Quantile Copula Causal Discovery (QCCD) google
Telling cause from effect using observational data is a challenging problem, especially in the bivariate case. Contemporary methods often assume an independence between the cause and the generating mechanism of the effect given the cause. From this postulate, they derive asymmetries to uncover causal relationships. In this work, we propose such an approach, based on the link between Kolmogorov complexity and quantile scoring. We use a nonparametric conditional quantile estimator based on copulas to implement our procedure, thus avoiding restrictive assumptions about the joint distribution between cause and effect. In an extensive study on real and synthetic data, we show that quantile copula causal discovery (QCCD) compares favorably to state-of-the-art methods, while at the same time being computationally efficient and scalable. …

Map-Based Multi-Policy Reinforcement Learning (MMPRL) google
In order for robots to perform mission-critical tasks, it is essential that they are able to quickly adapt to changes in their environment as well as to injuries and or other bodily changes. Deep reinforcement learning has been shown to be successful in training robot control policies for operation in complex environments. However, existing methods typically employ only a single policy. This can limit the adaptability since a large environmental modification might require a completely different behavior compared to the learning environment. To solve this problem, we propose Map-based Multi-Policy Reinforcement Learning (MMPRL), which aims to search and store multiple policies that encode different behavioral features while maximizing the expected reward in advance of the environment change. Thanks to these policies, which are stored into a multi-dimensional discrete map according to its behavioral feature, adaptation can be performed within reasonable time without retraining the robot. An appropriate pre-trained policy from the map can be recalled using Bayesian optimization. Our experiments show that MMPRL enables robots to quickly adapt to large changes without requiring any prior knowledge on the type of injuries that could occur. A highlight of the learned behaviors can be found here: https://youtu.be/qcCepAKL32U . …

RankLib google
RankLib is a library of learning to rank algorithms. Currently eight popular algorithms have been implemented:
• MART (Multiple Additive Regression Trees, a.k.a. Gradient boosted regression tree)
• RankNet
• RankBoost
• AdaRank
• Coordinate Ascent
• LambdaMART
• ListNet
• Random Forests
• With appropriate parameters for Random Forests, it can also do bagging several MART/LambdaMART rankers.
It also implements many retrieval metrics as well as provides many ways to carry out evaluation. …