In this work, we enrich a formalism for argumentation by including a formal characterization of features related to the knowledge, in order to capture proper reasoning in legal domains. We add meta-data information to the arguments in the form of labels representing quantitative and qualitative data about them. These labels are propagated through an argumentative graph according to the relations of support, conflict, and aggregation between arguments.

Argumentation theory is a powerful paradigm that formalizes a type of commonsense reasoning that aims to simulate the human ability to resolve a specific problem in an intelligent manner. A classical argumentation process takes into account only the properties related to the intrinsic logical soundness of an argument in order to determine its acceptability status. However, these properties are not always the only ones that matter to establish the argument's acceptability---there exist other qualities, such as strength, weight, social votes, trust degree, relevance level, and certainty degree, among others.

In this paper, we introduce a formalism for single-agent decision making that is based on Dynamic Argumentation Frameworks. The formalism can be used to justify a choice, which is based on the current situation the agent is involved. Taking advantage of the inference mechanism of the argumentation formalism, it is possible to consider preference relations and conflicts among the available alternatives for that reasoning. With this formalization, given a particular set of evidence, the justified conclusions supported by warranted arguments will be used by the agent's decision rules to determine which alternatives will be selected. We also present an algorithm that implements a choice function based on our formalization. Finally, we complete our presentation by introducing formal results that relate the proposed framework with approaches of classical decision theory.

We propose a distance supervised relation extraction approach for long-tailed, imbalanced data which is prevalent in real-world settings. Here, the challenge is to learn accurate "few-shot" models for classes existing at the tail of the class distribution, for which little data is available. Inspired by the rich semantic correlations between classes at the long tail and those at the head, we take advantage of the knowledge from data-rich classes at the head of the distribution to boost the performance of the data-poor classes at the tail. First, we propose to leverage implicit relational knowledge among class labels from knowledge graph embeddings and learn explicit relational knowledge using graph convolution networks. Second, we integrate that relational knowledge into relation extraction model by coarse-to-fine knowledge-aware attention mechanism. We demonstrate our results for a large-scale benchmark dataset which show that our approach significantly outperforms other baselines, especially for long-tail relations.

Data visualization can be considered the art to data analysis' science. This is where you get to be really creative in finding ways to communicate the secrets hidden within your raw data. The field of data visualization continues to explode, flooding that market with all strata of platforms from the simple (infogram) to the highly complex (power BI, Tableau). Artistry has a role in your data presentation, which can be strengthened by SVG tools such as RAWGraphs. Data can be represented in an infinite number of ways and the number is only increasing. From time-series data to geospatial data, scatter plots to nominal comparisons, bubble charts to spider charts, hierarchical tree diagrams to network maps and relational visualizations, you won't lack for options.

The artificial intelligence revolution has been going on for a while, but that does not mean all of software research and development is part of it. It is, nonetheless, an interesting example of what pushes the software industry forward. The ideas and techniques that brought machine learning into existence were developed over the span of several decades, gaining momentum in the 80s as a long-term endeavor that started in academia. Significant development occurred once the community started comparing its tools in competitions, and measuring performance and applicability. Fast-forward to the late 90s, and the big software companies would start to develop techniques and infrastructure to gather, store and process huge amounts of data -- data that then could be used to train classification and search-based algorithms.

Inspired by a theory into the organisms of memory and recall in the brain, neural networking is a digital simulation of how synapses may retain information, after being trained to recognize patterns. For instance, neural nets enable a computer, or perhaps a cloud-based service, to recognize the characters of printed text without the need for programming explicitly specifying what text is, or how it can spot a certain face in a crowd after having seen several photographs of the same face. As a neural networking problem becomes linearly broader -- for example, distinguishing one form of written text from another -- the data required to train it grows exponentially larger. There's a valid argument that some of the tasks being envisioned for neural nets, such as spotting when anyone is getting depressed or agitated, may be impossible, even with today's storage and memory technologies. So the revelations by researchers that chemical structures comprised of completely random assemblies of nanometer-scale wires may exhibit the electrical characteristics of memory in a brain perhaps shouldn't continue to be dismissed for much longer.

Differential privacy is becoming a standard notion for performing privacy-preserving machine learning over sensitive data. It provides formal guarantees, in terms of the privacy budget, $\epsilon$, on how much information about individual training records is leaked by the model. While the privacy budget is directly correlated to the privacy leakage, the calibration of the privacy budget is not well understood. As a result, many existing works on privacy-preserving machine learning select large values of $\epsilon$ in order to get acceptable utility of the model, with little understanding of the concrete impact of such choices on meaningful privacy. Moreover, in scenarios where iterative learning procedures are used which require privacy guarantees for each iteration, relaxed definitions of differential privacy are often used which further tradeoff privacy for better utility. In this paper, we evaluate the impacts of these choices on privacy in experiments with logistic regression and neural network models. We quantify the privacy leakage in terms of advantage of the adversary performing inference attacks and by analyzing the number of members at risk for exposure. Our main findings are that current mechanisms for differential privacy for machine learning rarely offer acceptable utility-privacy tradeoffs: settings that provide limited accuracy loss provide little effective privacy, and settings that provide strong privacy result in useless models. Open source code is available at https://github.com/bargavj/EvaluatingDPML.

Learning accurate dynamics models is necessary for optimal, compliant control of robotic systems. Current approaches to white-box modeling using analytic parameterizations, or black-box modeling using neural networks, can suffer from high bias or high variance. We address the need for a flexible, gray-box model of mechanical systems that can seamlessly incorporate prior knowledge where it is available, and train expressive function approximators where it is not. We propose to parameterize a mechanical system using neural networks to model its Lagrangian and the generalized forces that act on it. We test our method on a simulated, actuated double pendulum. We show that our method outperforms a naive, black-box model in terms of data-efficiency, as well as performance in model-based reinforcement learning. We also conduct a systematic study of our method's ability to incorporate available prior knowledge about the system to improve data efficiency.

Inspired by research in psychology, we introduce a behavioral approach for visual navigation using topological maps. Our goal is to enable a robot to navigate from one location to another, relying only on its visual input and the topological map of the environment. We propose using graph neural networks for localizing the agent in the map, and decompose the action space into primitive behaviors implemented as convolutional or recurrent neural networks. Using the Gibson simulator, we verify that our approach outperforms relevant baselines and is able to navigate in both seen and unseen environments.