This begs the question: Can SSL algorithms simultaneously improve upon both UL and SL?

Collaborative causal inference (CCI) is a federated learning method for pooling data from multiple, often self-interested, parties, to achieve a common learning goal over causal structures, e.g. estimation and optimization of treatment variables in a medical setting. Since obtaining data can be costly for the participants and sharing unique data poses the risk of losing competitive advantages, motivating the participation of all parties through equitable rewards and incentives is necessary. This paper devises an evaluation scheme to measure the value of each party's data contribution to the common learning task, tailored to causal inference's statistical demands, by comparing completed partially directed acyclic graphs (CPDAGs) inferred from observational data contributed by the participants. The Data Valuation Scheme thus obtained can then be used to introduce mechanisms that incentivize the agents to contribute data. It can be leveraged to reward agents fairly, according to the quality of their data, or to maximize all agents' data contributions.

In the past decade, artificial intelligence, cloud computing, quantum computing, and 5G communication technologies undergo rapid advances. Meanwhile, tremendous innovations also emerge and gain momentum in traditional technological domains, such as autonomous vehicles [1], drug discovery [2], and protein structure prediction [3]. Such contemporary innovation phenomena call for new theories and frameworks to explain them, understand the driving forces, and inform future innovation. Many contemporary design innovations share one characteristic in common: they are based on the synthesis and fusion of different technological domains, which used to be unrelated and separately developed, e.g., artificial intelligence and automobile. The rise of such innovations has ambiguated the boundaries of technological domains and industries.

Robust controllers ensure stability in feedback loops designed under uncertainty but at the cost of performance. Model uncertainty in time-invariant systems can be reduced by recently proposed learning-based methods, which improve the performance of robust controllers using data. However, in practice, many systems also exhibit uncertainty in the form of changes over time, e.g., due to weight shifts or wear and tear, leading to decreased performance or instability of the learning-based controller. We propose an event-triggered learning algorithm that decides when to learn in the face of uncertainty in the LQR problem with rare or slow changes. Our key idea is to switch between robust and learned controllers. For learning, we first approximate the optimal length of the learning phase via Monte-Carlo estimations using a probabilistic model. We then design a statistical test for uncertain systems based on the moment-generating function of the LQR cost. The test detects changes in the system under control and triggers re-learning when control performance deteriorates due to system changes. We demonstrate improved performance over a robust controller baseline in a numerical example.

MIT researchers have used AI to predict which technologies are rapidly improving -- and which ones are overhyped. In a new study, the team quantitatively assessed the future potential of 97% of the US patent system. The fastest-improving domains were predominantly software-related. They then converted their findings into an online system in which users can enter keywords to find improvement forecasts for specific technologies. Their research could give entrepreneurs, researchers, investors, and policy-makers clues about the future opportunities in tech.

An optimal dynamic treatment regime (DTR) consists of a sequence of decision rules in maximizing long-term benefits, which is applicable for chronic diseases such as HIV infection or cancer. In this paper, we develop a novel angle-based approach to search the optimal DTR under a multicategory treatment framework for survival data. The proposed method targets maximization the conditional survival function of patients following a DTR. In contrast to most existing approaches which are designed to maximize the expected survival time under a binary treatment framework, the proposed method solves the multicategory treatment problem given multiple stages for censored data. Specifically, the proposed method obtains the optimal DTR via integrating estimations of decision rules at multiple stages into a single multicategory classification algorithm without imposing additional constraints, which is also more computationally efficient and robust. In theory, we establish Fisher consistency of the proposed method under regularity conditions. Our numerical studies show that the proposed method outperforms competing methods in terms of maximizing the conditional survival function. We apply the proposed method to two real datasets: Framingham heart study data and acquired immunodeficiency syndrome (AIDS) clinical data.

Currently the news are filled with articles about the rise of machine intelligence, artificial intelligence and deep learning. For the average reader it seems that there was this single technical breakthrough that made AI possible. While I strongly believe in the fascinating opportunities around deep learning for image recognition, natural language processing and even end-to-end "intelligent" systems (e.g. First I read about tensorflow (for R) and watched a number of great talks about it. Do not miss Nuts and Bolts of Applying Deep Learning (Andrew Ng) and Tensorflow and deep learning - without at PhD by Martin Görner. Second I started to look at publications and error improvements on public datasets.