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. 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. However, our studies of social media indicate that most information epidemics fail to reach viral proportions. Bayesian inference involving probability distributions confined to constrained domains could be quite challenging for commonly used sampling algorithms. Firstly, the complexity of sensor planning is typically exponential in both the number of sensing actions and the planning time horizon. These gestures, known as cramped-synchronized general movements are highly correlated with a diagnosis of Cerebral Palsy. We will meet on Thursday January 23rd at 12pm in WCH215. Finally, we will explore possible vector space and graph representations of the problem, alternative approaches that have been tried, and suggest future work based on reinforcement learning and active learning. The prior is constructed by marginalizing out the time information of Kingman’s coalescent, providing a prior over tree structures which we call the Time-Marginalized Coalescent (TMC). Medicine is becoming a “big data” discipline. At ETH Zurich, the Department for Computer Science (D-INFK) supports significant activities in machine learning and computational intelligence. ... Journal of Machine Learning Research, 5. Active approaches seek to manage sensing resources so as to maximize a utility function while incorporating constraints on resource expenditures. Erfan Nozari received his B.Sc. We use this model to propose candidate detections, which are then refined by our second layer, a 3D statistical model that reasons about 3D shape changes and 3D camera viewpoints. We focus on the application of finding and analyzing cars. 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. 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. Optimal uncertainty quantification is shown as a way to rigorously connect simulations with Big Data. His research is focused on developing new machine learning algorithms which apply to life-long and real-world learning and decision making problems. The discussion will be led by Prof. Matthew Barth on the topic of Smart Cities. 31, No. We segment hard to detect, fast moving body limbs from their surrounding clutter and match them against pose exemplars to detect body pose and improve body part motion estimates with kinematic constraints. Her research is currently developing robot-assisted therapies for children with autism spectrum disorders, stroke and traumatic brain injury survivors, and individuals with Alzheimer’s Disease and other forms of dementia. Reinforcement learning lies at the intersection between these … The bound can be shown to be sharp. We demonstrate a Markov model based technique for recognizing gestures from accelerometers that explicitly represent duration. At USC she has been awarded the Viterbi School of Engineering Service Award and Junior Research Award, the Provost’s Center for Interdisciplinary Research Fellowship, the Mellon Mentoring Award, the Academic Senate Distinguished Faculty Service Award, and a Remarkable Woman Award. One example is dynamic in-game advertising, in which ads served over the Internet are seamlessly integrated into the 3D environments of video games played on consoles like the XBox 360. This makes highly connected people less “susceptible” to infection and stops information spread. I will focus on tree-structured copulas in particular as they provide a convenient building block for such models and their applications to modeling of multi-site rainfall. A key challenge is resolving contradictions among different information granularities, such as detections and motion estimates in the case of false alarm detections or leaking motion affinities. His work focuses on privacy decision-making and recommender systems. 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. George Papandreou holds a Diploma (2003) and a Ph.D. (2009) in electrical and computer engineering from the National Technical University of Athens, Greece. The dominant visual search paradigm for object class detection is sliding windows. Our classifier works in the ERM (empirical loss minimization) framework, and includes privacy preserving logistic regression and privacy preserving support vector machines. Personalized systems often require a relevant amount of personal information to properly learn the preferences of the user. He received a BS and MS in Electrical Engineering at the Univsersity of Florida in 1987 and 1989, respectively. 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. 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. Professor Amit Roy-Chowdhury has been selected as a recipient of the 2020 ECE Distinguished Alumni Award from the University of Maryland (UMD). Center for Machine Learning and Intelligent Systems: About Citation Policy Donate a Data Set Contact. The 3rd International Conference on Machine Learning and Intelligent Systems (MLIS 2021) will be held during November 8th-11th, 2021 in Xiamen, China. 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. 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. We decompose the observed covariance matrix into a sparse Gaussian Markov model (with a sparse precision matrix) and a sparse independence model (with a sparse covariance matrix). (See Details below.) Standard tracking representations typically reason about temporal coherence of detected bodies and parts. Then I will present our current work on a new (biased) MCMC algorithm that uses a sequential hypothesis test to approximate the Metropolis-Hastings test, allowing us to accept/reject samples with high confidence using only a fraction of the data required for the exact test. You have to pass the (take home) Placement Exam in order to enroll. 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. This online FDP will start from the 1st of December 2020 and will end on 5th December 2020. In order to create intelligent machines, we should endow them with features connecting areas like machine learning and optimal control. In some cases, the computational overhead for solving implicit equations undermines RMHMC’s benefits. I will go over the recent work on using copulas in two different settings. Katerina Fragkiadaki is a Ph.D. student in Computer and Information Science in the University of Pennsylvania. Bart Knijnenburg is a Ph.D candidate in Informatics at the University of California, Irvine. Furthermore, recently developed methods [Fisher III et al., 2009] have been shown to be useful for estimating these quantities in complex signal models. The result of this learning process is a Rephil model — a giant Bayesian network with concepts as nodes. In this talk, we approach thecrowdsourcing problem by transforming it into a standard inference problem in graphical models, and apply powerful inference algorithms such as belief propagation (BP). Suite 343 Winston Chung Hall Riverside, CA 92521, tel: (951) 827-2484 email: crisresearch@engr.ucr.edu. CENTER FOR RESEARCH IN INTELLIGENT SYSTEMS. Finally, we will present simulation results and applications of deep architectures and DT algorithms to protein structure prediction. 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. when IDs such as SSN are not available. 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. To date, our ability to perform exact closed-form inference or optimization with continuous variables is largely limited to special well-behaved cases. Such approaches are complicated by several factors. Statistical models with constrained probability distributions are abundant in machine learning. Highlighted results start from modeling of adaptive user profiles incorporating users taste, trust and privacy preferences. In collaboration with the Surgical Planning Lab at Brigham and Women’s Hospital, he is developing nonparametric approaches to image registration and functional imaging. 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. Our output is a tree-mixture model which serves as a good approximation to the underlying graphical model mixture. He received his PhD from MIT in 2001 and his bachelor degree from Stanford in 1996. 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. This talk is about trends in computing technology that are leading to exascale-class systems for both scientific simulations and data reduction. Graph identification is the process of transforming an observed input network into an inferred output graph. 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. Such measures are appealing due to a variety of useful properties. The mission of CIM is to excel in the field of intelligent systems, stressing basic research, technology development and education. It … Networks are interesting for machine learning because they grow in interesting ways. The smallest datasets are provided to test more computationally demanding machine learning algorithms (e.g., SVM). She is a recipient of an NSF Career Award and was awarded a National Physical Sciences Consortium Fellowship. We apply this approach to both synthetic data and a classic social network data set involving interactions among windsurfers on a Southern California beach. 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 concepts used by Rephil are not pre-specified; instead, they are derived by an unsupervised learning algorithm running on massive amounts of text. ... Journal of Machine Learning Research, 5. A conditional latent random field (CLRF) model is employed here to model the joint vertex evolution. Entity disambiguation (a.k.a. We will show how these analyses lead to a new general family of learning algorithms for deep architectures–the deep target (DT) algorithms. Professor Chen received the NSF CAREER award for her work on "Networked Multi-User Augmented Reality for Mobile Devices". The presentation will cover the ongoing work at CE-CERT and will include plans for future research and proposals. 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". We show that psychological factors fundamentally distinguish social contagion from viral contagion. In this talk we will provide a brief historical overview of deep architectures from their 1950s origins to today. By way of example, inference in distributed sensor networks presents a fundamental trade-off between the utility in a distributed set of measurements versus the resources expended to acquire them, fuse them into a model of uncertainty, and then transmit the resulting model. CRIS faculty will meet on Wednesday 10/23/19 to discuss research activities and related proposal opportunities. We have clearly shown that trust clearly increases accuracy of suggestions predicted by system. We demonstrate this on an example model for density estimation and show the TMC achieves competitive experimental results. I introduce dynamics-aware network analysis methods and demonstrate that they can identify more meaningful structures in social media networks than popular alternatives. 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. We characterize sufficient conditions for identifiability of the two models, \viz Markov and independence models. Our strategies adapt to the class being searched and to the content of a particular test image, exploiting context as the statistical relation between the appearance of a window and its location relative to the object, as observed in the training set. In this talk, I will describe computational and statistical methods that we have developed and applied to a variety of genomes, with the goal of characterizing genome architecture and function. In this context, this paper introduces topical influence, a quantitative measure of the extent to which an article tends to spread its topics to the articles that cite it. 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. Description. This model family can incorporate dependence in vertex co-presence, found in many social settings (e.g., subgroup structure, selective pairing). To overcome this limitation, we developed a novel M-best algorithm which incorporates non-maximal suppression into Yanover & Weiss’s algorithm. First, we study people detection and tracking under persistent occlusions. I will overview two approaches to graph identification: 1) coupled conditional classifiers (C^3), and 2) probabilistic soft logic (PSL). 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". Enter copulas, a statistical approach which separates the marginal distributions for random variables from their dependence structure. This is joint work with Georgios Papachristoudous, Jason L. Williams, & Michael Siracusa. We do this by embedding an Erlang-Cox state transition model, which has been shown to accurately represent the first three moments of a general distribution, within a Dynamic Bayesian Network (DBN). 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. Unfortunately, computing coordinated behavior is computationally expensive because the number of possible joint actions grows exponentially in the number of agents. Irvine-based Cylance Inc. has donated $50,000 to computer science professors Alex Ihler and Padhraic Smyth to support the activities of UCI’s Center for Machine Learning & Intelligent Systems. 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Received her diplomat in Computer and information Retrieval for modeling and understanding processes! A Markov model based technique for recognizing gestures from accelerometers that explicitly represent duration kamalika ’ s Mechanical Turk become! Experiments validate these results and also demonstrate that they can identify more meaningful structures in social media indicate that information! Is becoming a “ Big data information spread presentation, I will talk about a more work!