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Found 13 papers, 4 papers with code
Generating Synergistic Formulaic Alpha Collections via Reinforcement Learning
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.
Style Miner: Find Significant and Stable Explanatory Factors in Time Series with Constrained Reinforcement Learning
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.
Learn Continuously, Act Discretely: Hybrid Action-Space Reinforcement Learning For Optimal Execution
On the one hand, the continuous action space using percentage changes in prices is preferred for generalization.
Rethinking Pareto Approaches in Constrained Reinforcement Learning
Constrained Reinforcement Learning (CRL) burgeons broad interest in recent years, which pursues both goals of maximizing long-term returns and constraining costs.
Follow the Prophet: Accurate Online Conversion Rate Prediction in the Face of Delayed Feedback
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
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.
Trust the Model When It Is Confident: Masked Model-based Actor-Critic
In complex and noisy settings, model-based RL tends to have trouble using the model if it does not know when to trust the model.
GoChat: Goal-oriented Chatbots with Hierarchical Reinforcement Learning
A chatbot that converses like a human should be goal-oriented (i. e., be purposeful in conversation), which is beyond language generation.
Reading Like HER: Human Reading Inspired Extractive Summarization
In this work, we re-examine the problem of extractive text summarization for long documents.
Ranked #8 on
Extractive Text Summarization
on CNN / Daily Mail
Field-aware Calibration: A Simple and Empirically Strong Method for Reliable Probabilistic Predictions
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.
Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings
We propose Meta-Embedding, a meta-learning-based approach that learns to generate desirable initial embeddings for new ad IDs.
Policy Optimization with Model-based Explorations
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.
Policy Gradients for Contextual Recommendations
We evaluate PGCR on toy datasets as well as a real-world dataset of personalized music recommendations.
