We introduce a novel approach for fitting such CLRF models which leverages on the recent results for learning latent tree models and combines it with a parametric model for covariate effects and a logistic model for edge prediction (i.e. Center for Continuing Education & Department of Computer Science and Engg., NIT Warangal is organizing an online One Week FACULTY DEVELOPMENT PROGRAMME (FDP) On "Machine Learning for Intelligent Systems". ... Machine learning (ML) provides a mechanism for humans to process large amounts … Entity disambiguation (a.k.a. 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. In the first part, I will provide a tutorial motivating and introducing M-best algorithms particularly for those who are new to these approaches. With more careful choices, we show that our simple BP performs surprisingly well on both simulated and real-world datasets, competitive with state-of-the-art algorithms based on more complicated modeling assumptions. A year later, he entered the Computer Science Ph.D. program at U.C. ... P. Cortez and P. Rita. 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. Professor Hamed Mohsenian-Rad is named as Fellow of the Institute of Electrical and Electronics Engineers (IEEE). We show that by treating instantaneous machine learning classification values as observations and explicitly modeling duration, we improve the recognition of Cramped Syn- chronized General Movements, a motion highly correlated with an eventual diagnosis of Cerebral Palsy. 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. MARLAN AND ROSEMARY BOURNS COLLEGE OF ENGINEERING, 900 University Ave. Deep architectures are important for machine learning, for engineering applications, and for understanding the brain. For purposes of informing and programming artificial intelligence systems, real-world data on biologic and biosimilar use and patient outcomes would be drawn from multiple sources, such as hospital systems and payers. By using a greedy merge approach and some tricks to avoid unnecessary match operations, it is fast. However, predicting a single (most probable) hypothesis is often suboptimal when training data is noisy or underlying model is complex. Bayesian posterior sampling can be painfully slow on very large datasets, since traditional MCMC methods such as Hybrid Monte Carlo are designed to be asymptotically unbiased and require processing the entire dataset to generate each sample. Machine learning algorithms increasingly work with sensitive information on individuals, and hence the problem of privacy-preserving data analysis — how to design data analysis algorithms that operate on the sensitive data of individuals while still guaranteeing the privacy of individuals in the data– has achieved great practical importance. 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 … Acknowledgments: This is joint work with Zahra Zamani & Ehsan Abbasnejad (Australian National University), Karina Valdivia Delgado & Leliane Nunes de Barros (University of Sao Paulo), and Simon Fang (M.I.T.). First, we address the problem of privacy-preserving classification, and present an efficient classifier which is private in the differential privacy model of Dwork et al. We characterize sufficient conditions for identifiability of the two models, \viz Markov and independence models. ... School of Informatics Center for Genomics and BioInformatics Indiana University. We develop methods for building intelligent systems that learn, perceive and interact with … Details about her research are found at http://robotics.usc.edu/interaction/. Second, we study human motion and pose estimation. Data Science and Intelligent Systems Concepts and techniques from data science and intelligent computing are being rapidly integrated into many areas of Electrical and Computer Engineering (ECE), in particular by exploiting new developments in machine learning. Maja Mataric is professor and Chan Soon-Shiong chair in Computer Science, Neuroscience, and Pediatrics at the University of Southern California, founding director of the USC Center for Robotics and Embedded Systems (cres.usc.edu), co-director of the USC Robotics Research Lab (robotics.usc.edu) and Vice Dean for Research in the USC Viterbi School of Engineering. This overfitting is greatly reduced by randomly omitting half of the feature detectors on each training case. 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. Probabilistic Bayesian methods such as Markov random fields are well suited for describing ambiguous images and videos, providing us with the natural conceptual framework for representing the uncertainty in interpreting them and automatically learning model parameters from training data. The mission of CIM is to excel in the field of intelligent systems, stressing basic research, technology development and education. Experimental results show that our method improves RMHMC’s overall computational efficiency. Approximate approaches (c.f. Rephil determines, for example, that “apple pie” relates to some of the same concepts as “chocolate cake”, but has little in common with “apple ipod”. I will describe their mathematical foundations, learning and inference algorithms, and empirical evaluation, showing their power in terms of both accuracy and scalability. The robot’s physical embodiment is at the heart of SAR’s effectiveness, as it leverages the inherently human tendency to engage with lifelike (but not necessarily human-like or otherwise biomimetic) social behavior. 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. 3, March 2020.Saeed Saadatnejad, Mohammadhosein Oveisi, Matin Hashemi, "LSTM-Based ECG Classification for Continuous Monitoring on … Based on joint work with Claire Monteleoni (George Washington University), Anand Sarwate (TTI Chicago), and Daniel Hsu (Microsoft Research). ... Journal of Machine Learning Research, 5. Motivated by this overview, we will study and prove several theorems regarding deep architectures and one of their main ingredients–autoencoder circuits–in particular in the unrestricted Boolean and unrestricted probabilistic cases. Professor Chen received the NSF CAREER award for her work on "Networked Multi-User Augmented Reality for Mobile Devices". Current research projects led by the members of this group include: Automatic detection of fake news Reinforcement learning and deep networks Since 2009 he has been a postdoctoral research scholar at the University of California, Los Angeles. Moreover, it can incorporate the effect of covariates (e.g. In this talk, I will present novel tracking representations that allow to track people and their body pose by exploiting information at multiple granularities when available, whole body, parts or pixel-wise motion correspondences and their segmentations. 900 University Ave. Suite 343 Winston Chung Hall Riverside, CA 92521 . We introduce a new prior for use in Nonparametric Bayesian Hierarchical Clustering. Enter copulas, a statistical approach which separates the marginal distributions for random variables from their dependence structure. Katerina Fragkiadaki is a Ph.D. student in Computer and Information Science in the University of Pennsylvania. He has contributed to Google production systems for spelling correction, transliteration, and semantic modeling of text. Active approaches seek to manage sensing resources so as to maximize a utility function while incorporating constraints on resource expenditures. Such approaches are complicated by several factors. Link to arXiv: http://arxiv.org/abs/1211.3759. Although results in supervised and reinforcement learning show that transfer may significantly improve the learning performance, most of the literature on transfer is focused on batch learning tasks. A Data-Driven Approach to Predict the Success of Bank Telemarketing. 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. Such measures are appealing due to a variety of useful properties. We show that psychological factors fundamentally distinguish social contagion from viral contagion. Simulation results demonstrate that the resulting algorithm can provide similar estimation performance to that of greedy and myopic methods for a fraction of the resource expenditures. 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’ll begin with a brief overview of SRL, and discuss its relation to network analysis, extraction, and alignment. He received a BS and MS in Electrical Engineering at the Univsersity of Florida in 1987 and 1989, respectively. In collaborative multi-agent systems, teams of agents must coordinate their behavior in order to maximize their common utility. Too often, sparsity assumptions on the fitted model are too restrictive to provide a faithful representation of the observed data. MLIS conference is convened annually to provide a platform for knowledge exchange of the most recent scientific and technological advances in the field of machine learning and intelligent systems, and to … Our model trains an ensemble of regression trees by the gradient boosting machine to optimize a smoothed approximation of the concordance index, which is one of the most widely used metrics in survival model evaluation. This talk is about trends in computing technology that are leading to exascale-class systems for both scientific simulations and data reduction. However, existing methods for solving such models assume there is only a single objective. 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. Such systems are useful, not only for addressing tasks that are inherently distributed, but also for decomposing tasks that would otherwise be too complex to solve. CRIS faculty will meet on Wednesday 11/13/19 to discuss the potential use of high resolution satellite data and other GIS data with AI models, as well as explore ideas on using the geographical information rather than treating this data as mere images. A variety of molecular biology technologies have recently made it clear that the function of the genome in vivo is determined both by the linear sequences of nucleotides along the chromosome and the three-dimensional conformation of chromosomes within the nucleus. Finally, one consequence of this algorithmic development are new fundamental performance bounds for information gathering systems [Williams et al., 2007b] which show that, under mild assumptions, optimal (though intractable) planning schemes can yield no better than twice the performance of greedy methods for certain choices of information measures. These systems are networks of interacting elements such as constellation... Prof. Fabio Pasqualetti has been awarded a 2020 Young Investigator Award from the Air Force Office of Scientific Research! The honor is conferred by the IEEE Board of Directors upon a person with an extraordinary record of accomplishments in any of the IEEE... Prof. Samet Oymak and his collaborators Necmiye Ozay, Dimitra Panagou (University of Michigan) and Sze Zheng Yong (Arizona State University) are awarded1.2M NSF grant to improve Cyber-Physical System safety. Within the machine learning community, there is a growing interest in learning structured models from input data that is itself structured, an area often referred to as statistical relational learning (SRL). At ETH Zurich, the Department for Computer Science (D-INFK) supports significant activities in machine learning and computational intelligence. Machine learning systems … This has in turn allowed information systems to consume and understand this extra knowledge in order to improve interaction and collaboration among individuals and system. We show that the resulting transformation is equivalent to transforming Riemannian Hamilton dynamics to Lagrangian dynamics. Quick Speaker Bio: Scott Sanner is a Senior Researcher in the Machine Learning Group at NICTA Canberra and an Adjunct Fellow at the Australian National University, having joined both in 2007. 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. Over the past decade, improvements in information technology have led to the development of new media and new forms of advertising. Machine learning plays an increasingly important role in computer vision, allowing us to build complex vision systems that better capture the properties of images. In this talk we will provide a brief historical overview of deep architectures from their 1950s origins to today. I will use two current projects to drive the discussion: (1) monitoring of blood CO2 and pH levels for patients on mechanical ventilation and (2) predicting acute kidney injury and identifying potential causes. We focus on the application of finding and analyzing cars. For purposes of informing and programming artificial intelligence systems, real-world data on biologic and biosimilar use and patient outcomes would be drawn from multiple sources, such as hospital systems and payers. In this presentation, I will discuss the use of information measures for resource allocation in distributed sensing systems. The following research groups are involved: Intelligent Systems and Robotics We introduce a novel bandit algorithm based on a method-of-moments approach for the estimation of the possible tasks and derive regret bounds for it. He received his doctorate in 2006, with a thesis focused on the integration of probabilistic and logical approaches to artificial intelligence. Personalized systems often require a relevant amount of personal information to properly learn the preferences of the user. I will illustrate these ideas with applications in image inpainting and deblurring, image segmentation, and scene labeling, showing how the Perturb-and-MAP model makes large-scale Bayesian inference computationally tractable for challenging computer vision problems. Center for Machine Learning and Intelligent Systems Bren School of Information and Computer Science University of California, Irvine I will go over the recent work on using copulas in two different settings. We propose a novel method for estimating the mixture components with provable guarantees. Our mission is to train cohorts with both theoretical, practical and systems skills in autonomous systems - comprising machine learning, robotics, sensor systems and verification- and a deep understanding of the cross-disciplinary … I will discuss how the Perturb-and-MAP model relates to the standard Gibbs MRF and how it can be used in conjunction with other approximate Bayesian computation techniques. 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? Riverside, CA 92521, 900 University Ave. Center for Machine Learning and Intelligent Systems Bren School of Information and Computer Science University of California, Irvine This makes highly connected people less “susceptible” to infection and stops information spread. CRIS faculty will meet on Wednesday 10/9/19 to discuss the Center's activities and opportunities. Erfan Nozari received his B.Sc. I will describe the nature of the physics problem, the challenges we face in analyzing the data, the previous successes and failures of some ML techniques, and the open challenges. Dr. William Stafford Noble is Professor in the Department of Genome Sciences in the School of Medicine at the University of Washington where he has a joint appointment in the Department of Computer Science and Engineering in the College of Engineering. The UCI Machine Learning Repository is a collection of databases, domain theories, and data generators that are used by the machine learning community for the empirical analysis of machine learning algorithms. One approach uses geometrically motivated methods that explore the parameter space more efficiency by exploiting its geometric properties. Our results show that the proposed method can provide a natural and efficient framework for handling several types of constraints on target distributions. 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. Standard tracking representations typically reason about temporal coherence of detected bodies and parts. For more information, please visit: http://users.cecs.anu.edu.au/~ssanner/. Title: Machine Learning Towards Intelligent Systems: Applications, Challenges, and Opportunities. At the same time focusing on automated distributed management of profiles, we showed that coverage of system can be increased effectively, surpassing comparable state of art techniques. She received her diplomat in Computer Engineering from the National Technical University of Athens. The main hurdle for a direct application of traditional M-best algorithms to computer vision applications is a lack of diversity : the second best hypothesis is only one-pixel off from the best one. We show that our classifier is private, provide analytical bounds on the sample requirement of our classifier, and evaluate it on real data. In this talk, I will discuss our recent attempts to develop a new class of scalable computational methods to facilitate the application of Bayesian statistics in data-intensive scientific problems. As other intelligent systems, applications in computer vision heavily rely on MAP hypotheses of probabilistic models. SRI’s Artificial Intelligence Center advances the most critical areas of AI and machine learning. 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. 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. ... Machine learning (ML) provides a mechanism for humans to process large amounts of data, gain insights about the behavior of the data, and make more informed decision based on the resulting analysis. Finally, I will show how we can learn certainty of detections under various pose and motion specific contexts, and use such certainty during steering for jointly inferring multi-frame body pose and video segmentation. She received her PhD from Stanford University, her Master’s degree from University of California, Berkeley, and her undergraduate degree from University of California, Santa Barbara. and others. The concepts used by Rephil are not pre-specified; instead, they are derived by an unsupervised learning algorithm running on massive amounts of text. However, privacy surveys demonstrate that Internet users want to limit the collection and dissemination of their personal data. Consequently, exploiting loose couplings between agents, as expressed in graphical models, is key to rendering such decision making efficient. Instead, each neuron learns to detect a feature that is generally helpful for producing the correct answer given the combinatorially large variety of internal contexts in which it must operate. 900 University Ave. Suite 343 Winston Chung Hall Riverside, CA 92521 . Socially assistive robotics (SAR) is a new field of intelligent robotics that focuses on developing machines capable of assisting users through social rather than physical interaction. Description. 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. 30000 . Brian Milch is a software engineer at Google’s Los Angeles office. She is a recipient of an NSF Career Award and was awarded a National Physical Sciences Consortium Fellowship. Random gdropouth gives big improvements on many benchmark tasks and sets new records for speech and object recognition.” This seminar will present a mathematical analysis of the dropout algorithm and its intriguing properties. The Max Planck Institute for Intelligent Systems and Eidgenoessische Technische Hochschule (ETH) Zurich have recently joined forces in order to master this scientific challenge by forming a unique Max Planck ETH Center for Learning Systems. Additional on-line computable bounds, often tighter in practice, are presented as well. After a stint as a postdoctoral researcher at the Information Theory and Applications Center at UC San Diego, she joined the CSE department at UCSD as an assistant professor in 2010. These results were highlighted mainly under the context of EU FP7 Smartmuseum project. Human-robot interaction (HRI) for SAR is a growing multifaceted research area at the intersection of engineering, health sciences, neuroscience, social, and cognitive sciences. It is used by students, educators, and researchers all over the world as a primary source of machine learning data sets. Crowdsourcing on platforms like Amazon’s Mechanical Turk have become a popular paradigm for labeling large datasets. Machine Learning for Intelligent Systems (01 – 12 – 2020 to 05 –12 – 2020) Organized by Center for Continuing Education & Department of Computer Science and Engg., NIT Warangal About the NIT … The discussion will be led by Prof. Matthew Barth on the topic of Smart Cities. 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. Intelligent systems and machines are capable of adapting their behaviour by sensing and interpreting their environment, making decisions and plans, and then carrying out those plans using physical actions. I will overview two approaches to graph identification: 1) coupled conditional classifiers (C^3), and 2) probabilistic soft logic (PSL). It is the first Center … 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. Using 26 weeks of historical data from Massive, we compare our algorithm’s ad slotting performance with Massive’s legacy algorithm over a rolling horizon, and find that we reduce make-good costs by 80-87%, reserve more premium ad slots for future sales, increase the number of unique individuals that see each ad campaign, and deliver ads in a smoother, more consistent fashion over time. Kamalika’s research is on the design and analysis of machine-learning algorithms and their applications. Among applications of such estimators is a new robust approach to independent component analysis. Our experiments validate these results and also demonstrate that our models have better inference accuracy under simple algorithms such as loopy belief propagation. degree in Human- Computer Interaction from Carnegie Mellon University. A patient joins a network of disease infection because they are in contact with someone who has been infected. 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. Dropout is a new learning algorithm recently introduced by Hinton and his group. His research is focused on developing new machine learning algorithms which apply to life-long and real-world learning and decision making problems. Some examples include regression models with norm constraints (e.g., Lasso), probit models, many copula models, and Latent Dirichlet Allocation (LDA) models. We next address the question of differentially private statistical estimation. 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Is to realize increasingly important notion of privacy preferences system we recorded data 10! Accuracy under simple algorithms such as loopy belief propagation exascale-class systems for spelling correction, transliteration, and BS... Specific topic will be announced at a later time MIT before returning to Google production systems for correction... Approaches these problems with methods from Bayesian Statistics, signal processing significant challenges. Profile data taken from on-line social networks bridge is built because there major... General movements are highly correlated with a large-scale breast cancer prognosis dataset activities!, computing coordinated behavior is computationally expensive because the number of possible joint actions grows in... Career award for her work on using copulas in two different settings the observed data a tutorial and. Of Maryland ( UMD ) born preterm, high-energy physics, and multimodal.... 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