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DTSTART:20220327T010000
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DTSTART;TZID=Europe/Helsinki:20220707T140000
DTEND;TZID=Europe/Helsinki:20220707T144500
DTSTAMP:20260731T072424
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SUMMARY:Charles MARGOSSIAN (Columbia)  - "Nested $\hat R$: Assessing the convergence of Markov chains Monte Carlo when running many short chains"
DESCRIPTION:Statistical Seminar: \nTime: 2:00 pm -2.45pm\nDate: 07th of July 2022\nPlace: Room 3001 \nCharles MARGOSSIAN (Columbia)  – “Nested $\hat R$: Assessing the convergence of Markov chains Monte Carlo when running many short chains” \nAbstract: The growing availability of hardware accelerators\, such as GPUs\, has generated interest in MCMC strategies where we run many chains in parallel. After the warmup phase\, precise Monte Carlo estimators can be constructed using a short sampling phase\, potentially with a single iteration. To implement this approach\, we need a reliable diagnostic for MCMC convergence. A natural candidate is the widely used $\hat R$ statistic\, also known as the potential scale reduction factor. I demonstrate shortcomings with $\hat R$ in the many-short-chains regime and present a useful generalization\, termed the Nested $\hat R$. In addition\, studying convergence diagnostics gives us principled guidelines to choose the number of chains\, as well as the length of the warmup and sampling phases — tuning parameters otherwise chosen using heuristics or trial-and-error.\n \n References: A preprint is available on arxiv\n(https://arxiv.org/pdf/2110.13017.pdf). This manuscript is currently being revised. A more recent version of this work can be found in my thesis (https://academiccommons.columbia.edu/doi/10.7916/0wsc-kz90\, chapter 3). \nOrganizers:\nCristina BUTUCEA (CREST)\, Alexandre TSYBAKOV (CREST)\, Karim LOUNICI (CMAP) \, Jaouad MOURTADA (CREST)\nSponsors:\nCREST-CMAP \n
URL:https://crest.science/event/charles-margossiancolumbia-nested-hat-r-assessing-the-convergence-of-markov-chains-monte-carlo-when-running-many-short-chains/
CATEGORIES:Statistics
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DTSTART;TZID=Europe/Helsinki:20220707T144500
DTEND;TZID=Europe/Helsinki:20220707T153000
DTSTAMP:20260731T072424
CREATED:20220622T070435Z
LAST-MODIFIED:20220622T070435Z
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SUMMARY:Mathieu GERBER (Bristol University) " A Global Stochastic Optimization Particle Filter Algorithm"
DESCRIPTION:Statistical Seminar: \nTime: 2:45 pm -3.30pm\nDate: 07th of July 2022\nPlace: Room 3001 \nMathieu GERBER (Bristol University) ” A Global Stochastic Optimization Particle Filter Algorithm” \nAbstract:We introduce a new online algorithm for expected log-likelihood maximization in situations where the objective function is multi-modal and/or has saddle points\, that we term G-PFSO. The key element underpinning G-PFSO is a probability distribution which (a) is shown to concentrate on the target parameter value as the sample size increases and (b) can be efficiently estimated by means of a standard particle filter algorithm. This distribution depends on a learning rate\, where the faster the learning rate the quicker it concentrates on the desired element of the search space\, but the less likely G-PFSO is to escape from a local optimum of the objective function. In order to achieve a fast convergence rate with a slow learning rate\, G-PFSO exploits the acceleration property of averaging\, well-known in the stochastic gradient literature. Considering several challenging estimation problems\, the numerical experiments show that\, with high probability\, G-PFSO successfully finds the highest mode of the objective function and converges to its global maximizer at the optimal rate. While the focus of this work is expected log-likelihood\nmaximization\, the proposed methodology and its theory apply more generally for optimizing a function defined through an expectation.\n \nJoint work : Randal Douc \nOrganizers:\nCristina BUTUCEA (CREST)\, Alexandre TSYBAKOV (CREST)\, Karim LOUNICI (CMAP) \, Jaouad MOURTADA (CREST)\nSponsors:\nCREST-CMAP \n
URL:https://crest.science/event/mathieu-gerber-bristol-university-a-global-stochastic-optimization-particle-filter-algorithm/
CATEGORIES:Statistics
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