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Journal ArticleDOI

Pattern Recognition and Machine Learning

01 Aug 2007-Technometrics (Taylor & Francis)-Vol. 49, Iss: 3, pp 366-366
TL;DR: This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.
Abstract: (2007). Pattern Recognition and Machine Learning. Technometrics: Vol. 49, No. 3, pp. 366-366.
Citations
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Journal ArticleDOI
TL;DR: This tutorial is a high-level introduction to Bayesian nonparametric methods and contains several examples of their application.

549 citations


Cites background from "Pattern Recognition and Machine Lea..."

  • ...(For a detailed discussion of De Finetti’s representation theorems, see Bernardo and Smith (1994).)...

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Proceedings ArticleDOI
03 Dec 2010
TL;DR: IGP is developed, a nonparametric statistical model based on dependent output Gaussian processes that can estimate crowd interaction from data that naturally captures the non-Markov nature of agent trajectories, as well as their goal-driven navigation.
Abstract: In this paper, we study the safe navigation of a mobile robot through crowds of dynamic agents with uncertain trajectories. Existing algorithms suffer from the “freezing robot” problem: once the environment surpasses a certain level of complexity, the planner decides that all forward paths are unsafe, and the robot freezes in place (or performs unnecessary maneuvers) to avoid collisions. Since a feasible path typically exists, this behavior is suboptimal. Existing approaches have focused on reducing the predictive uncertainty for individual agents by employing more informed models or heuristically limiting the predictive covariance to prevent this overcautious behavior. In this work, we demonstrate that both the individual prediction and the predictive uncertainty have little to do with the frozen robot problem. Our key insight is that dynamic agents solve the frozen robot problem by engaging in “joint collision avoidance”: They cooperatively make room to create feasible trajectories. We develop IGP, a nonparametric statistical model based on dependent output Gaussian processes that can estimate crowd interaction from data. Our model naturally captures the non-Markov nature of agent trajectories, as well as their goal-driven navigation. We then show how planning in this model can be efficiently implemented using particle based inference. Lastly, we evaluate our model on a dataset of pedestrians entering and leaving a building, first comparing the model with actual pedestrians, and find that the algorithm either outperforms human pedestrians or performs very similarly to the pedestrians. We also present an experiment where a covariance reduction method results in highly overcautious behavior, while our model performs desirably.

547 citations


Cites methods from "Pattern Recognition and Machine Lea..."

  • ...Standard approaches to approximate inference in models derived from GPs include Laplace approximation [3] and Expectation Propagation [17]....

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Journal ArticleDOI
01 Apr 2015
TL;DR: AdFisher, an automated tool that explores how user behaviors, Google's ads, and Ad Settings interact, finds that the Ad Settings was opaque about some features of a user’s profile, that it does provide some choice on advertisements, and that these choices can lead to seemingly discriminatory ads.
Abstract: To partly address people’s concerns over web tracking, Google has created the Ad Settings webpage to provide information about and some choice over the profiles Google creates on users We present AdFisher, an automated tool that explores how user behaviors, Google’s ads, and Ad Settings interact Our tool uses a rigorous experimental design and analysis to ensure the statistical significance of our results It uses machine learning to automate the selection of a statistical test We use AdFisher to find that Ad Settings is opaque about some features of a user’s profile, that it does provide some choice on ads, and that these choices can lead to seemingly discriminatory ads In particular, we found that visiting webpages associated with substance abuse will change the ads shown but not the settings page We also found that setting the gender to female results in getting fewer instances of an ad related to high paying jobs than setting it to male

544 citations

Journal ArticleDOI
TL;DR: An overview of this emerging field of molecular informatics, the basic concepts of prominent deep learning methods are presented, and motivation to explore these techniques for their usefulness in computer‐assisted drug discovery and design is offered.
Abstract: Artificial neural networks had their first heyday in molecular informatics and drug discovery approximately two decades ago. Currently, we are witnessing renewed interest in adapting advanced neural network architectures for pharmaceutical research by borrowing from the field of “deep learning”. Compared with some of the other life sciences, their application in drug discovery is still limited. Here, we provide an overview of this emerging field of molecular informatics, present the basic concepts of prominent deep learning methods and offer motivation to explore these techniques for their usefulness in computer-assisted drug discovery and design. We specifically emphasize deep neural networks, restricted Boltzmann machine networks and convolutional networks.

538 citations


Cites methods from "Pattern Recognition and Machine Lea..."

  • ...This method, in which each variable draws a sample from its posterior while keeping the other variables fixed, is also known as Gibbs sampling.([63]) Essentially, the same trick of inferring states by using conditional probabilities may be used to derive an expression for the negative phase term....

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Journal ArticleDOI
TL;DR: Q-learning and the integral RL algorithm as core algorithms for discrete time (DT) and continuous-time (CT) systems, respectively are discussed, and a new direction of off-policy RL for both CT and DT systems is discussed.
Abstract: This paper reviews the current state of the art on reinforcement learning (RL)-based feedback control solutions to optimal regulation and tracking of single and multiagent systems. Existing RL solutions to both optimal $\mathcal {H}_{2}$ and $\mathcal {H}_\infty $ control problems, as well as graphical games, will be reviewed. RL methods learn the solution to optimal control and game problems online and using measured data along the system trajectories. We discuss Q-learning and the integral RL algorithm as core algorithms for discrete-time (DT) and continuous-time (CT) systems, respectively. Moreover, we discuss a new direction of off-policy RL for both CT and DT systems. Finally, we review several applications.

536 citations