We consider the task of estimating the causal effect of a treatment variable on a long-term outcome variable using data from an observational domain and an experimental domain. The observational data is assumed to be confounded and hence without further assumptions, this dataset alone cannot be used for causal inference. Also, only a short-term version of the primary outcome variable of interest is observed in the experimental data, and hence, this dataset alone cannot be used for causal inference either. In a recent work, Athey et al. (2020) proposed a method for systematically combining such data for identifying the downstream causal effect in view. Their approach is based on the assumptions of internal and external validity of the experimental data, and an extra novel assumption called latent unconfoundedness. In this paper, we first review their proposed approach and discuss the latent unconfoundedness assumption. Then we propose two alternative approaches for data fusion for the purpose of estimating average treatment effect as well as the effect of treatment on the treated. Our first proposed approach is based on assuming equi-confounding bias for the short-term and long-term outcomes. Our second proposed approach is based on the proximal causal inference framework, in which we assume the existence of an extra variable in the system which is a proxy of the latent confounder of the treatment-outcome relation.

Under the theme "Ensuring AI as a Common Good to Transform Education", the 2021 International Forum on Artificial Intelligence (AI) and Education convened policy-makers and practitioners from around the world on 7 and 8 December 2021. The goal was to share knowledge on how governance can be aligned to direct AI towards the common good for education and humanity, and how countries are leveraging AI to deliver the unfulfilled promises and enable the futures of learning. The Forum was co-organized by UNESCO and China with the support of the Inter-UN-Agency Working Group on Artificial Intelligence. It convened approximately 74 speakers including 17 Ministers or Vice Ministers, from UN agencies, international organizations and more than 40 countries around the world. During the two-day event, the Forum attracted more than 9,000 real-time participants and viewers from more than 100 countries.

Jessamine Chan's debut novel, The School for Good Mothers, is not a domestic manual on keeping house. Nor is it the sort of slog that might make tidying look like an appealing alternative. Yet as I read it over the course of one snowy evening, I repeatedly put it down to complete household tasks normally ignored until morning. Every last sock met its match. This book is a horror story so potent it will fill even the most diligent parent with an itchy impulse to panic-clean, to straighten up, to act like someone's watching.

Start today and improve your skills. So you know the theory of Machine Learning and know how to create your first algorithms. There are tons of courses out there about the underlying theory of Machine Learning which don't go any deeper – into the applications. This course is not one of them. Are you ready to apply all of the theory and knowledge to real life Machine Learning challenges?

This is the most complete course online for learning about Python, Data Science, and Machine Learning. Join Jose Portilla's over 2.6 million students to learn about the future today! Welcome to the most complete course on learning Data Science and Machine Learning on the internet! After teaching over 2 million students I've worked for over a year to put together what I believe to be the best way to go from zero to hero for data science and machine learning in Python! This course is designed for the student who already knows some Python and is ready to dive deeper into using those Python skills for Data Science and Machine Learning.

In this work we present a platform to assess robot platform skills using an internet-of-things (IoT) task board device to aggregate performances across remote sites. We demonstrate a concept for a modular, scale-able device and web dashboard enabling remote competitions as an alternative to in-person robot competitions. We share data from nine robot platforms located across four continents in three manipulation task categories of object localization, object insertion, and component disassembly through an organized international robot competition - the Robothon Grand Challenge. This paper discusses the design of an electronic task board, the strategies implemented by the top-performing teams and compares their results with a benchmark solution to the presented task board. Through this platform, we demonstrate fully remote, online competitions can generate innovative robotic solutions and tested a tool for measuring remote performances. Using the open-sourced task board code and design files, the reader can reproduce the benchmark solution or configure the platform for their own use case and share their results transparently without transporting their robot platform.

As the rapid development of Artifical Intelligence in the current technology field, Reinforcement Learning has been proven as a powerful technique that allows autonomous agents to learn optimal behaviors (called policies) in unknown and complex environments through models of rewards and penalization. However, in order to make this technique (Reinforcement Learning) work correctly and get the precise reward function, which returns the feedback to the learning agent about when the agent behaves correctly or not, the reward function needs to be thoroughly specified. As a result, in real-world complex environments, such as autonomous driving, specifying a correct reward function could be one of the hard tasks to tackle for the Reinforcement Learning model designers. To this end, Apprenticeship Learning techniques, in which the agent can infer a reward function from expert behaviors, are of high interest due to the fact that they could result in highly specified reward function efficiently. However, for critical tasks such as autonomous driving, we need to critically consider about the safety-related issues, so as to we need to build techniques to automatically check and ensure that the inferred rewards functions and policies resulted from the Reinforcement Learning model fulfill the needed safety requirements of the critical tasks that we have mentioned previously. In order to have a well-designed Reinforcement Learning model, which is able to generate the highly-specified reward function satisfying the safety-related considerations, the technique called "Safety-Aware Apprenticeship Learning" was built in 2018[23], which would be introduced in detail in the later sections. Although the technique "Safety-Aware Apprenticeship Learning" has been built, it only considers Single-Agent scenario. In the other word, the current "Safety-Aware Apprenticeship Learning" technique can only be applied to one agent running in an isolated environment, a fact which limits the potential implementation of this technique.

Entity alignment aims at integrating heterogeneous knowledge from different knowledge graphs. Recent studies employ embedding-based methods by first learning the representation of Knowledge Graphs and then performing entity alignment via measuring the similarity between entity embeddings. However, they failed to make good use of the relation semantic information due to the trade-off problem caused by the different objectives of learning knowledge embedding and neighborhood consensus. To address this problem, we propose Relational Knowledge Distillation for Entity Alignment (RKDEA), a Graph Convolutional Network (GCN) based model equipped with knowledge distillation for entity alignment. We adopt GCN-based models to learn the representation of entities by considering the graph structure and incorporating the relation semantic information into GCN via knowledge distillation. Then, we introduce a novel adaptive mechanism to transfer relational knowledge so as to jointly learn entity embedding and neighborhood consensus. Experimental results on several benchmarking datasets demonstrate the effectiveness of our proposed model.

State-of-the-art approaches to ObjectGoal navigation Prior work has made good progress on this task by rely on reinforcement learning and typically require significant formulating it as a reinforcement learning (RL) problem computational resources and time for learning. We and developing useful representations [20, 60], auxiliary propose Potential functions for ObjectGoal Navigation with tasks [61], data augmentation techniques [37], and improved Interaction-free learning (PONI), a modular approach that reward functions [37]. Despite this progress, end-toend disentangles the skills of'where to look?' for an object and RL incurs high computational cost, has poor sample efficiency, 'how to navigate to (x, y)?'. Our key insight is that'where and tends to generalize poorly to new scenes [7,12, to look?' can be treated purely as a perception problem, 37] since skills like moving without collisions, exploration, and learned without environment interactions. To address and stopping near the object are all learned from scratch this, we propose a network that predicts two complementary purely using RL. Modular navigation methods aim to address potential functions conditioned on a semantic map and uses these issues by disentangling'where to look for an object?' them to decide where to look for an unseen object. We train and'how to navigate to (x, y)?' [12,36]. These methods the potential function network using supervised learning on have emerged as strong competitors to end-to-end RL a passive dataset of top-down semantic maps, and integrate with good sample efficiency, better generalization to new it into a modular framework to perform ObjectGoal navigation.

The increasing use of machine learning in high-stakes domains -- where people's livelihoods are impacted -- creates an urgent need for interpretable and fair algorithms. In these settings it is also critical for such algorithms to be accurate. With these needs in mind, we propose a mixed integer optimization (MIO) framework for learning optimal classification trees of fixed depth that can be conveniently augmented with arbitrary domain specific fairness constraints. We benchmark our method against the state-of-the-art approach for building fair trees on popular datasets; given a fixed discrimination threshold, our approach improves out-of-sample (OOS) accuracy by 2.3 percentage points on average and obtains a higher OOS accuracy on 88.9% of the experiments. We also incorporate various algorithmic fairness notions into our method, showcasing its versatile modeling power that allows decision makers to fine-tune the trade-off between accuracy and fairness.