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DTSTART;TZID=UTC:20231113T080000
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SUMMARY:Natural Language Processing\, Julien Boelaert (CERAPS\, Université de Lille)
DESCRIPTION:  \n\n\n\n  \n  \nSCHEDULE\n  \nMonday\n  \n13th November 2023 \n20th November 2023\n  \nFrom 13:00 to 16:15\n  \nRoom 2033\n\n\n  \nThursday\n  \n16th November 2023 \n23rd November 2023\n  \nFrom 13:00 to 16:15\n  \nRoom 2033\n\n\n\nAims and objectives\nThe aim of this course is to provide an introduction to the main contemporary methods for natural language processing\, and to illustrate them with recent uses of text as data in social sciences. \nNatural language processing has made giant steps during the last decade\, as illustrated in 2023 by the resounding popularity of chatGPT. In addition\, text corpora have become increasingly available for exploitation by social scientists\, be it through digitization of originally paper sources (eg. Parliamentary sessions transcripts\, printed newspapers\, books\, historical sources\, …) or audio sources (through automatic transcription)\, or through the advent of natively digital sources (from social media\, online newspapers\, …). \nThe course will start with the standard (aka pre-neural) methods of the late 20th century\, based on large document-feature matri-ces. We will then cover more recent developments: word embeddings (for improved NLP\, or studies about bias in text corpora)\, topic modeling with Latent Dirichlet Allocation (unsupervised detection of topics)\, and Transformer models (current state of the art\, BERT- and GPT-like models). Each session will comprise a theoretical lecture\, and applied examples on R or python. \n\n
URL:https://crest.science/event/natural-language-processing-julien-boelaert-ceraps-universite-de-lille/
LOCATION:2033
CATEGORIES:Doctoral Courses,Sociology
ORGANIZER;CN="Etienne%20Ollion":MAILTO:etienne.ollion@ensae.fr
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DTSTART;TZID=Europe/Helsinki:20231120T121500
DTEND;TZID=Europe/Helsinki:20231120T133000
DTSTAMP:20260711T061356
CREATED:20231004T054717Z
LAST-MODIFIED:20231017T104622Z
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SUMMARY:Elisabeth PROEHL (University of Amsterdam) "Existence and Uniqueness of Recursive Equilibria With Aggregate and Idiosyncratic Risk"
DESCRIPTION:Macro seminar\nTime : 12h15 – 13h30 \nDate : 20 Novembre 2023 \nSalle 3001 \nElisabeth PROEHL (University of Amsterdam) “Existence and Uniqueness of Recursive Equilibria With Aggregate and Idiosyncratic Risk” \nAbstract: In this paper\, I study the existence and uniqueness of recursive equilibria in economies with aggregate and idiosyncratic risk. Rather than relying on compactness to establish existence\, I exploit the monotonicity property of the equilibrium model and rely on arguments from convex analysis. This methodology does not only give rise to a convergent iterative procedure\, but more strikingly\, it also yields uniqueness. To illustrate my theoretical results\, I establish sufficient conditions for the existence and uniqueness of solutions to the stochastic growth model as in Krusell and Smith (1998) and the heterogeneous-agent exchange economy as in Huggett (1993) with aggregate risk. \nPablo WINANT (CREST) \n
URL:https://crest.science/event/elisabeth-proehl-university-of-amsterdam/
CATEGORIES:Macroeconomics,Seminars
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DTSTART;TZID=Europe/Helsinki:20231120T140000
DTEND;TZID=Europe/Helsinki:20231120T150000
DTSTAMP:20260711T061356
CREATED:20231025T131420Z
LAST-MODIFIED:20231117T085307Z
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SUMMARY:Anindya DE (University of Pennsylvania) - "Testing convex truncation"
DESCRIPTION:Statistical Seminar: Every Monday at 2:00 pm.\nTime: 2:00 pm – 3:00 pm\nDate: 20th November 2023\nPlace: 3001 \n  \nAnindya De (University of Pennsylvania) “Testing convex truncation” \n Abstract: We study the basic statistical problem of testing whether normally distributed n-dimensional data has been truncated\, i.e. altered by only retaining points that lie in some unknown truncation set S. As our main algorithmic results\, (1) We give a computationally efficient O(n)-sample algorithm that can distinguish the standard normal distribution from the normal conditioned on an unknown and arbitrary convex set S. (2) We give a different computationally efficient O(n)-sample algorithm that can distinguish the standard normal distribution from the normal conditioned on an unknown and arbitrary mixture of symmetric convex sets. \nThese results stand in sharp contrast with known results for learning or testing convex bodies with respect to the normal distribution or learning convex-truncated normal distributions\, where state-of-the-art algorithms require essentially n^{O(sqrt{n})} samples. An easy argument shows that no finite number of samples suffices to distinguish the normal from an unknown and arbitrary mixture of general (not necessarily symmetric) convex sets\, so no common generalization of results (1) and (2) above is possible. We also prove lower bounds on the sample complexity of distinguishing algorithms (computationally efficient or otherwise) for various classes of convex truncations; in some cases these lower bounds match our algorithms up to logarithmic or even constant factors. \nBased on joint work with : Shivam Nadimpalli and Rocco Servedio. \nLink zoom : https://zoom.us/j/99093379768?pwd=c3I3ejQxUkwrV3k0dTNXUHpyUGNIdz09 \nOrganizers: \nZCristina BUTUCEA (CREST)\, Karim LOUNICI (CMAP) \, Jaouad MOURTADA (CREST) \nSponsors:\nCREST-CMAP \n
URL:https://crest.science/event/anindya-de-university-of-pennsylvania-to-be-announced/
CATEGORIES:Seminars,Statistics
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