--The purpose of this work is to develop and study a distributed strategy for Pareto optimization of an aggregate cost consisting of regularized risks. Each risk is modeled as the expectation of some loss function with unknown probability distribution while the regularizers are assumed deterministic, but are not required to be differentiable or even continuous. The individual, regularized, cost functions are distributed across a strongly-connected network of agents and the Pareto optimal solution is sought by appealing to a multi-agent diffusion strategy. T o this end, the regularizers are smoothed by means of infimal convolution and it is shown that the Pareto solution of the approximate, smooth problem can be made arbitrarily close to the solution of the original, non-smooth problem. Performance bounds are established under conditions that are weaker than assumed before in the literature, and hence applicable to a broader class of adaptation and learning problems. Index T erms --Distributed optimization, diffusion strategy, smoothing, proximal operator, non-smooth regularizer, proximal diffusion, regularized diffusion. The objective of distributed learning is the solution of global, stochastic optimization problems across networks of agents through localized interactions and without information about the statistical properties of the data. Using streaming data, the resulting strategies are adaptive in nature and able to track drifts in the location of the minimizers due to variations in the statistical properties of the data. Regularization is one useful technique to encourage or enforce structural properties on the sought after minimizer, such as sparsity or constraints. A substantial number of regularizers are inherently non-smooth, while many cost functions are differentiable.

A new procedure to construct predictive models in supervised learning problems by paying attention to the clustering structure of the input data is introduced. We are interested in situations where the input data consists of more than one unknown cluster, and where there exist different underlying models on these clusters. Thus, instead of constructing a single predictive model on the whole dataset, we propose to use a K-means clustering algorithm with different options of Bregman divergences, to recover the clustering structure of the input data. Then one dedicated predictive model is fit per cluster. For each divergence, we construct a simple local predictor on each observed cluster. We obtain one estimator, the collection of the K simple local predictors, per divergence, and we propose to combine them in a smart way based on a consensus idea. Several versions of consensual aggregation in both classification and regression problems are considered. A comparison of the performances of all constructed estimators on different simulated and real data assesses the excellent performance of our method. In a large variety of prediction problems, the consensual aggregation procedure outperforms all the other models.

Recently, pretrained language models (e.g., BERT) have achieved great success on many downstream natural language understanding tasks and exhibit a certain level of commonsense reasoning ability. However, their performance on commonsense tasks is still far from that of humans. As a preliminary attempt, we propose a simple yet effective method to teach pretrained models with commonsense reasoning by leveraging the structured knowledge in ConceptNet, the largest commonsense knowledge base (KB). Specifically, the structured knowledge in KB allows us to construct various logical forms, and then generate multiple-choice questions requiring commonsense logical reasoning. Experimental results demonstrate that, when refined on these training examples, the pretrained models consistently improve their performance on tasks that require commonsense reasoning, especially in the few-shot learning setting. Besides, we also perform analysis to understand which logical relations are more relevant to commonsense reasoning.

One of the most studied problems in machine learning is finding reasonable constraints that guarantee the generalization of a learning algorithm. These constraints are usually expressed as some simplicity assumptions on the target. For instance, in the V apnik-Chervonenkis (VC) theory the space of possible hypotheses is considered to have a limited VC dimension and in kernel methods there are assumptions on the spectrum of the operator in the Hilbert space. One way to formulate the simplicity assumption is via information theoretic concepts. In this paper, the constraint on the entropy H ( X) of the input variable X is studied as a simplicity assumption. It is proven that the sample complexity to achieve an null -δ Probably Approximately Correct (P AC) hypothesis is bounded by 2 2 H ( X) / null log 1 δ null 2 which is sharp up to the 1 null 2 factor. Morever, it is shown that if a feature learning process is employed to learn the compressed representation from the dataset, this bound no longer exists. These findings have important implications on the Information Bottleneck (IB) theory which had been utilized to explain the generalization power of Deep Neural Networks (DNNs), but its applicability for this purpose is currently under debate by researchers. In particular, this is a rigorous proof for the previous heuristic that compressed representations are expnentially easier to be learned. However, our analysis pinpoints two factors preventing the IB, in its current form, to be applicable in studying neural networks. Firstly, the exponential dependence of sample complexity on 1 / null, which can lead to a dramatic e ff ect on the bounds in practical applications when null is small. Secondly, our analysis reveals that arguments based on input compression are inherently insu fficient to explain generalization of methods like DNNs in which the features are also learned using available data. Keywords: Compressed Representation; Generalization Bound; Information Bottleneck. 1. Introduction The main objective of learning is to develop algorithms which can learn general patterns by using a finite number of samples drawn from a target distribution. The "no free lunch" theorem states that if there is no constraint on the distribution, it is impossible to say anything about the samples not seen in the training set (Wolpert, 1996b).

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For over 300 years, the Alamo City has been a dynamic platform for technological innovation and global commerce. Tech Port SA, offered by Port San Antonio, rounds-up the latest stories chronicling the region's movers and shakers in advanced industries.

This summer, four mechanical engineering graduate students had the opportunity to gain hands-on experience working in industry. Through the recently launched Industry Immersion Project Program (I2P), students were paired with a company and tasked with tackling a short-term project. Projects in this inaugural year for the program came from a diverse range of industries, including manufacturing, robotics, and aerospace engineering. A flagship program of the MechE Alliance, the I2P Program matches students with a company and project that best fits within their own academic experience at MIT. Projects are designed to be short term, lasting three to six months. Building upon programs such as the Master of Engineering in Advanced Manufacturing and Design and Leaders for Global Operations, which foster collaborations between students and the manufacturing industry, the I2P Program offers graduate students real-world experiences across industries.

Artificial Intelligence and machine learning have disrupted human activity since its inception in the 1960s. Today, we depend on intelligent machines to perform highly sophisticated and specific tasks without explicit human input. Rather, they rely on patterns and inferences instead. AI algorithms have been used in a wide variety of applications, from email filtering and computer vision to the disruption of the retail, travel and finance industries. An ancient sector of our economy, and one that has largely remained unchanged; education- has yet to realise the full implications of artificial intelligence within its operations.

Every industrial revolution brings a wave of change. In the fourth industrial revolution, technology is drastically redefining the landscape of work. Today's video explores how automation and AI are accelerating the skill shift needed for the future of work. How can young people stay ahead of this curve? By 2030, artificial intelligence (AI) will boost the global economy by $15.7 trillion.

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