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Seminar series in Probability and Statistics

We are happy to announce that our seminar series is back to its physical, on-campus format.

The Seminar Series in Probability and Statistics is a lecture series of invited speakers in the fields of probability, statistics, and related fields, organized every second week. The talks are in seminar format, aimed at a broad audience ranging from graduate students of probability and statistics to advanced researchers of these and related fields. The seminar usually lasts an hour with roughly 35-45 minutes of presentation and the remaining time spent on questions and discussion. This year we introduce an informal gathering immediately after the seminar, with some drinks and snacks/cookies triggering further discussion.

PhD students from EEMCS will receive 1 Graduate School credit from the Graduate School for attending 6 seminars of the series.

Gebruik de volgende webcal om de feed toe te voegen aan de kalender of kopieer de link om handmatig te abonneren.

webcal://www.tudelft.nl/ewi/over-de-faculteit/afdelingen/applied-mathematics/applied-probability/events/seminars-in-probability-and-statistics?tx_lookupfeed_feed%5Baction%5D=ical&tx_lookupfeed_feed%5Bcontroller%5D=Feed&tx_lookupfeed_feed%5Blimit%5D=15&tx_lookupfeed_feed%5BlookupUid%5D=1095775&type=1657271091&cHash=b7bcce2c2bd66deb584cdbbfa7abcbbd

Meer info over webcal.

13 januari 2025 15:45 t/m 16:45

[STAT/AP] Rianne de Heide: Recent developments in e-value-based multiple testing

20 januari 2025 15:45 t/m 16:45

[STAT/AP] Julia Olkhovskaya: Challenges and recent progress in reinforcement learning for large-scale environments.

Reinforcement learning (RL) has gained considerable attention recently as a framework for making sequential decisions in uncertain environments. While most of the existing research focuses on settings with a finite number of state-action pairs (the tabular setting) or simple models, like linear state-action value functions, it remains challenging to develop an RL algorithm that can efficiently manage large state-action spaces with more complex value functions. Recent studies have looked into nonlinear function approximations, with kernel ridge regression offering a natural step between linear models and neural networks. I will discuss the range of model complexities - from tabular representations to linear and nonlinear function approximations - and highlight some ongoing results and open problems in achieving statistical efficiency in these areas.

When:

Mondays from 4pm to 5 pm
(unless otherwise specified)

Give a talk?

We are excited to hear from you! Please click here or contact us directly.

Organizers

J. (Júlia) Komjáthy

F. (Fabian) Mies

Past talks

Seminar Archives
Photo by Edge2Edge Media on Unsplash