zhao Yang, Min Lu, Junwei Pan, Dapeng Liu, Lei Xiao, Qing He

The delayed feedback problem is one of the imperative challenges in online advertising, which is caused by the highly diversified feedback delay of a conversion varying from a few minutes to several days.

GuideBoot: Guided Bootstrap for Deep Contextual Bandits

no code implementations18 Jul 2021 Feiyang Pan, Haoming Li, Xiang Ao, Wei Wang, Yanrong Kang, Ao Tan, Qing He

The proposed method is efficient as it can make decisions on-the-fly by utilizing only one randomly chosen model, but is also effective as we show that it can be viewed as a non-Bayesian approximation of Thompson sampling.

Multi-Armed Bandits Thompson Sampling

GoChat: Goal-oriented Chatbots with Hierarchical Reinforcement Learning

no code implementations24 May 2020 Jianfeng Liu, Feiyang Pan, Ling Luo

A chatbot that converses like a human should be goal-oriented (i. e., be purposeful in conversation), which is beyond language generation.

Chatbot Hierarchical Reinforcement Learning +4

Field-aware Calibration: A Simple and Empirically Strong Method for Reliable Probabilistic Predictions

no code implementations26 May 2019 Feiyang Pan, Xiang Ao, Pingzhong Tang, Min Lu, Dapeng Liu, Lei Xiao, Qing He

It is often observed that the probabilistic predictions given by a machine learning model can disagree with averaged actual outcomes on specific subsets of data, which is also known as the issue of miscalibration.

BIG-bench Machine Learning Click-Through Rate Prediction

Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings

1 code implementation25 Apr 2019 Feiyang Pan, Shuokai Li, Xiang Ao, Pingzhong Tang, Qing He

We propose Meta-Embedding, a meta-learning-based approach that learns to generate desirable initial embeddings for new ad IDs.

Click-Through Rate Prediction Meta-Learning

Policy Optimization with Model-based Explorations

no code implementations18 Nov 2018 Feiyang Pan, Qingpeng Cai, An-Xiang Zeng, Chun-Xiang Pan, Qing Da, Hua-Lin He, Qing He, Pingzhong Tang

Model-free reinforcement learning methods such as the Proximal Policy Optimization algorithm (PPO) have successfully applied in complex decision-making problems such as Atari games.

Atari Games Decision Making +3

Policy Gradients for Contextual Recommendations

no code implementations12 Feb 2018 Feiyang Pan, Qingpeng Cai, Pingzhong Tang, Fuzhen Zhuang, Qing He

We evaluate PGCR on toy datasets as well as a real-world dataset of personalized music recommendations.

Decision Making Multi-Armed Bandits +2

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