Problems of interpolation, classification, and clustering are considered. In the tenets of Radon--Nikodym approach $\langle f(\mathbf{x})\psi^2 \rangle / \langle\psi^2\rangle$, where the $\psi(\mathbf{x})$ is a linear function on input attributes, all the answers are obtained from a generalized eigenproblem $|f|\psi^{[i]}\rangle = \lambda^{[i]} |\psi^{[i]}\rangle$. The solution to the interpolation problem is a regular Radon-Nikodym derivative. The solution to the classification problem requires prior and posterior probabilities that are obtained using the Lebesgue quadrature[1] technique. Whereas in a Bayesian approach new observations change only outcome probabilities, in the Radon-Nikodym approach not only outcome probabilities but also the probability space $|\psi^{[i]}\rangle$ change with new observations. This is a remarkable feature of the approach: both the probabilities and the probability space are constructed from the data. The Lebesgue quadrature technique can be also applied to the optimal clustering problem. The problem is solved by constructing a Gaussian quadrature on the Lebesgue measure. A distinguishing feature of the Radon-Nikodym approach is the knowledge of the invariant group: all the answers are invariant relatively any non-degenerated linear transform of input vector $\mathbf{x}$ components. A software product implementing the algorithms of interpolation, classification, and optimal clustering is available from the authors.

We present Generative Adversarial Privacy and Fairness (GAPF), a data-driven framework for learning private and fair representations. GAPF leverages recent advancements in adversarial learning to allow a data holder to learn "universal" representations that decouple a set of sensitive attributes from the rest of the dataset. Under GAPF, finding the optimal privacy mechanism is formulated as a constrained minimax game between a private/fair encoder and an adversary. We show that for appropriately chosen adversarial loss functions, GAPF provides privacy guarantees against strong information-theoretic adversaries and enforces demographic parity. We also evaluate the performance of GAPF on multi-dimensional Gaussian mixture models and real datasets, and show how a designer can certify that representations learned under an adversary with a fixed architecture perform well against more complex adversaries.

Designing effective model-based reinforcement learning algorithms is difficult because the ease of data generation must be weighed against the bias of model-generated data. In this paper, we study the role of model usage in policy optimization both theoretically and empirically. We first formulate and analyze a model-based reinforcement learning algorithm with a guarantee of monotonic improvement at each step. In practice, this analysis is overly pessimistic and suggests that real off-policy data is always preferable to model-generated on-policy data, but we show that an empirical estimate of model generalization can be incorporated into such analysis to justify model usage. Motivated by this analysis, we then demonstrate that a simple procedure of using short model-generated rollouts branched from real data has the benefits of more complicated model-based algorithms without the usual pitfalls. In particular, this approach surpasses the sample efficiency of prior model-based methods, matches the asymptotic performance of the best model-free algorithms, and scales to horizons that cause other model-based methods to fail entirely.

Elections and opinion polls often have many candidates, with the aim to either rank the candidates or identify a small set of winners according to voters' preferences. In practice, voters do not provide a full ranking; instead, each voter provides their favorite K candidates, potentially in ranked order. The election organizer must choose K and an aggregation rule. We provide a theoretical framework to make these choices. Each K-Approval or K-partial ranking mechanism (with a corresponding positional scoring rule) induces a learning rate for the speed at which the election correctly recovers the asymptotic outcome. Given the voter choice distribution, the election planner can thus identify the rate optimal mechanism. Earlier work in this area provides coarse order-of-magnitude guaranties which are not sufficient to make such choices. Our framework further resolves questions of when randomizing between multiple mechanisms may improve learning, for arbitrary voter noise models. Finally, we use data from 5 large participatory budgeting elections that we organized across several US cities, along with other ranking data, to demonstrate the utility of our methods. In particular, we find that historically such elections have set K too low and that picking the right mechanism can be the difference between identifying the ultimate winner with only a 80% probability or a 99.9% probability after 400 voters.

In this work we present a novel approach to solving concurrent multiagent planning problems in which several agents act in parallel. Our approach relies on a compilation from concurrent multiagent planning to classical planning, allowing us to use an off-the-shelf classical planner to solve the original multiagent problem. The solution can be directly interpreted as a concurrent plan that satisfies a given set of concurrency constraints, while avoiding the exponential blowup associated with concurrent actions. Our planner is the first to handle action effects that are conditional on what other agents are doing. Theoretically, we show that the compilation is sound and complete. Empirically, we show that our compilation can solve challenging multiagent planning problems that require concurrent actions.

This paper presents an empirical study aiming at understanding the modeling style and the overall semantic structure of Linked Open Data. We observe how classes, properties and individuals are used in practice. We also investigate how hierarchies of concepts are structured, and how much they are linked. In addition to discussing the results, this paper contributes (i) a conceptual framework, including a set of metrics, which generalises over the observable constructs; (ii) an open source implementation that facilitates its application to other Linked Data knowledge graphs.

Service supply chain management is to prepare spare parts for failed products under warranty. Their goal is to reach agreed service level at the minimum cost. We convert this business problem into a preference based multi-objective optimization problem, where two quality criteria must be simultaneously optimized. One criterion is accuracy of demand forecast and the other is service level. Here we propose a general framework supporting solving preference-based multi-objective optimization problems (MOPs) by multi-gradient descent algorithm (MGDA), which is well suited for training deep neural network. The proposed framework treats agreed service level as a constrained criterion that must be met and generate a Pareto-optimal solution with highest forecasting accuracy. The neural networks used here are two Encoder-Decoder LSTM modes: one is used for pre-training phase to learn distributed representation of former generations' service parts consumption data, and the other is used for supervised learning phase to generate forecast quantities of current generations' service parts. Evaluated under the service parts consumption data in Lenovo Group Ltd, the proposed method clearly outperform baseline methods.

It's great to spot a familiar face -- and now researchers have'cracked the code' that our brains use to tell one apart from another. Our memories of the faces of people we know focus on key facial features let us recognise them when we meet. Volunteers were asked to rank how closely randomly-created digital faces matched with their memory of the face of a colleague. This process was repeated over and over, revealing key identifying facial features of the colleague being remembered. Computer software analysed the data on these instances of key features to recreate the faces in question.

NASA's Osiris-REx spacecraft has made its closest approach yet to an asteroid 1.4 billion miles from Earth. The probe dipped down to just .4 According to NASA, this put it in position to break the record for'the closest distance a spacecraft has orbited a body in the solar system.' Stunning new images from after the maneuver now reveal a close look at the boulders and craters dotting the surface of the distant object. The close-up image shared this week by NASA was captured on the 13th, showing Bennu half sunlit and half in shadow from the spacecraft's view. At the time, Osiris-REx was .4

FightCamp offers an interactive library of workouts available via subscription. And the Bowflex Max Trainer cardio machine incorporates artificial intelligence to help you step your home workout game up - literally. Best Buy is taking a step toward the future of at-home workouts, offering connected gym equipment from various popular brands both in-store and online. The electronics retailer announced Tuesday it is selling indoor cycling bikes, compression recovery systems, fitness rollers and connected treadmills from wellness tech brands like Flywheel, NormaTec and Hyperice. The high-end fitness equipment, now available online, will be sold in more than 100 stores by the end of this year, and Best Buy will help customers select equipment and install it after purchase.