BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//CREST - ECPv5.1.3//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:CREST
X-ORIGINAL-URL:https://crest.science
X-WR-CALDESC:Events for CREST
BEGIN:VTIMEZONE
TZID:Europe/Helsinki
BEGIN:DAYLIGHT
TZOFFSETFROM:+0200
TZOFFSETTO:+0300
TZNAME:EEST
DTSTART:20260329T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0300
TZOFFSETTO:+0200
TZNAME:EET
DTSTART:20261025T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260316
DTEND;VALUE=DATE:20260331
DTSTAMP:20260709T224434
CREATED:20251210T123817Z
LAST-MODIFIED:20260324T072239Z
UID:18644-1773619200-1774915199@crest.science
SUMMARY:Taiji Suzuki (Univ. Tokyo) - Feature learning perspective of deep foundation models: statistical and optimization theories
DESCRIPTION:FEATURE LEARNING PERSPECTIVE OF DEEP FOUNDATION MODELS: STATISTICAL AND OPTIMIZATION THEORIES\nTAIJI SUZUKI\nUniversity of Tokyo\nDepartment of Mathematical Informatics \nSchedule: \nMarch 16\, 2026 | 2PM – 5PM – Room 2005\nMarch 19\, 2026 | 2PM – 5PM – Room 2005\nMarch 26\, 2026 | 2PM – 5PM – Room 2005\nMarch 30\, 2026 | 2PM – 5PM – Room 2005 \nContent:\nThe main focus of this lecture will be on the feature learning aspects of deep foundation models\, especially about the benefit of feature learning to achieve better predictive accuracy and more efficient optimization. As the deep foundation models are developed following the scaling law\, a theoretical understanding of the learning principles behind practice is getting more important. For superior generalization of deep models\, it is essentially important to acquire compressed representations avoiding mere memorization\, making representation/feature learning fundamental. It has been theoretically shown that deep learning gains various advantages in generalization via its feature learning ability which naturally arises from its deep structure. It will be discussed how the feature learning ability affects the rate of convergence by comparing it with the sub-optimal rate of non-feature learning methods. This issue will be discussed not only from statistical  perspective but also from optimization perspective. Interesting examples include estimating a Gaussian single index model in which the computational complexity can be characterized by quantities socalled information exponent and generative exponent. If time permits\, optimization guarantees by mean field Langevin dynamics and its statistical property will also be discussed. Furthermore\, feature learning is significant not only during pre-training but also during test-time inference. This will be demonstrated concisely using in-context learning as an example. It will be discussed how the test time feature learning as well as pre-training feature learning affects the performance of test time inference. In summary\, the following topics will be covered in the lecture (but some of them could be omitted depending on time constraint):\n• Nonparametric function estimation by deep learning on high dimension data and its minimax optimality.\n• Stochastic gradient descent for neural network training; Gaussian single index model\, k-parity problem\, information exponent\, CSQ/SQ lower bound.\n• Mean field Langevin dynamics and its statistical property.\n• Test time inference and test time scaling: Transformer\, in-context learning\, chain-ofthought. \nEvaluation: \nTake home project. \nReferences:\nE. Giné and R. Nickl. Mathematical foundations of infinite-dimensional statistical models. Cambridge Series in Statistical and Probabilistic Mathematics. Cambridge University Press\, 2015.\nS. Hayakawa and T. Suzuki. On the minimax optimality and superiority of deep neural network learning over sparse parameter spaces. Neural Networks\, 123:343–361\, 2020.\nJ. Kim\, T. Nakamaki\, and T. Suzuki. Transformers are minimax optimal nonparametric incontext learners. In Advances in Neural Information Processing Systems\, volume 37\, pages 106667–106713\, 2024.\nJ. D. Lee\, K. Oko\, T. Suzuki\, and D. Wu. Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit. In Advances in Neural Information Processing Systems\, volume 37\, pages 58716–58756\, 2024.\nN. Nishikawa\, Y. Song\, K. Oko\, D. Wu\, and T. Suzuki. Nonlinear transformers can perform inference-time feature learning. In Proceedings of the 42nd International Conference on Machine Learning\, volume 267 of Proceedings of Machine Learning Research\, pages 46554– 46585. PMLR\, 2025.\nA. Nitanda\, D. Wu\, and T. Suzuki. Convex analysis of the mean field Langevin dynamics. In Proceedings of The 25th International Conference on Artificial Intelligence and Statistics\, volume 151 of Proceedings of Machine Learning Research\, pages 9741–9757. PMLR\, 28–30 Mar 2022.\nNitanda\, A. Lee\, D. T. X. Kai\, M. Sakaguchi\, and T. Suzuki. Propagation of chaos for mean-field Langevin dynamics and its application to model ensemble. In Forty-second International Conference on Machine Learning\, 2025.\nK. Oko\, Y. Song\, T. Suzuki\, and D. Wu. Pretrained transformer efficiently learns lowdimensional target functions in-context. In Advances in Neural Information Processing Systems\, volume 37\, pages 77316–77365\, 2024.\nT. Suzuki. Adaptivity of deep ReLU network for learning in Besov and mixed smooth Besov spaces: optimal rate and curse of dimensionality. In International Conference on Learning Representations\, 2019.\nT. Suzuki and A. Nitanda. Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space. In Advances in Neural Information Processing Systems\, volume 34\, pages 3609–3621\, 2021. A. B. Tsybakov. \n  \n
URL:https://crest.science/event/taiji-suzuki-univ-tokyo-feature-learning-perspective-of-deep-foundation-models-statistical-and-optimization-theories/
CATEGORIES:Doctoral Courses,Statistics
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Helsinki:20260323T121500
DTEND;TZID=Europe/Helsinki:20260323T133000
DTSTAMP:20260709T224434
CREATED:20260316T152427Z
LAST-MODIFIED:20260316T152631Z
UID:18863-1774268100-1774272600@crest.science
SUMMARY:Gernot MULLER (Tuebingen University) "The Propagation of Tariff Shocks via Production Networks"
DESCRIPTION:[vc_row][vc_column][vc_column_text]Macro seminar\nTime : 12h15 – 13h30 \nDate : 23th  March 2026 \nSalle 3001 \nGernot MULLER (Tuebingen University) “The Propagation of Tariff Shocks via Production Networks” \nAbstract: A tariff on final goods shifts expenditure toward domestically produced goods and therefore constitutes a positive demand shock. By contrast\, a tariff on intermediate goods raises production costs for domestic firms and thus constitutes a negative supply shock. The overall impact of import tariffs depends on the structure of the economy’s input–output network. While tariffs unambiguously raise inflation\, the persistence of inflationary effects depends on the network structure. We establish these results in a New Keynesian small open-economy model with an input–output network and provide supporting evidence based on sectoral time-series data\, showing that tariffs are contractionary on average. \n Organizer :  Olivier LOISEL \n
URL:https://crest.science/event/gernot-muller-tuebingen-university-the-propagation-of-tariff-shocks-via-production-networks/
CATEGORIES:Macroeconomics,Seminars
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Helsinki:20260323T140000
DTEND;TZID=Europe/Helsinki:20260323T153000
DTSTAMP:20260709T224434
CREATED:20260223T091740Z
LAST-MODIFIED:20260316T081350Z
UID:18819-1774274400-1774279800@crest.science
SUMMARY:Thomas BERRETT (University of Warwick) - Permutation two-sample testing under local differential privacy
DESCRIPTION:Statistical Seminar: Every Monday at 2:00 pm.\nTime: 2:00 pm – 3:00 pm\nDate: 23th March\nPlace: 3001 \n  \nThomas BERRETT (University of Warwick) – Permutation two-sample testing under local differential privacy \n  \n Abstract :  \nPersonal and sensitive data is now collected at larger scales than ever before. Growing concern from data subjects and regulatory bodies\, however\, has led to an increased demand for statistical procedures that do not compromise the privacy of the individuals whose data are collected and analysed. In this talk I will discuss recent work on two-sample testing under a local differential privacy constraint where a permutation procedure is used to calibrate the tests. While permutation testing is a classical resampling technique\, popular due to its ease of implementation and uniform Type I error control\, its use under local privacy constraints is complicated by the fact that access to the data is limited. In this work we design appropriate privacy mechanisms\, both interactive and non-interactive\, that allow for permutation tests. Our analysis shows that these lead to minimax optimal separation rates in both discrete and continuous settings\, with interactive procedures being significantly more powerful. This is recent joint work with Alexander Kent and Yi Yu (https://arxiv.org/abs/2505.24811). \n  \nOrganizers: \nAnna KORBA (CREST)\, Vincent DIVOL (CREST) \, Jaouad MOURTADA (CREST) \nSponsors:\nCREST-CMAP \n
URL:https://crest.science/event/thomas-berrett-university-of-warwick-tba/
CATEGORIES:Seminars,Statistics
ATTACH;FMTTYPE=:
END:VEVENT
END:VCALENDAR