In this talk we take a data mining perspective and we discuss what (and how) can be learned from a social network and a database of traces of past propagations over the social network. This this talk I will discuss my work in collaboration with Children’s Hospital Los Angeles in applying machine learning to improve health care, particularly pediatric intensive care. Following finishing his Ph.D. in 2012, he joined the school of computer science at Carnegie Mellon university as a postdoctoral fellow, where he is working with Professor Brunskill on the subject of transfer of knowledge in sequential decision making problems. Medicine is becoming a “big data” discipline. Second, we study human motion and pose estimation. We will meet on Thursday January 23rd at 12pm in WCH215. These results were highlighted mainly under the context of EU FP7 Smartmuseum project. In the first part, I introduce an extension of the algebraic decision diagram (ADD) to continuous variables — termed the extended ADD (XADD) — to represent arbitrary piecewise functions over discrete and continuous variables and show how to efficiently compute elementary arithmetic operations, integrals, and maximization for these functions. The ability to learn is not only central to most aspects of intelligent behavior, but machine learning techniques have become key components of many software systems. This talk discusses a way to apply machine learning methods to network classifiers for networks that grow by adding cohorts. We model the interactions via a dynamic social network with joint edge and vertex dynamics. Suite 343 Winston Chung Hall Riverside, CA 92521, tel: (951) 827-2484 email: crisresearch@engr.ucr.edu. The Hume Center's Intelligent Systems Lab (ISL) conducts research to address critical areas of national security in three technological thrusts: 1) data science, machine learning, artificial intelligence, 2) … The organization's goal is to establish top AI research institutes, strengthen basic research and create a European PhD programme for AI. The Department of Mathematics (D-MATH) and the Department for Biosystems Science and Engineering located in Basel (D-BSSE) bring together statistics, machine learning, and biomedical research. In contrast, many real-world problems are characterized by the presence of multiple objectives to which the solution is not a single action but the set of actions optimal for all trade-offs between the objectives. I will go over the recent work on using copulas in two different settings. His research focuses on information-theoretic approaches to machine learning, computer vision, and signal processing. In this paper we propose a nonparametric survival model (GBMCI) that does not make explicit assumptions on hazard functions. Center for Machine Learning and Intelligent Systems Bren School of Information and Computer Science University of California, Irvine All faculty broadly interested in control, robotics, and machine intelligence are welcome to attend! Nima Dokoohaki holds a MSc (2007) in software engineering of distributed systems, and a PhD (2013) in information and communication technologies from KTH-Royal Institute of Technology, Sweden. CRIS faculty will meet on Wednesday 10/23/19 to discuss research activities and related proposal opportunities. MARLAN AND ROSEMARY BOURNS COLLEGE OF ENGINEERING, 900 University Ave. I will present a new computationally efficient probabilistic random field model, which can be best described as a “Perturb-and-MAP” generative process: We obtain a random sample from the whole field at once by first injecting noise into the system’s energy function, then solving an optimization problem to find the least energy configuration of the perturbed system. Another approach uses techniques that are designed to speed up sampling algorithms through faster exploration of the parameter space. She is a board member of the International Machine Learning Society, a former Machine Learning Journal Action Editor, Associate Editor for the ACM Transactions of Knowledge Discovery from Data, JAIR Associate Editor, and she has served on the AAAI Council. The Department of Mathematics (D-MATH) and the … The 3rd International Conference on Machine Learning and Intelligent Systems (MLIS 2021) will be held during November 8th-11th, 2021 in Xiamen, China. 2004. I will introduce graph steering, a framework that specifically targets inference under potentially sparse unary detection potentials and dense pairwise motion affinities – a particular characteristic of the video signal – in contrast to standard MRFs. Learning from prior tasks and transferring that experience to improve future performance is critical for building lifelong learning agents. We next address the question of differentially private statistical estimation. Lise Getoor is an Associate Professor in the Computer Science Department at the University of Maryland, College Park and University of Maryland Institute for Advanced Computer Studies. However, existing methods for solving such models assume there is only a single objective. in Symbolic Systems at Stanford University. But privacy-decisions are inherently difficult: they have delayed and uncertain repercussions that are difficult to trade-off with the possible immediate gratification of disclosure. Link to arXiv: http://arxiv.org/abs/1211.3759. Graph identification is the process of transforming an observed input network into an inferred output graph. These challenges are not unique to high energy physics, and there is the potential for great progress in collaboration between high energy physicists and machine learning experts. In this talk, we present a novel framework incorporating sparsity in different domains. 20000 . Her work has been funded by ARO, DARPA, IARPA, Google, jIBM, LLNL, Microsoft, NGA, NSF, Yahoo! Given the text of the articles and their citation graph, we show how to learn a probabilistic model to recover both the degree of topical influence of each article and the influence relationships between articles. By walking through a simple example using two M-best algorithms, Nilsson’98 and Yanover & Weiss’03, the audience will gain insights into the algorithms and their application to various graphical models. The presentation will cover the ongoing work at CE-CERT and will include plans for future research and proposals. Optimality in this case is with respect to a quadratic objective chosen for tractability, however, by explicitly modeling the stochastic nature of viewers seeing ads and the low-level ad slotting heuristic of the ad server, we derive sufficient conditions that tell us when our solution is also optimal with respect to two important practical objectives: minimizing the variance of the number of impressions served, and maximizing the number of unique individuals that are shown each ad campaign. To date, our ability to perform exact closed-form inference or optimization with continuous variables is largely limited to special well-behaved cases. Can we help users to balance the benefits and risks of information disclosure in a user-friendly manner, so that they can make good privacy decisions? In this talk I will describe a system that leverages accelerometers to recognize a particular involuntary gesture in babies that have been born preterm. With Perturb-and-MAP random fields we thus turn powerful deterministic energy minimization methods into efficient probabilistic random sampling algorithms that bypass costly Markov-chain Monte-Carlo (MCMC) and can generate in a fraction of a second independent random samples from mega-pixel sized images. Our framework incorporates sparse covariance and sparse precision estimations as special cases and thus introduces a richer class of high-dimensional models. We will meet on Thursday January 16th at 12pm in WCH215. We develop methods for building intelligent systems that learn, perceive and interact with the world, both autonomously and in collaboration with humans. Title: Machine Learning Towards Intelligent Systems: Applications, Challenges, and Opportunities. Behrooz Zarebavani, Foad Jafarinejad, Matin Hashemi, Saber Salehkaleybar, "cuPC: CUDA-based Parallel PC Algorithm for Causal Structure Learning on GPU", IEEE Transactions on Parallel and Distributed Systems (TPDS), Vol. 2011 The DT approach converts the problem of learning a deep architecture into the problem of learning many shallow architectures by providing learning targets for the deep layers. In this paper we study the problem of \textit{sequential transfer in online learning}, notably in the multi-armed bandit framework, where the objective is to minimize the cumulative regret over a sequence of tasks by incrementally transferring knowledge from prior tasks. We propose an efficient decomposition method based on a modification of the popular $\ell_1$-penalized maximum-likelihood estimator ($\ell_1$-MLE). This model family can incorporate dependence in vertex co-presence, found in many social settings (e.g., subgroup structure, selective pairing). Resulting recommendation algorithms have shown to increase accuracy of profiles, through incorporation of knowledge of items and users and diffusing them along the trust networks. Scott earned a PhD from the University of Toronto, an MS degree from Stanford, and a double BS degree from Carnegie Mellon. Firstly, the complexity of sensor planning is typically exponential in both the number of sensing actions and the planning time horizon. Noble is the recipient of an NSF CAREER award and is a Sloan Research Fellow. We introduce a new prior for use in Nonparametric Bayesian Hierarchical Clustering. He has worked on applications as varied as computer vision, sociology, game theory, decision theory, and computational biology. The European Laboratory for Learning and Intelligent Systems is a pan-European nonprofit organization for the promotion of artificial intelligence with a focus on machine learning. Prior to joining Purdue, he was a postdoctoral fellow with Alberta Ingenuity Centre for Machine Learning at the Department of Computing Science at the University of Alberta. Erfan Nozari received his B.Sc. The approach integrates the value of information discounted by resource expenditures over a rolling time horizon. First, we study people detection and tracking under persistent occlusions. I will first talk about two such biased algorithms: Stochastic Gradient Langevin Dynamics and its successor Stochastic Gradient Fisher Scoring, both of which use stochastic gradients estimated from mini-batches of data, allowing them to mix very fast. By using a greedy merge approach and some tricks to avoid unnecessary match operations, it is fast. In addition to being more elegant than sliding windows, we demonstrate experimentally on the PASCAL VOC 2010 dataset that our strategies evaluate two orders of magnitude fewer windows while achieving higher object detection performance. The smallest datasets are provided to test more computationally demanding machine learning algorithms (e.g., SVM). Fitting high-dimensional data involves a delicate tradeoff between faithful representation and the use of sparse models. The CAREER is NSF's most prestigious award in support of early-career faculty who have the... ECE professors, Amit Roy-Chowdhury and Ertem Tuncel, have received a new $500K grant from NSF’s Communications and Information Foundations program on information theoretic analysis of machine learning algorithms in computer vision. CS4780/CS5780: Machine Learning for Intelligent Systems [FALL 2018] (painting by Katherine Voor) Attention!! This talk is in two parts. You have to pass the (take home) Placement Exam in order to enroll. You have to pass the (take home) Placement Exam in order to enroll. Riemannian Manifold HMC (RMHMC) further improves HMC’s performance by exploiting the geometric properties of the parameter space. I will describe their mathematical foundations, learning and inference algorithms, and empirical evaluation, showing their power in terms of both accuracy and scalability. As other intelligent systems, applications in computer vision heavily rely on MAP hypotheses of probabilistic models. Professor Amit Roy-Chowdhury has been selected as a recipient of the 2020 ECE Distinguished Alumni Award from the University of Maryland (UMD). It automatically learns the information value of each feature from the data. Matthias will present an overview of the field and a technique that can utilize any available attributes including co-occurring entities, relations, and topics from unstructured text. I will describe the data collection, how the data do and do not fit into machine learning assumptions, and the current state and trends in medical data. We have clearly shown that trust clearly increases accuracy of suggestions predicted by system. However, our studies of social media indicate that most information epidemics fail to reach viral proportions. the identification of influential users, by targeting whom certain desirable marketing outcomes can be achieved, we provide an overview of some recent progresses in this area and discuss some open problems. Traditional survival models (e.g., the prevalent proportional hazards model) often impose strong assumptions on hazard functions, which describe how the risk of an event changes over time depending on covariates associated with each individual. Current research projects … He received a BS and MS in Electrical Engineering at the Univsersity of Florida in 1987 and 1989, respectively. Regression, Clustering, Causal-Discovery . The center, part of the University of Maryland Institute for Advanced Computer Studies, incentivizes faculty, students and visiting scholars to collaborate on the latest technologies and theoretical applications based in machine learning. This procedure gives users personalized “nudges” and personalized “justifications” based on a context-aware prediction of their privacy preferences. For such problems, we propose a novel Markov Chain Monte Carlo (MCMC) method that provides a general and computationally efficient framework for handling boundary conditions. His research group develops and applies statistical and machine learning techniques for modeling and understanding biological processes at the molecular level. The dominant visual search paradigm for object class detection is sliding windows. This in turn led to proposal of several ontologies for user and content characteristics modeling for improving indexing and retrieval of user content and profiles across the platform. We also introduced several trust-based recommendation techniques and frameworks capable of mining implicit and explicit trust across ratings networks taken from social and opinion web. Machine learning systems … The ability to learn is not only central to most aspects of intelligent behavior, but machine learning techniques have become key components of many software systems. We develop methods for building intelligent systems that learn, perceive and interact with … We focus on the application of finding and analyzing cars. We present an approach to detecting and analyzing the 3D configuration of objects in real-world images with heavy occlusion and clutter. Finally, we conducted an analysis to understand the clinical impact of this technique. He is currently a postdoctoral research assistant at software and computer systems (SCS) lab at KTH, where he focuses on big data and social informatics, particularly his research interests include trust, social network mining and analysis and recommender systems. We argue that lower risk estimates can often be obtained using gapproximateh MCMC methods that mix very fast (and thus lower the variance quickly) at the expense of a small bias in the stationary distribution. For examples, machine learning techniques are used to build search engines, to recommend movies, to understand natural language and images, and to build autonomous robots. Personalized systems often require a relevant amount of personal information to properly learn the preferences of the user. In order to test our system we recorded data from 10 babies admitted to the newborn intensive care unit at the UCI Medical Center. Kamalika Chaudhuri received a Bachelor of Technology degree in Computer Science and Engineering in 2002 from the Indian Institute of Technology, Kanpur, and a PhD in Computer Science from UC Berkeley in 2007. SRI’s Artificial Intelligence Center advances the most critical areas of AI and machine learning. (See Details below.) ... School of Informatics Center for Genomics and BioInformatics Indiana University. He received his doctorate in 2006, with a thesis focused on the integration of probabilistic and logical approaches to artificial intelligence. We characterize sufficient conditions for identifiability of the two models, \viz Markov and independence models. At ETH Zurich, the Department for Computer Science (D-INFK) supports significant activities in machine learning and computational intelligence. These gestures, known as cramped-synchronized general movements are highly correlated with a diagnosis of Cerebral Palsy. Finally, we will present simulation results and applications of deep architectures and DT algorithms to protein structure prediction. Time-Series, Domain-Theory . Several systemic research fields, which pose central questions on the understanding of complex systems, from recognition, to learning, to adaptation, are investigated within the Max Planck ETH … I introduce dynamics-aware network analysis methods and demonstrate that they can identify more meaningful structures in social media networks than popular alternatives. tel: (951) 827-2484 In multi-user augmented reality (AR), multiple users are able to view and interact with a common set of virtual objects. CRIS faculty in machine intelligence are known across the world for their research in computer vision, machine learning, data mining, quantitative modeling, and spatial databases. Details about her research are found at http://robotics.usc.edu/interaction/. Professor Hyoseung Kim received the National Science Foundation (NSF) Faculty Early Career Development Program (CAREER) award for his work on "Real-Time Scheduling of Intelligent Applications". This talk will describe Rephil, a system used widely within Google to identify the concepts or topics that underlie a given piece of text. We do so with a two-layer model; the first layer reasons about 2D appearance changes due to within-class variation and viewpoint. Decision Support Systems… Among applications of such estimators is a new robust approach to independent component analysis. Networks are interesting for machine learning because they grow in interesting ways. People readily ascribe intention, personality, and emotion to robots; SAR leverages this engagement stemming from non-contact social interaction involving speech, gesture, movement demonstration and imitation, and encouragement, to develop robots capable of monitoring, motivating, and sustaining user activities and improving human learning, training, performance and health outcomes. Application areas include signal-level approaches to multi-modal data fusion, signal and image processing in sensor networks, distributed inference under resource constraints, resource management in sensor networks, and analysis of seismic and radar images. Her research areas include machine learning, and reasoning under uncertainty; in addition she works in data management, visual analytics and social network analysis. I will discuss the structure of Rephil models, the distributed machine learning algorithm that we use to build these models from terabytes of data, and the Bayesian network inference algorithm that we use to identify concepts in new texts under tight time constraints. His research interests lie in the area of statistical machine learning, more specifically, computational methods for learning and inference for sparse models of high-dimensional data, and their applications to scientific problems. Statistical models with constrained probability distributions are abundant in machine learning. Padhraic Smyth, computer science professor in University of California--Irvine's Donald Bren School of Information and Computer Sciences and associate director for the college’s Center for … CS4780/CS5780: Machine Learning for Intelligent Systems [FALL 2018] (painting by Katherine Voor) Attention!! Mohammad Gheshlaghi Azar studied Electrical Engineering (control theory) at University of Tehran, Iran from 2003 till 2006. Our solution suggests explicit modeling of trust and embedding trust metrics and mechanisms within very fabric of user profiles. A conditional latent random field (CLRF) model is employed here to model the joint vertex evolution. It requires a combination of entity resolution, link prediction, and collective classification techniques. ... Journal of Machine Learning Research, 5. Integrating symbolic and statistical methods for testing intelligent systems: Applications to machine learning and computer vision Abstract: Embedded intelligent systems ranging from tiny implantable biomedical devices to large swarms of autonomous unmanned aerial systems … This project provides interesting links between work conducted at the UCR campus focused on…. For more information, please visit: http://users.cecs.anu.edu.au/~ssanner/. We will present three instances of steering. He first joined Google in 2000, after completing a B.S. 2004. degree in Human-Technology Interaction from Eindhoven University of Technology, The Netherlands, and his M.A. The main objective of this meeting is to brainstorm on, and possibly form teams for, the upcoming NSF NRI-2.0 initiative. His research interests are in probabilistic machine learning, computer vision, and multimodal perception. Survival analysis focuses on modeling and predicting the time to an event of interest. Padhraic Smyth is a Professor at the University of California, Irvine, in the Department of Computer Science with a joint appointment in Statistics, and is also Director of the Center for Machine Learning and Intelligent Systems … More about the Article: Prof. Erfan Nozari joins cris and data Department! Group develops and applies statistical and machine learning, for Engineering applications and! Inferred output graph cases, the complexity of sensor planning is typically exponential both... 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