Quantifying probabilistic robustness of tree-based classifiers against natural distortions

The concept of trustworthy AI has gained widespread attention lately. One of the aspects relevant to trustworthy AI is robustness of ML models. In this study, we show how to probabilistically quantify robustness against naturally occurring distortions of input data for tree-based classifiers under the assumption that the natural distortions can be described by multivariate probability distributions that can be transformed to multivariate normal distributions. The idea is to extract the decision rules of a trained tree-based classifier, separate the feature space into non-overlapping regions and determine the probability that a data sample with distortion returns its predicted label. The approach is based on the recently introduced measure of real-world-robustness, which works for all black box classifiers, but is only an approximation and only works if the input dimension is not too high, whereas our proposed method gives an exact measure.Comment: 9 pages, 5 figure

Discovering Transcriptional Regulatory Rules from Gene Expression and TF-DNA Binding Data by Decision Tree Learning

Background: One of the most promising but challenging task in the post-genomic era is to reconstruct the transcriptional regulatory networks. The goal is to reveal, for each gene that responds to a certain biological event, which transcription factors affect its transcription, and how several transcription factors coordinate to accomplish specific regulations. Results: Here we propose a supervised machine learning approach to address these questions. We build decision trees to associate the expression level of a gene with the transcription factor binding data of its promoter. From the decision trees, we extract regulatory rules that specify how the binding of a combination of several transcription factors affects the expression of a gene. Such rules are easy to interpret, and represent experimentally testable hypotheses. We use a decision tree ensemble approach to increase modeling accuracy and robustness. We also propose a novel method to integrate rules learned from several time series that measure the same biological processes. We apply our method to publicly available cell cycle expression data and transcription factor binding data for the budding yeast. Cross-validation experiments show that our method is highly accurate and reliable. The method correctly identifies all major known yeast cell cycle transcription factors, and assigns them into appropriate cell cycle phases. It also explicitly reveals synergetic relationships of transcription factors, most of which agree well with existing literatures, while the rest provide testable biological hypotheses. Conclusions: The high accuracy of our method indicates that our method is valid and that the learned regulatory rules can be used as the basic building elements of a transcriptional regulatory network. As more and more gene expression and TF binding data are available, we believe that our method will be useful for reconstructing large scale transcriptional regulatory networks

Investigation of Air Transportation Technology at Princeton University, 1989-1990

The Air Transportation Technology Program at Princeton University proceeded along six avenues during the past year: microburst hazards to aircraft; machine-intelligent, fault tolerant flight control; computer aided heuristics for piloted flight; stochastic robustness for flight control systems; neural networks for flight control; and computer aided control system design. These topics are briefly discussed, and an annotated bibliography of publications that appeared between January 1989 and June 1990 is given

Reinforcement Learning With Temporal Logic Rewards

Reinforcement learning (RL) depends critically on the choice of reward functions used to capture the de- sired behavior and constraints of a robot. Usually, these are handcrafted by a expert designer and represent heuristics for relatively simple tasks. Real world applications typically involve more complex tasks with rich temporal and logical structure. In this paper we take advantage of the expressive power of temporal logic (TL) to specify complex rules the robot should follow, and incorporate domain knowledge into learning. We propose Truncated Linear Temporal Logic (TLTL) as specifications language, that is arguably well suited for the robotics applications, together with quantitative semantics, i.e., robustness degree. We propose a RL approach to learn tasks expressed as TLTL formulae that uses their associated robustness degree as reward functions, instead of the manually crafted heuristics trying to capture the same specifications. We show in simulated trials that learning is faster and policies obtained using the proposed approach outperform the ones learned using heuristic rewards in terms of the robustness degree, i.e., how well the tasks are satisfied. Furthermore, we demonstrate the proposed RL approach in a toast-placing task learned by a Baxter robot

Disturbances, robustness and adaptation in forest commons: comparative insights from two cases in the Southeastern Alps

Exposure to disturbances of different nature and scale can represent a threat for the survival of rural communities but also a stimulus to adjustment. Disturbance, robustness and adaptation are here examined through the lens of Forest Commons, as a typical institution, developed by communities in the southeastern Alps since several centuries. The paper relies on Commons' theory and further developments and carries out a historically-embedded analysis of disturbances, robustness and adaptation in Forest Commons of Slovenia and Veneto (Italy). Data have been drawn from multiple sources, following an approach based on an area scale and later on case-studies. The analysis focuses on evidence of Forest Commons\ub4 reactions to disturbances induced by political changes and State actions. Ostrom's design principles are used to test robustness of eight selected cases and identification of their adaptation patterns. The paper concludes by confirming Forest Commons as robust and adaptive socio-ecological systems and thus useful in Community Forestry conceptualisation. However, thanks to its cross-border analysis, it also points out future research needs for their better understanding

Robustness

The standard theory of decision making under uncertainty advises the decision maker to form a statistical model linking outcomes to decisions and then to choose the optimal distribution of outcomes. This assumes that the decision maker trusts the model completely. But what should a decision maker do if the model cannot be trusted? Lars Hansen and Thomas Sargent, two leading macroeconomists, push the field forward as they set about answering this question. They adapt robust control techniques and apply them to economics. By using this theory to let decision makers acknowledge misspecification in economic modeling, the authors develop applications to a variety of problems in dynamic macroeconomics. Technical, rigorous, and self-contained, this book will be useful for macroeconomists who seek to improve the robustness of decision-making processes.decision-making, uncertainty, statistical models, control techniques, economic modeling, dynamic microeconomics, misspecification