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DTSTART;TZID=America/New_York:20190311T160000
DTEND;TZID=America/New_York:20190311T170000
DTSTAMP:20260529T111852
CREATED:20190308T162816Z
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UID:9013-1552320000-1552323600@idss-stage.mit.edu
SUMMARY:Using Computer Vision to Study Society:  Methods and Challenges
DESCRIPTION:  \nAbstract: \nTargeted socio-economic policies require an accurate understanding of a country’s demographic makeup. To that end\, the United States spends more than 1 billion dollars a year gathering census data such as race\, gender\, education\, occupation and unemployment rates. Compared to the traditional method of collecting surveys across many years which is costly and labor intensive\, data-driven\, machine learning driven approaches are cheaper and faster–with the potential ability to detect trends in close to real time. In this work\, we leverage the ubiquity of Google Street View images and develop a computer vision pipeline to predict income\, per capita carbon emission\, crime rates and other city attributes from a single source of publicly available visual data. We first detect cars in 50 million images across 200 of the largest US cities and train a model to determine demographic attributes using the detect cars. To facilitate our work\, we used a graph based algorithm to collect a challenging fine-grained dataset consisting of over 2600 classes of cars comprised of images from Google Street View and other web sources. Our prediction results correlate well with ground truth income (r=0.82)\, race\, education\, voting\, sources investigating crime rates\, income segregation\, per capita carbon emission\, and other market research. Data mining based works such as this one can be used for many types of applications–some ethical and others not. I will finally discuss work (inspired by my experiences while working on this project)\, on auditing and exposing biases found in computer vision systems. Using recent work on exposing the gender and skin type bias found in commercial gender classification systems as a case study\, I will discuss how the lack of standardization and documentation in AI is leading to biased systems used in high stakes scenarios. I will end with the concept of AI datasheets for datasets\, and model cards for model reporting to standardize information for datasets and pre-trained models\, to push the field as a whole towards transparency and accountability. Host: Antonio Torralba. \n Bio: \nTimnit Gebru is a research scientist in the Ethical AI team at Google and just finished her postdoc in the Fairness Accountability Transparency and Ethics (FATE) group at Microsoft Research\, New York. Prior to that\, she was a PhD student in the Stanford Artificial Intelligence Laboratory\, studying computer vision under Fei-Fei Li. Her main research interest is in data mining large-scale\, publicly available images to gain sociological insight\, and working on computer vision problems that arise as a result\, including fine-grained image recognition\, scalable annotation of images\, and domain adaptation. She is currently studying the ethical considerations underlying any data mining project\, and methods of auditing and mitigating bias in sociotechnical systems. The New York Times\, MIT Tech Review and others have recently covered her work. As a cofounder of the group Black in AI\, she works to both increase diversity in the field and reduce the negative impacts of racial bias in training data used for human-centric machine learning models.
URL:https://idss-stage.mit.edu/calendar/using-computer-vision-to-study-society-methods-and-challenges/
LOCATION:32-G449 (KIva/Patel)
CATEGORIES:IDSS Special Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181127T160000
DTEND;TZID=America/New_York:20181127T170000
DTSTAMP:20260529T111852
CREATED:20181031T164820Z
LAST-MODIFIED:20181129T142536Z
UID:8542-1543334400-1543338000@idss-stage.mit.edu
SUMMARY:Censored: Distraction and Diversion Inside China's Great Firewall
DESCRIPTION:Abstract:\nAs authoritarian governments around the world develop sophisticated technologies for controlling information\, many observers have predicted that these controls would be ineffective because they are easily thwarted and evaded by savvy Internet users. In Censored\, Margaret Roberts demonstrates that even censorship that is easy to circumvent can still be enormously effective. Taking advantage of digital data harvested from the Chinese Internet and leaks from China’s Propaganda Department\, this book sheds light on how and when censorship influences the Chinese public. \nRoberts finds that much of censorship in China works not by making information impossible to access but by requiring those seeking information to spend extra time and money for access. By inconveniencing users\, censorship diverts the attention of citizens and powerfully shapes the spread of information. When Internet users notice blatant censorship\, they are willing to compensate for better access. But subtler censorship\, such as burying search results or introducing distracting information on the web\, is more effective because users are less aware of it. Roberts challenges the conventional wisdom that online censorship is undermined when it is incomplete and shows instead how censorship’s porous nature is used strategically to divide the public. \nDrawing parallels between censorship in China and the way information is manipulated in the United States and other democracies\, Roberts reveals how Internet users are susceptible to control even in the most open societies. Demonstrating how censorship travels across countries and technologies\, Censored gives an unprecedented view of how governments encroach on the media consumption of citizens. \n  \nAbout the Speaker:\nMargaret Roberts is an Associate Professor in the Department of Political Science at the University of California\, San Diego. Roberts research focuses on better measuring and understanding the political information strategies of authoritarian governments\, with a specific focus on studying censorship and propaganda in China. She has also developed widely used methods for automated content analysis in the social sciences. Roberts received her PhD in Government from Harvard University in 2014\, an M.S. in Statistics and B.A. in International Relations and Economics from Stanford in 2009. Her work has appeared in venues such as the American Political Science Review\, American Journal of Political Science\, Political Analysis\, Journal of the American Statistical Association and Science.
URL:https://idss-stage.mit.edu/calendar/censored-distraction-and-diversion-inside-chinas-great-firewall/
LOCATION:32-141\, United States
CATEGORIES:IDSS Special Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181011T160000
DTEND;TZID=America/New_York:20181011T170000
DTSTAMP:20260529T111852
CREATED:20181010T144504Z
LAST-MODIFIED:20181010T145040Z
UID:8389-1539273600-1539277200@idss-stage.mit.edu
SUMMARY:Local Geometric Analysis and Applications
DESCRIPTION:Abstract: Local geometric analysis is a method to define a coordinate system in a small neighborhood in the space of distributions over a given alphabet. It is a powerful technique since the notions of distance\, projection\, and inner product defined this way are useful in the optimization problems involving distributions\, such as regressions. It has been used in many places in the literature such as correlation analysis\, correspondence analysis. In this talk\, we will go through some of the basic setups and properties\, and discuss a few applications in information theory\, dimension reduction and softmax regression. \n About this Seminar: This seminar consists of a series of lectures each followed by a period of informal discussion and social. The topics are at the nexus of information theory\, inference\, causality\, estimation\, and non-convex optimization. The lectures are intended to be tutorial in nature with the goal of learning about interesting and exciting topics rather than merely hearing about the most recent results. The topics are driven by the interests of the speakers\, and with the exception of the two lectures on randomness and information\, there is no planned coherence or dependency among them. Ad hoc follow-on meetings about any of the topics presented are highly encouraged.
URL:https://idss-stage.mit.edu/calendar/local-geometric-analysis-and-applications/
LOCATION:32-D677\, United States
CATEGORIES:IDSS Special Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180925T160000
DTEND;TZID=America/New_York:20180925T170000
DTSTAMP:20260529T111852
CREATED:20180726T171445Z
LAST-MODIFIED:20180904T145459Z
UID:8078-1537891200-1537894800@idss-stage.mit.edu
SUMMARY:Text as Data in Social Science: Discovery\, Measurement and Causal Inference
DESCRIPTION:Social scientists are increasingly turning to computer-assisted text analysis as a way of understanding the digital footprints left by communities and individuals.  Much of the technology that powers these approaches is borrowed from the fields of computer science and statistics; yet\, social scientists have substantially different goals.  We focus on the development of methods that support three core tasks: discovery\, measurement and causal inference with text.  We introduce the Structural Topic Model (STM)\, a bayesian generative model of text which is built for social science inference.  Using this model as a running example\, we will discuss the challenges of discovery\, measurement and causal inference and how to adapt our tools to approach each task.  The tasks will be illustrated with multiple examples across many different domains.  The talk will end with future directions for this fast-moving\, inter-disciplinary field. [Includes joint work with Molly Roberts\, Justin Grimmer\, Dustin Tingley\, Edo Airoldi\, Richard Nielsen and others.]\nAbout the speaker: Brandon Stewart is an Assistant Professor in the Department of Sociology and is also affiliated with the Department of Politics and the Office of Population Research. He develops new quantitative statistical methods for applications across the social sciences. Methodologically his focus is in tools which facilitate automated text analysis and model complex heterogeneity in regression. Many recent applications of these methods have centered on using large corpora of text to better understand propaganda in contemporary China. His research has been published in journals such as American Journal of Political Science\, Political Analysis and the Proceedings of the Association of Computational Linguistics. His work has won the Edward R Chase Dissertation Prize\, the Gosnell Prize for Excellence in Political Methodology\, and the Political Analysis Editor’s Choice Award.
URL:https://idss-stage.mit.edu/calendar/idss-seminar-brandon-stewart/
LOCATION:32-141\, United States
CATEGORIES:IDSS Special Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180920T160000
DTEND;TZID=America/New_York:20180920T170000
DTSTAMP:20260529T111852
CREATED:20180910T161945Z
LAST-MODIFIED:20180919T022223Z
UID:8249-1537459200-1537462800@idss-stage.mit.edu
SUMMARY:Topics in Information and Inference Seminar
DESCRIPTION:Title: Strong data processing inequalities and information percolation\n\n Abstract: The data-processing inequality\, that is\, $I(U;Y) \le I(U;X)$ for a Markov chain $U \to X \to Y$\, has been the method of choice for proving impossibility (converse) results in information theory and many other disciplines. A channel-dependent improvement is called the strong data-processing inequality (or SDPI). In this talk we will: a) review SDPIs; b) show how point-to-point SDPIs can be combined into an SDPI for a network; c) show recent applications to problems of statistical inference on graphs (spiked Wigner model\, community detection etc.)
URL:https://idss-stage.mit.edu/calendar/topics-in-information-and-inference-seminar/
LOCATION:32-D677\, United States
CATEGORIES:IDSS Special Seminars
ORGANIZER;CN="":MAILTO:jeannille.hiciano@gmail.com
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180821T140000
DTEND;TZID=America/New_York:20180821T150000
DTSTAMP:20260529T111852
CREATED:20180821T144701Z
LAST-MODIFIED:20180821T144701Z
UID:8203-1534860000-1534863600@idss-stage.mit.edu
SUMMARY:Resource-efficient ML in 2 KB RAM for the Internet of Things
DESCRIPTION:Abstract: We propose an alternative paradigm for the Internet of Things (IoT) where machine learning algorithms run locally on severely resource-constrained edge and endpoint devices without necessarily needing cloud connectivity. This enables many scenarios beyond the pale of the traditional paradigm including low-latency brain implants\, precision agriculture on disconnected farms\, privacy-preserving smart spectacles\, etc. \nTowards this end\, we develop novel tree and kNN based algorithm\, called Bonsai and ProtoNN\, for efficient prediction on IoT devices — such as those based on the Arduino Uno board having an 8 bit ATmega328P microcontroller operating at 16 MHz with no native floating point support\, 2 KB RAM and 32 KB read-only flash memory. Experimental results on multiple benchmark datasets demonstrate that Bonsai and ProtoNN can make predictions in milliseconds even on slow microcontrollers\, can fit in KB of memory\, have lower battery consumption than all other algorithms while achieving prediction accuracies that can be as much as 30% higher than state-of-the-art methods for resource-efficient machine learning. \nTime permitting\, I will also discuss our recent results about deploying RNNs on similar sized tiny devices. \nJoint work with Manik Varma\, Harsha Simhadri\, Arun Suggala\, Ankit Goyal\, Chirag Gupta\, Don Dennis\, Aditya Kusupati\, Shishir Patil\, Ashish Kumar. \nBiography: I am a member of the Machine Learning and Optimization and the Algorithms and Data Sciences Group at Microsoft Research\, Bangalore\, India. My research interests are in machine learning\, non-convex optimization\, high-dimensional statistics\, and optimization algorithms in general. I am also interested in applications of machine learning to privacy\, computer vision\, text mining and natural language processing.\nEarlier\, I completed my PhD at the University of Texas at Austin under Prof. Inderjit S. Dhillon.
URL:https://idss-stage.mit.edu/calendar/resource-efficient-ml-in-2-kb-ram-for-the-internet-of-things/
LOCATION:E18-304\, United States
CATEGORIES:IDSS Special Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180226T160000
DTEND;TZID=America/New_York:20180226T170000
DTSTAMP:20260529T111852
CREATED:20180209T173751Z
LAST-MODIFIED:20180209T174117Z
UID:7358-1519660800-1519664400@idss-stage.mit.edu
SUMMARY:Provably Secure Machine Learning
DESCRIPTION:Abstract:  The widespread use of machine learning systems creates a new class of computer security vulnerabilities where\, rather than attacking the integrity of the software itself\, malicious actors exploit the statistical nature of the learning algorithms. For instance\, attackers can add fake data (e.g. by creating fake user accounts)\, or strategically manipulate inputs to the system once it is deployed. \nSo far\, attempts to defend against these attacks have focused on empirical performance against known sets of attacks. I will argue that this is a fundamentally inadequate paradigm for achieving meaningful security guarantees. Instead\, we need algorithms that are provably secure by design\, in line with best practices for traditional computer security. \nTo achieve this goal\, we take inspiration from robust statistics and robust optimization\, but with an eye towards the security requirements of modern machine learning systems. Motivated by the trend towards models with thousands or millions of features\, we investigate the robustness of learning algorithms in high dimensions. We show that most algorithms are brittle to even small fractions of adversarial data\, and then develop new algorithms that are provably robust. Additionally\, to accommodate the increasing use of deep learning\, we develop an algorithm for certifiably robust optimization of non-convex models such as neural networks. \nBiography:   Jacob Steinhardt is a graduate student in artificial intelligence at Stanford University working with Percy Liang.   His main research interest is in designing machine learning algorithms with the reliability properties of good software. So far this has led to the study of provably secure machine learning systems\, as well as the design of learning algorithms that can detect their own failures and generalize predictably in new situations. Outside of research\, Jacob is a technical advisor to the Open Philanthropy Project\, and mentors gifted high school students through the USACO and SPARC summer programs.
URL:https://idss-stage.mit.edu/calendar/provably-secure-machine-learning/
LOCATION:32-G449 (Kiva/Patel)
CATEGORIES:IDSS Special Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170313T150000
DTEND;TZID=America/New_York:20170313T163000
DTSTAMP:20260529T111852
CREATED:20190904T175423Z
LAST-MODIFIED:20190904T175423Z
UID:10617-1489417200-1489422600@idss-stage.mit.edu
SUMMARY:Learning from People
DESCRIPTION:Abstract:\nLearning from people represents a new and expanding frontier for data science. Two critical challenges in this domain are of developing algorithms for robust learning and designing incentive mechanisms for eliciting high-quality data. In this talk\, I describe progress on these challenges in the context of two canonical settings\, namely those of ranking and classification. In addressing the first challenge\, I introduce a class of “permutation-based” models that are considerably richer than classical models\, and present algorithms for estimation that are both rate-optimal and significantly more robust than prior state-of-the-art methods. I also discuss how these estimators automatically adapt and are simultaneously also rate-optimal over the classical models\, thereby enjoying a surprising a win-win in the bias-variance tradeoff. As for the second challenge\, I present a class of “multiplicative” incentive mechanisms\, and show that they are the unique mechanisms that can guarantee honest responses. Extensive experiments on a popular crowdsourcing platform reveal that the theoretical guarantees of robustness and efficiency indeed translate to practice\, yielding several-fold improvements over prior art. \nBio:\nNihar B. Shah is a PhD candidate in the EECS department at the University of California\, Berkeley. He is the recipient of the Microsoft Research PhD Fellowship 2014-16\, the Berkeley Fellowship 2011-13\, the IEEE Data Storage Best Paper and Best Student Paper Awards for the years 2011/2012\, and the SVC Aiya Medal from the Indian Institute of Science for the best master’s thesis in the department. His research interests include statistics and machine learning\, with a current focus on applications to learning from people.
URL:https://idss-stage.mit.edu/calendar/learning-from-people/
LOCATION:34-401 (Grier Room)\, The Stata Center (34-401)\, 50 Vassar Street\, Cambridge\, 02139\, United States
CATEGORIES:IDSS Special Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170306T173000
DTEND;TZID=America/New_York:20170306T173000
DTSTAMP:20260529T111852
CREATED:20190627T212134Z
LAST-MODIFIED:20190829T195456Z
UID:10105-1488821400-1488821400@idss-stage.mit.edu
SUMMARY:Gene Regulation in Space and Time\, or From Ellipsoid Packing to Causal Inference - DaVinci Lecture (presented by Tau Beta Pi)
DESCRIPTION:Abstract: Although the genetic information in each cell within an organism is identical\, gene expression varies widely between different cell types. The quest to understand this phenomenon has led to many interesting mathematics problems. Experimental evidence suggests that the differential gene expression is related to the spatial organization of chromosomes in the cell nucleus. I will present a new model\, based on ellipsoid packing and causal inference\, that can link the 3d organization of chromosomes with gene regulation. Such models have important implications in understanding the mechanisms underlying cellular reprogramming events.
URL:https://idss-stage.mit.edu/calendar/gene-regulation-in-space-and-time-or-from-ellipsoid-packing-to-causal-inference-davinci-lecture-presented-by-tau-beta-pi-2/
LOCATION:4-237\, United States
CATEGORIES:IDSS Special Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170213T160000
DTEND;TZID=America/New_York:20170213T160000
DTSTAMP:20260529T111852
CREATED:20190627T212137Z
LAST-MODIFIED:20190904T175906Z
UID:10113-1487001600-1487001600@idss-stage.mit.edu
SUMMARY:Towards a Theory of Fairness in Machine Learning
DESCRIPTION:Abstract:  Algorithm design has moved from being a tool used exclusively for designing systems to one used to present people with personalized content\, advertisements\, and other economic opportunities. Massive amounts of information is recorded about people’s online behavior including the websites they visit\, the advertisements they click on\, their search history\, and their IP address. Algorithms then use this information for many purposes: to choose which prices to quote individuals for airline tickets\, which advertisements to show them\, and even which news stories to promote. These systems create new challenges for algorithm design. When a person’s behavior influences the prices they may face in the future\, they may have a strong incentive to modify their behavior to improve their long-term utility; therefore\, these algorithms’ performance should be resilient to strategic manipulation. Furthermore\, when an algorithm makes choices that affect people’s everyday lives\, the effects of these choices raise ethical concerns such as whether the algorithm’s behavior violates individuals’ privacy or whether the algorithm treats people fairly. \nMachine learning algorithms in particular have received much attention for exhibiting bias\, or unfairness\, in a large number of contexts. In this talk\, I will describe my recent work on developing a definition of fairness for machine learning. One definition of fairness\, encoding the notion of ‘fair equality of opportunity’\, informally\, states that if one person has higher expected quality than another person\, the higher quality person should be given at least as much opportunity as the lower quality person. I will present a result characterizing the performance degradation of algorithms\, which satisfy this condition in the contextual bandits setting. To complement these theoretical results\, I then present the results of several empirical evaluations of fair algorithms. \nI will also briefly describe my work on designing algorithms whose performance guarantees are resilient to strategic manipulation of their inputs\, and machine learning for optimal auction design. \nBio: Jamie Morgenstern is a Warren Center postdoctoral fellow in Computer Science and Economics at the University of Pennsylvania. She received her Ph.D. in Computer Science from Carnegie Mellon University in 2015\, and her B.S. in Computer Science and B.A. in Mathematics from the University of Chicago in 2010. Her research focuses on machine learning for mechanism design\, fairness in machine learning\, and algorithmic game theory. She received a Microsoft Women’s Research Scholarship\, an NSF Graduate Research Fellowship\, and a Simons Award for Graduate Students in Theoretical Computer Science.
URL:https://idss-stage.mit.edu/calendar/towards-a-theory-of-fairness-in-machine-learning-2/
LOCATION:32-G449 (Kiva)\, United States
CATEGORIES:IDSS Special Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20160915T110000
DTEND;TZID=America/New_York:20160915T110000
DTSTAMP:20260529T111852
CREATED:20190627T212152Z
LAST-MODIFIED:20190829T195738Z
UID:10139-1473937200-1473937200@idss-stage.mit.edu
SUMMARY:Duration and deadline differentiated electricity demand: a model of flexible demand Speaker
DESCRIPTION:
URL:https://idss-stage.mit.edu/calendar/duration-and-deadline-differentiated-electricity-demand-a-model-of-flexible-demand-speaker-2/
LOCATION:E18-304\, United States
CATEGORIES:IDSS Special Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20151207T160000
DTEND;TZID=America/New_York:20151207T160000
DTSTAMP:20260529T111852
CREATED:20190627T212207Z
LAST-MODIFIED:20190829T195842Z
UID:10178-1449504000-1449504000@idss-stage.mit.edu
SUMMARY:CIR Seminar Series
DESCRIPTION:
URL:https://idss-stage.mit.edu/calendar/cir-seminar-series-2/
LOCATION:4-231\, United States
CATEGORIES:IDSS Special Seminars
END:VEVENT
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