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X-WR-CALDESC:Events for CREST
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TZID:Europe/Helsinki
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TZOFFSETFROM:+0200
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DTSTART:20260329T010000
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DTSTART:20261025T010000
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DTSTART;TZID=Europe/Helsinki:20260309T121500
DTEND;TZID=Europe/Helsinki:20260309T133000
DTSTAMP:20260710T035127
CREATED:20250715T071037Z
LAST-MODIFIED:20260204T091953Z
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SUMMARY:Stephen HANSEN (University College London) "Policymakers’ Uncertainty"
DESCRIPTION:[vc_row][vc_column][vc_column_text]Macro seminar\nTime : 12h15 – 13h30 \nDate : 09th  March 2026 \nSalle 3001 \nStephen HANSEN (University College London) “Policymakers’ Uncertainty” \nAbstract: We examine how uncertainty impacts decision-making by the Federal Open Market Committee (FOMC). Drawing from private deliberations\, we quantify the uncertainty types the FOMC perceives and their policy impact. Inflation uncertainty prompts a tighter stance\, unexplained by macroeconomic forecasts or public uncertainty indicators\, particularly when expected inflation nears or exceeds the target. The FOMC’s focus on upper-tail inflation risks explains this response\,\ndiverging from common models of policymaking under uncertainty. Narrative evidence connects tail-risk perceptions to credibility concerns. We thus highlight how the Fed’s risk management contributed to price stability in pre-pandemic decades\, with implications for future monetary policy frameworks.  \nOrganizer :  Alessandro RIBONI \n
URL:https://crest.science/event/stephen-hansen-university-college-london-t-b-a/
CATEGORIES:Macroeconomics,Seminars
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DTSTART;TZID=Europe/Helsinki:20260309T133000
DTEND;TZID=Europe/Helsinki:20260316T173000
DTSTAMP:20260710T035127
CREATED:20260212T083208Z
LAST-MODIFIED:20260212T083217Z
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SUMMARY:Xunyu Zhou (Columbia University) - Introduction to Continuous-Time Reinforcement Learning
DESCRIPTION:INTRODUCTION TO CONTINUOUS-TIME REINFORCEMENT LEARNING\nXUNYU ZHOU\nColumbia University\nIndustrial Engineering & Operations Research Department \nSchedule: \nMarch 9\, 2026 | 1:30PM – 5:30PM – Room 2006\nMarch 12\, 2026 | 1:30PM – 5:30PM – Room 2006\nMarch 16\, 2026 | 1:30PM – 5:30PM – Room 2006 \nContent:\nThis crash course covers fundamental theory and algorithms for reinforcement learning with continuous-time controlled diffusion processes\, which have been developed in the last five years. It includes the following topics.\n1. Exploration vs exploitation: relaxed control\, entropy regularization\, exploratory HJB equation and Gibbs measure.\n2. Gaussian exploration under linear-quadratic control: optimality of Gaussian exploration and cost of exploration.\n3. Temperature control of Langevin diffusions: simulated annealing for nonconvex optimization and optimal temperature control.\n4. Policy evaluation: martingale characterization\, martingale loss function and martingale orthogonality conditions.\n5. Policy gradient: policy gradient via policy evaluation\, and temporal difference learning. q-learning theory: generalized Hamiltonian and policy improvement\, Q-function and q-function\, martingale characterization of q-function. \nEvaluation: \nTake home project. \n
URL:https://crest.science/event/xunyu-zhou-columbia-university-introduction-to-continuous-time-reinforcement-learning/
CATEGORIES:Doctoral Courses,Finance
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DTSTART;TZID=Europe/Helsinki:20260309T140000
DTEND;TZID=Europe/Helsinki:20260309T153000
DTSTAMP:20260710T035127
CREATED:20260223T090907Z
LAST-MODIFIED:20260305T110038Z
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SUMMARY:Aymeric DIEULEVEUT (Ecole Polytechnique) "Will computers replace optimization researchers?  Computer-aided proofs in first-order optimization\, a case study on error feedback."
DESCRIPTION:Statistical Seminar: Every Monday at 2:00 pm.\nTime: 2:00 pm – 3:00 pm\nDate: 9th March\nPlace: 3001 \n  \nAymeric DIEULEVEUT (Ecole Polytechnique)  “Will computers replace optimization researchers? Computer-aided proofs in first-order optimization\, a case study on error feedback.” \n  \n Abstract: First-order methods are widely used in optimization and machine learning\, and their behavior often analyzed through the spectrum of worst case convergence rates. Obtaining such guarantees is often difficult and both time consuming and error-prone. Starting with the work of Drori and Teboulle (2014)\, novel techniques have been used to gain numerical insights\, leading to the release of various performance estimation (PE) software. \nIn this talk\, I will show how various computer-aided techniques can be used to study first-order optimization methods in a systematic way. From performance estimation problems with automated Lyapunov discovery\, to symbolic regression and computer algebra systems\, novel tools completely reshape the way we approach theory of optimization. \nAs a main example\, I will focus on error feedback methods used with compressed communication in distributed optimization. While error feedback has been widely studied\, existing theory often provides untight (thus unreliable) bounds. I will present tight analyses with matching lower bounds that allow a fair comparison between error feedback schemes and standard compressed gradient descent\, and help explain when error feedback is useful and when it is not. \n  \nOrganizers: \nAnna KORBA (CREST)\, Vincent DIVOL (CREST) \, Jaouad MOURTADA (CREST) \nSponsors:\nCREST-CMAP \n
URL:https://crest.science/event/aymeric-dieuleveut-ecole-polytechnique-tba/
CATEGORIES:Seminars,Statistics
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