AI in the UK: Ready, Willing and Able?
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Risk Bounds for Classification and Regression Rules That Interpolate
Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate Mikhail Belkin Daniel Hsu Partha P. Mitra The Ohio State University Columbia University Cold Spring Harbor Laboratory Abstract Many modern machine learning models are trained to achieve zero or near-zero training error in order to obtain near-optimal (but non-zero) test error. This phe- nomenon of strong generalization performance for “overfitted” / interpolated clas- sifiers appears to be ubiquitous in high-dimensional data, having been observed in deep networks, kernel machines, boosting and random forests. Their performance is consistently robust even when the data contain large amounts of label noise. Very little theory is available to explain these observations. The vast majority of theoretical analyses of generalization allows for interpolation only when there is little or no label noise. This paper takes a step toward a theoretical foundation for interpolated classifiers by analyzing local interpolating schemes, including geometric simplicial interpolation algorithm and singularly weighted k-nearest neighbor schemes. Consistency or near-consistency is proved for these schemes in classification and regression problems. Moreover, the nearest neighbor schemes exhibit optimal rates under some standard statistical assumptions. Finally, this paper suggests a way to explain the phenomenon of adversarial ex- amples, which are seemingly ubiquitous in modern machine learning, and also discusses some connections to kernel machines and random forests in the interpo- lated regime. 1 Introduction The central problem of supervised inference is to predict labels of unseen data points from a set of labeled training data. The literature on this subject is vast, ranging from classical parametric and non-parametric statistics [48, 49] to more recent machine learning methods, such as kernel machines [39], boosting [36], random forests [15], and deep neural networks [25]. -
Probabilistic Circuits: Representations, Inference, Learning and Theory
Inference Probabilistic Representations Learning Circuits Theory Antonio Vergari YooJung Choi University of California, Los Angeles University of California, Los Angeles Robert Peharz Guy Van den Broeck TU Eindhoven University of California, Los Angeles January 7th, 2021 - IJCAI-PRICAI 2020 Fully factorized NaiveBayes AndOrGraphs PDGs Trees PSDDs CNets LTMs SPNs NADEs Thin Junction Trees ACs MADEs MAFs VAEs DPPs FVSBNs TACs IAFs NAFs RAEs Mixtures BNs NICE FGs GANs RealNVP MNs The Alphabet Soup of probabilistic models 2/153 Fully factorized NaiveBayes AndOrGraphs PDGs Trees PSDDs CNets LTMs SPNs NADEs Thin Junction Trees ACs MADEs MAFs VAEs DPPs FVSBNs TACs IAFs NAFs RAEs Mixtures BNs NICE FGs GANs RealNVP MNs Intractable and tractable models 3/153 Fully factorized NaiveBayes AndOrGraphs PDGs Trees PSDDs CNets LTMs SPNs NADEs Thin Junction Trees ACs MADEs MAFs VAEs DPPs FVSBNs TACs IAFs NAFs RAEs Mixtures BNs NICE FGs GANs RealNVP MNs tractability is a spectrum 4/153 Fully factorized NaiveBayes AndOrGraphs PDGs Trees PSDDs CNets LTMs SPNs NADEs Thin Junction Trees ACs MADEs MAFs VAEs DPPs FVSBNs TACs IAFs NAFs RAEs Mixtures BNs NICE FGs GANs RealNVP MNs Expressive models without compromises 5/153 Fully factorized NaiveBayes AndOrGraphs PDGs Trees PSDDs CNets LTMs SPNs NADEs Thin Junction Trees ACs MADEs MAFs VAEs DPPs FVSBNs TACs IAFs NAFs RAEs Mixtures BNs NICE FGs GANs RealNVP MNs a unifying framework for tractable models 6/153 Why tractable inference? or expressiveness vs tractability 7/153 Why tractable inference? or expressiveness -
Risk Bounds for Classification and Regression Rules That Interpolate
Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate Mikhail Belkin1, Daniel Hsu2, and Partha P. Mitra3 1The Ohio State University, Columbus, OH 2Columbia University, New York, NY 3Cold Spring Harbor Laboratory, Cold Spring Harbor, NY October 29, 2018 Abstract Many modern machine learning models are trained to achieve zero or near-zero training error in order to obtain near-optimal (but non-zero) test error. This phenomenon of strong generalization performance for “overfitted” / interpolated classifiers appears to be ubiquitous in high-dimensional data, having been observed in deep networks, kernel machines, boosting and random forests. Their performance is consistently robust even when the data contain large amounts of label noise. Very little theory is available to explain these observations. The vast majority of theoretical analyses of generalization allows for interpolation only when there is little or no label noise. This paper takes a step toward a theoretical foundation for interpolated classifiers by analyzing local interpolating schemes, including geometric simplicial interpolation algorithm and singularly weighted k-nearest neighbor schemes. Consistency or near-consistency is proved for these schemes in classification and regression problems. Moreover, the nearest neighbor schemes exhibit optimal rates under some standard statistical assumptions. Finally, this paper suggests a way to explain the phenomenon of adversarial examples, which are seemingly ubiquitous in modern machine learning, and also discusses some connections to kernel machines and random forests in the interpolated regime. 1 Introduction arXiv:1806.05161v3 [stat.ML] 26 Oct 2018 The central problem of supervised inference is to predict labels of unseen data points from a set of labeled training data. -
Letters to the Editor
Articles Letters to the Editor Research Priorities for is a product of human intelligence; we puter scientists, innovators, entrepre - cannot predict what we might achieve neurs, statisti cians, journalists, engi - Robust and Beneficial when this intelligence is magnified by neers, authors, professors, teachers, stu - Artificial Intelligence: the tools AI may provide, but the eradi - dents, CEOs, economists, developers, An Open Letter cation of disease and poverty are not philosophers, artists, futurists, physi - unfathomable. Because of the great cists, filmmakers, health-care profes - rtificial intelligence (AI) research potential of AI, it is important to sionals, research analysts, and members Ahas explored a variety of problems research how to reap its benefits while of many other fields. The earliest signa - and approaches since its inception, but avoiding potential pitfalls. tories follow, reproduced in order and as for the last 20 years or so has been The progress in AI research makes it they signed. For the complete list, see focused on the problems surrounding timely to focus research not only on tinyurl.com/ailetter. - ed. the construction of intelligent agents making AI more capable, but also on Stuart Russell, Berkeley, Professor of Com - — systems that perceive and act in maximizing the societal benefit of AI. puter Science, director of the Center for some environment. In this context, Such considerations motivated the “intelligence” is related to statistical Intelligent Systems, and coauthor of the AAAI 2008–09 Presidential Panel on standard textbook Artificial Intelligence: a and economic notions of rationality — Long-Term AI Futures and other proj - Modern Approach colloquially, the ability to make good ects on AI impacts, and constitute a sig - Tom Dietterich, Oregon State, President of decisions, plans, or inferences. -
Part I Officers in Institutions Placed Under the Supervision of the General Board
2 OFFICERS NUMBER–MICHAELMAS TERM 2009 [SPECIAL NO.7 PART I Chancellor: H.R.H. The Prince PHILIP, Duke of Edinburgh, T Vice-Chancellor: 2003, Prof. ALISON FETTES RICHARD, N, 2010 Deputy Vice-Chancellors for 2009–2010: Dame SANDRA DAWSON, SID,ATHENE DONALD, R,GORDON JOHNSON, W,STUART LAING, CC,DAVID DUNCAN ROBINSON, M,JEREMY KEITH MORRIS SANDERS, SE, SARAH LAETITIA SQUIRE, HH, the Pro-Vice-Chancellors Pro-Vice-Chancellors: 2004, ANDREW DAVID CLIFF, CHR, 31 Dec. 2009 2004, IAN MALCOLM LESLIE, CHR, 31 Dec. 2009 2008, JOHN MARTIN RALLISON, T, 30 Sept. 2011 2004, KATHARINE BRIDGET PRETTY, HO, 31 Dec. 2009 2009, STEPHEN JOHN YOUNG, EM, 31 July 2012 High Steward: 2001, Dame BRIDGET OGILVIE, G Deputy High Steward: 2009, ANNE MARY LONSDALE, NH Commissary: 2002, The Rt Hon. Lord MACKAY OF CLASHFERN, T Proctors for 2009–2010: JEREMY LLOYD CADDICK, EM LINDSAY ANNE YATES, JN Deputy Proctors for MARGARET ANN GUITE, G 2009–2010: PAUL DUNCAN BEATTIE, CC Orator: 2008, RUPERT THOMPSON, SE Registrary: 2007, JONATHAN WILLIAM NICHOLLS, EM Librarian: 2009, ANNE JARVIS, W Acting Deputy Librarian: 2009, SUSANNE MEHRER Director of the Fitzwilliam Museum and Marlay Curator: 2008, TIMOTHY FAULKNER POTTS, CL Director of Development and Alumni Relations: 2002, PETER LAWSON AGAR, SE Esquire Bedells: 2003, NICOLA HARDY, JE 2009, ROGER DERRICK GREEVES, CL University Advocate: 2004, PHILIPPA JANE ROGERSON, CAI, 2010 Deputy University Advocates: 2007, ROSAMUND ELLEN THORNTON, EM, 2010 2006, CHRISTOPHER FORBES FORSYTH, R, 2010 OFFICERS IN INSTITUTIONS PLACED UNDER THE SUPERVISION OF THE GENERAL BOARD PROFESSORS Accounting 2003 GEOFFREY MEEKS, DAR Active Tectonics 2002 JAMES ANTHONY JACKSON, Q Aeronautical Engineering, Francis Mond 1996 WILLIAM NICHOLAS DAWES, CHU Aerothermal Technology 2000 HOWARD PETER HODSON, G Algebra 2003 JAN SAXL, CAI Algebraic Geometry (2000) 2000 NICHOLAS IAN SHEPHERD-BARRON, T Algebraic Geometry (2001) 2001 PELHAM MARK HEDLEY WILSON, T American History, Paul Mellon 1992 ANTHONY JOHN BADGER, CL American History and Institutions, Pitt 2009 NANCY A. -
Machine Learning Conference Report
Part of the conference series Breakthrough science and technologies Transforming our future Machine learning Conference report Machine learning report – Conference report 1 Introduction On 22 May 2015, the Royal Society hosted a unique, high level conference on the subject of machine learning. The conference brought together scientists, technologists and experts from across academia, industry and government to learn about the state-of-the-art in machine learning from world and UK experts. The presentations covered four main areas of machine learning: cutting-edge developments in software, the underpinning hardware, current applications and future social and economic impacts. This conference is the first in a new series organised This report is not a verbatim record, but summarises by the Royal Society, entitled Breakthrough Science the discussions that took place during the day and the and Technologies: Transforming our Future, which will key points raised. Comments and recommendations address the major scientific and technical challenges reflect the views and opinions of the speakers and of the next decade. Each conference will focus on one not necessarily that of the Royal Society. Full versions technology and cover key issues including the current of the presentations can be found on our website at: state of the UK industry sector, future direction of royalsociety.org/events/2015/05/breakthrough-science- research and the wider social and economic implications. technologies-machine-learning The conference series is being organised through the Royal Society’s Science and Industry programme, which demonstrates our commitment to reintegrate science and industry at the Society and to promote science and its value, build relationships and foster translation. -
AAAI News AAAI News
AAAI News AAAI News Winter News from the Association for the Advancement of Artificial Intelligence AAAI-18 Registration Student Activities looking for internships or jobs to meet with representatives from over 30 com - As part of its outreach to students, Is Open! panies and academia in an informal AAAI-18 will continue several special "meet-and-greet" atmosphere. If you AAAI-18 registration information is programs specifically for students, are representing a company, research now available at aaai.org/aaai18, and including the Doctoral Consortium, organization or university and would online registration can be completed at the Student Abstract Program, Lunch like to participate in the job fair, please regonline.com/aaai18. The deadline with a Fellow, and the Volunteer Pro - send an email with your contact infor - for late registration rates is January 5, gram, in addition to the following: 2018. Complete tutorial and workshop mation to [email protected] no later than January 5. The organizers of information, as well as other special Student Reception the AAAI/ACM SIGAI Job Fair are John program information is available at AAAI will welcome all students to Dickerson (University of Maryland, these sites. AAAI-18 by hosting an evening stu - USA) and Nicholas Mattei (IBM, USA). dent reception on Friday, February 2. Make Your Hotel Reservation Although the reception is especially The Winograd Now! beneficial to new students at the con - Schema Challenge AAAI has reserved a block of rooms at ference, all are welcome! Please join us and make all the newcomers welcome! Nuance Communications, Inc. is spon - the Hilton New Orleans Riverside at soring a competition to encourage reduced conference rates. -
Probabilistic Machine Learning: Foundations and Frontiers
Probabilistic Machine Learning: Foundations and Frontiers Zoubin Ghahramani1,2,3,4 1 University of Cambridge 2 Alan Turing Institute 3 Leverhulme Centre for the Future of Intelligence 4 Uber AI Labs [email protected] http://mlg.eng.cam.ac.uk/zoubin/ Sackler Forum, National Academy of Sciences Washington DC, 2017 Machine Learning Zoubin Ghahramani 2 / 53 Many Related Terms Statistical Modelling Artificial Intelligence Neural Networks Machine Learning Data Mining Data Analytics Deep Learning Pattern Recognition Data Science Zoubin Ghahramani 3 / 53 Many Related Fields Engineering Computer Science Statistics Machine Learning Computational Applied Mathematics Neuroscience Economics Cognitive Science Physics Zoubin Ghahramani 4 / 53 Many Many Applications Bioinformatics Computer Vision Robotics Scientific Data Analysis Natural Language Processing Machine Learning Information Retrieval Speech Recognition Recommender Systems Signal Processing Machine Translation Medical Informatics Targeted Advertising Finance Data Compression Zoubin Ghahramani 5 / 53 Machine Learning • Machine learning is an interdisciplinary field that develops both the mathematical foundations and practical applications of systems that learn from data. Main conferences and journals: NIPS, ICML, AISTATS, UAI, KDD, JMLR, IEEE TPAMI Zoubin Ghahramani 6 / 53 Canonical problems in machine learning Zoubin Ghahramani 7 / 53 Classification x x x o x xxx o x o o o x x o o x o • Task: predict discrete class label from input data • Applications: face recognition, image recognition, -
Bayesian Activity Recognition Using Variational Inference
BAR: Bayesian Activity Recognition using variational inference Ranganath Krishnan Mahesh Subedar Omesh Tickoo [email protected] [email protected] [email protected] Intel Labs Hillsboro, OR (USA) Abstract Uncertainty estimation in deep neural networks is essential for designing reliable and robust AI systems. Applications such as video surveillance for identifying suspicious activities are designed with deep neural networks (DNNs), but DNNs do not provide uncertainty estimates. Capturing reliable uncertainty estimates in safety and security critical applications will help to establish trust in the AI system. Our contribution is to apply Bayesian deep learning framework to visual activity recognition application and quantify model uncertainty along with principled confidence. We utilize the stochastic variational inference technique while training the Bayesian DNNs to infer the approximate posterior distribution around model parameters and perform Monte Carlo sampling on the posterior of model parameters to obtain the predictive distribution. We show that the Bayesian inference applied to DNNs provide reliable confidence measures for visual activity recognition task as compared to conventional DNNs. We also show that our method improves the visual activity recognition precision-recall AUC by 6.2% compared to non-Bayesian baseline. We evaluate our models on Moments-In-Time (MiT) activity recognition dataset by selecting a subset of in- and out-of-distribution video samples. 1 Introduction Activity recognition is an active area of research with multiple approaches depending on the applica- tion domain and the types of sensors [1, 2, 3]. In recent years, the DNN models applied to the visual activity recognition task outperform the earlier methods based on Gaussian Mixture Models and Hid- den Markov Models [4, 5] using handcrafted features. -
Automatic Opioid User Detection from Twitter
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18) Automatic Opioid User Detection From Twitter: Transductive Ensemble Built On Different Meta-graph Based Similarities Over Heterogeneous Information Network Yujie Fan, Yiming Zhang, Yanfang Ye ∗, Xin Li Department of Computer Science and Electrical Engineering, West Virginia University, WV, USA fyf0004,[email protected], fyanfang.ye,[email protected] Abstract 13,000 people died from heroin overdose, both reflecting sig- nificant increase from 2002 [NIDA, 2017]. Opioid addiction Opioid (e.g., heroin and morphine) addiction has is a chronic mental illness that requires long-term treatment become one of the largest and deadliest epidemics and care [McLellan et al., 2000]. Although Medication As- in the United States. To combat such deadly epi- sisted Treatment (MAT) using methadone or buprenorphine demic, in this paper, we propose a novel frame- has been proven to provide best outcomes for opioid addiction work named HinOPU to automatically detect opi- recovery, stigma (i.e., bias) associated with MAT has limited oid users from Twitter, which will assist in sharp- its utilization [Saloner and Karthikeyan, 2015]. Therefore, ening our understanding toward the behavioral pro- there is an urgent need for novel tools and methodologies to cess of opioid addiction and treatment. In HinOPU, gain new insights into the behavioral processes of opioid ad- to model the users and the posted tweets as well diction and treatment. as their rich relationships, we introduce structured In recent years, the role of social media in biomedical heterogeneous information network (HIN) for rep- knowledge mining has become more and more important resentation. -
The Future of AI: Opportunities and Challenges
The Future of AI: Opportunities and Challenges Puerto Rico, January 2-5, 2015 ! Ajay Agrawal is the Peter Munk Professor of Entrepreneurship at the University of Toronto's Rotman School of Management, Research Associate at the National Bureau of Economic Research in Cambridge, MA, Founder of the Creative Destruction Lab, and Co-founder of The Next 36. His research is focused on the economics of science and innovation. He serves on the editorial boards of Management Science, the Journal of Urban Economics, and The Strategic Management Journal. & Anthony Aguirre has worked on a wide variety of topics in theoretical cosmology, ranging from intergalactic dust to galaxy formation to gravity physics to the large-scale structure of inflationary universes and the arrow of time. He also has strong interest in science outreach, and has appeared in numerous science documentaries. He is a co-founder of the Foundational Questions Institute and the Future of Life Institute. & Geoff Anders is the founder of Leverage Research, a research institute that studies psychology, cognitive enhancement, scientific methodology, and the impact of technology on society. He is also a member of the Effective Altruism movement, a movement dedicated to improving the world in the most effective ways. Like many of the members of the Effective Altruism movement, Geoff is deeply interested in the potential impact of new technologies, especially artificial intelligence. & Blaise Agüera y Arcas works on machine learning at Google. Previously a Distinguished Engineer at Microsoft, he has worked on augmented reality, mapping, wearable computing and natural user interfaces. He was the co-creator of Photosynth, software that assembles photos into 3D environments. -
Deep Learning
ADVANCEMENTS AND TRENDS IN MEDICAL IMAGE ANALYSIS USING DEEP LEARNING A presentation by Shekoofeh Azizi University of British Columbia, Canada, Ph.D. 2014-2018 Electrical and Computer Engineering Isfahan University of Technology, Iran, M.Sc. 2011-2013 Computer Engineering / Hardware Design Isfahan University of Technology, Iran, B.Sc. 2007-2011 Computer Engineering / Hardware Engineering Philips Research North America, 2015-now National Institutes of Health (NIH), 2015-now MICCAI Student Board Officer, 2016-now Women in MICCAI, 2017-now 2 UBC Queen’s University SFU Univ. of Western Ontario VGH Robarts Research Technical Univ. of Munich ETH Zurich NVidia Sejong Univ., Korea Philips IBM Univ. of Colorado NIH Size is related to the number of collaborators Academic Collaborators Industrial/Clinical Collaborators 3 OUTLINE Deep Learning Medical Imaging Challenges and Vision Opportunities 4 DEEP LEARNING 5 ARTIFICIAL INTELLIGENCE VS. DATA SCIENCE AI is a technique which enables machines to mimic the “human behaviour” It’s 17°C Hey Google, and sunny in Weather in Victoria Victoria! Voice Services Voice to Command Brain/Model Voice Output User Google Home Google Home 6 ARTIFICIAL INTELLIGENCE VS. DATA SCIENCE Data Science is about processes and systems to extract knowledge or insights from data in Artificial Intelligence various forms. (AI) Machine Learning is the connection Machine Learning between data science and artificial (ML) intelligence since machine learning is the process of learning from data over time. Data Science Deep Learning (DL) 7 MACHINE LEARNING (ML) Machine learning is the process of learning from data over time. Reinforcement Learning Andrew Ng, Machine Learning Course, Coursera. 9 DEEP LEARNING (DL) Inspired by the functionality of our brains Number of sides: 4? Closed form shape? Perpendicular sides? Square ? Equal sides? 10 DEEP LEARNING (DL) Cat Feature Learning Dog 11 DEEP LEARNING (DL) 12 WHY DEEP LEARNING? Consistent improvement over the state-of-the-art across a large variety of domains.