Meta learning is a promising paradigm to enable skill transfer across tasks.Most previous methods employ the empirical risk minimization principle in optimization.However, the resulting worst fast adaptation to a subset of tasks can be catastrophic in risk-sensitive scenarios.To robustify fast adaptation, this paper optimizes meta learning pipelines from a distributionally robust perspective and meta trains models with the measure of tail task risk.We take the two-stage strategy as heuristics to solve the robust meta learning problem, controlling the worst fast adaptation cases at a certain probabilistic level. Experimental results show that our simple method can improve the robustness of meta learning to task distributions and reduce the conditional expectation of the worst fast adaptation risk.

Meta learning is a promising paradigm to enable skill transfer across tasks.Most previous methods employ the empirical risk minimization principle in optimization.However, the resulting worst fast adaptation to a subset of tasks can be catastrophic in risk-sensitive scenarios.To robustify fast adaptation, this paper optimizes meta learning pipelines from a distributionally robust perspective and meta trains models with the measure of tail task risk.We take the two-stage strategy as heuristics to solve the robust meta learning problem, controlling the worst fast adaptation cases at a certain probabilistic level. Experimental results show that our simple method can improve the robustness of meta learning to task distributions and reduce the conditional expectation of the worst fast adaptation risk.

Solving strategic games whose action space is prohibitively large is a critical yet under-explored topic in economics, computer science and artificial intelligence. This paper proposes new learning algorithms in two-player zero-sum games where the number of pure strategies is huge or even infinite. Specifically, we combine no-regret analysis from online learning with double oracle methods from game theory. Our method -- \emph{Online Double Oracle (ODO)} -- achieves the regret bound of $\mathcal{O}(\sqrt{T k \log(k)})$ in self-play setting where $k$ is NOT the size of the game, but rather the size of \emph{effective strategy set} that is linearly dependent on the support size of the Nash equilibrium. On tens of different real-world games, including Leduc Poker that contains $3^{936}$ pure strategies, our methods outperform no-regret algorithms and double oracle methods by a large margin, both in convergence rate to Nash equilibrium and average payoff against strategic adversary.

You could define "a great website" a variety of ways. In an effort to cover a wide gamut of website types, I'll try to bring a common denominator into play by saying: A great website inspires the visitor to take action. Of course, the best website in the world won't invoke an action every time. However, when you understand and apply the elements that make a great website, your chances to produce a successful outcome multiply immensely. So here we go: a generous list of things you can do to create a great website.

Accruing a large amount of data is relatively simple. Data can be scraped, created or copied and then be stored in huge data storages. A key driver in developing an intelligent model, however, is not just a sheer mass of data but also an effective strategy to intelligently label data to add structure and sense to the data. Data labeling can, therefore, be described as a way to organize information depending on its content. This content determines the tag or label to be assigned to a specific piece of information after it has been processed.

Ever since the concept of Artificial Intelligence emerged, it became one of the most talked-about trends in the world. People see AI as "the new normal" as it has made its way into different work processes in all most all kinds of industries from augmented analytics to facial surveillance. Throughout 2018, we saw an incredible surge in platforms, tools, and applications focused on AI-and Machine Learning technologies. Ai and ML started in the internet and software trade, but now, we can also see them in different aspects of manufacturing, agriculture, healthcare, and more. Get the entire 10-part series on our in-depth study on activist investing in PDF. Save it to your desktop, read it on your tablet, or print it out to read anywhere!

Despite the availability of big data and improvements in technology, errors and lapses in the workflow and processes of image-guided radiotherapy (IGRT) may lead to a misbalancing of risk and benefit among patients with cancer. One of the risks of radiotherapy include secondary radiation-induced malignancy, but the exact mechanism and dose-response relationship between them is unknown. By properly accessing digital data and balancing it with modern technology, the authors suggest that effective strategies for reducing the risk of cancer can be formulated. For this review, the authors present the background and current challenges in 4 key areas of radiotherapy: screening and diagnosis, contouring and planning, targeting and delivery, and follow-up care and re-irradiation. They also present potential future options, such as using artificial intelligence to create safe and effective strategies in radiotherapy.

The main goal I have is to equip the reader with an in-depth understanding of the fundamentals of applied machine learning. If you would like to build a solid foundation to analyze the implications of artificial intelligence for your industry and your personal life, then this series is for you. The plan is to cover the the most successful machine learning models as well as some of the latest validated research trends. I will not discuss any approaches that have either failed to gain traction or any speculative ideas that have yet to receive empirical support. The material is self-contained and develops the foundations of applied machine learning one step at a time.

While many people drink in the hope of drowning out unwanted memories, a surprising new study suggests this may not be an effective strategy. The study suggests that drinking alcohol improves your memory for information learned before the drinking episode began. While the reason for this remains unclear, researchers suggest that alcohol may block the learning of new information, giving the brain more resources to lay down recently learned information into long-term memory. While many people drink in the hopes of drowning out unwanted memories, a surprising new study suggests this may not be an effective strategy. The study looked at the effects of drinking alcohol in a natural setting on memory.