Causal Deep Reinforcement Learning using Observational Data

Deep reinforcement learning (DRL) requires the collection of plenty of interventional data, which is sometimes expensive and even unethical in the real world, such as in the autonomous driving and the medical field. Offline reinforcement learning promises to alleviate this issue by exploiting the vast amount of observational data available in the real world. However, observational data may mislead the learning agent to undesirable outcomes if the behavior policy that generates the data depends on unobserved random variables (i.e., confounders). In this paper, we propose two deconfounding methods in DRL to address this problem. The methods first calculate the importance degree of different samples based on the causal inference technique, and then adjust the impact of different samples on the loss function by reweighting or resampling the offline dataset to ensure its unbiasedness. These deconfounding methods can be flexibly combined with the existing model-free DRL algorithms such as soft actor-critic and deep Q-learning, provided that a weak condition can be satisfied by the loss functions of these algorithms. We prove the effectiveness of our deconfounding methods and validate them experimentally

Face Alignment Assisted by Head Pose Estimation

In this paper we propose a supervised initialization scheme for cascaded face alignment based on explicit head pose estimation. We first investigate the failure cases of most state of the art face alignment approaches and observe that these failures often share one common global property, i.e. the head pose variation is usually large. Inspired by this, we propose a deep convolutional network model for reliable and accurate head pose estimation. Instead of using a mean face shape, or randomly selected shapes for cascaded face alignment initialisation, we propose two schemes for generating initialisation: the first one relies on projecting a mean 3D face shape (represented by 3D facial landmarks) onto 2D image under the estimated head pose; the second one searches nearest neighbour shapes from the training set according to head pose distance. By doing so, the initialisation gets closer to the actual shape, which enhances the possibility of convergence and in turn improves the face alignment performance. We demonstrate the proposed method on the benchmark 300W dataset and show very competitive performance in both head pose estimation and face alignment.Comment: Accepted by BMVC201

Responsible Task Automation: Empowering Large Language Models as Responsible Task Automators

The recent success of Large Language Models (LLMs) signifies an impressive stride towards artificial general intelligence. They have shown a promising prospect in automatically completing tasks upon user instructions, functioning as brain-like coordinators. The associated risks will be revealed as we delegate an increasing number of tasks to machines for automated completion. A big question emerges: how can we make machines behave responsibly when helping humans automate tasks as personal copilots? In this paper, we explore this question in depth from the perspectives of feasibility, completeness and security. In specific, we present Responsible Task Automation (ResponsibleTA) as a fundamental framework to facilitate responsible collaboration between LLM-based coordinators and executors for task automation with three empowered capabilities: 1) predicting the feasibility of the commands for executors; 2) verifying the completeness of executors; 3) enhancing the security (e.g., the protection of users' privacy). We further propose and compare two paradigms for implementing the first two capabilities. One is to leverage the generic knowledge of LLMs themselves via prompt engineering while the other is to adopt domain-specific learnable models. Moreover, we introduce a local memory mechanism for achieving the third capability. We evaluate our proposed ResponsibleTA on UI task automation and hope it could bring more attentions to ensuring LLMs more responsible in diverse scenarios. The research project homepage is at https://task-automation-research.github.io/responsible_task_automation

Oportunidades y dependencia. Evolución del cambio técnico en la minería cupríferachilena, 1900-1950

Màster Oficial d'Història Econòmica, Facultat d'Economia i Empresa, Universitat de Barcelona, Curs: 2020-2021, Tutor: Marc Badía MiróDurante la primera mitad del siglo XX, la minería del cobre chilena vivió transformaciones significativas en su inserción en los mercados internacionales, lo cual fue empujado por el aumento de la producción basado en la explotación a gran escala de las grandes minas. El cambio técnico jugó un papel imprescindible en estos avances tanto para hacer explotables los minerales en vetas de menor ley, como para diversificar el tipo productos metalúrgicos transformados, aunque eso fuera a cambio de genera una dependencia extranjera importante. Este trabajo se dedicará a revisar la evolución del cambio técnico y entender el doble impacto de las nuevas técnicas en la extracción del metal rojo, por medio de relacionar el uso de maquinaria con la evolución de la producción y exportación minería cuprífera a medio-largo plazo

The burden of attention:CEO publicity and tax avoidance

We use search volume index (SVI) for a CEO’s name and stock ticker from Google Trends to measure CEO publicity, and examine the competing hypotheses on its relation to tax avoidance. On the one hand, CEOs who receive more attention from retail investors may engage in tax evasion activities to meet investors’ performance expectations; on the other hand, they are more concerned with public image and avoiding being labeled as tax avoiders. Based on the CEOs of S&P 500 firms between 2004 and 2011, our finding supports the former and shows that CEOs with higher publicity manage to have a lower effective tax rate and cash effective tax rate. Such effect is moderated by board independence. Finally, firms with higher CEO publicity pay auditors higher tax fees, suggesting that these CEOs tend to use more tax planning services from auditors