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.
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.
On the one hand, the continuous action space using percentage changes in prices is preferred for generalization.
Constrained Reinforcement Learning (CRL) burgeons broad interest in recent years, which pursues both goals of maximizing long-term returns and constraining costs.
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.
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.
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.
A chatbot that converses like a human should be goal-oriented (i. e., be purposeful in conversation), which is beyond language generation.
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
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.
We propose Meta-Embedding, a meta-learning-based approach that learns to generate desirable initial embeddings for new ad IDs.
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.
We evaluate PGCR on toy datasets as well as a real-world dataset of personalized music recommendations.