BLISS Seminar


The BLISS seminar (formerly NCD seminar) is the area seminar of the Berkeley Laboratory for Information and System Sciences. Talks at the seminar cover topics including but not limited to information and coding theory, signal processing, optimization, statistics, and control. The list of talks for the current semester can be found below, and past seminars from 2016 onwards are listed here. For an archive of all talks from 1996-2015, visit the old webpage.

A calendar of all the talks is maintained here. Feel free to add it to your own.

Spring 2022
Location: Virtual (subscribe to our mailing list for details)
Regular seminar time: Wednesdays 10 AM - 11 AM PT

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To give a talk at the seminar, contact Nived Rajaraman or Tom Courtade.

Recordings of the talks from Spring 2022 are available here.
Recordings from Fall 2021 are available here.

Spring 2022 Talks

Dates marked in bold indicate that talks are at non-regular dates / times.

Jan 26 Qian Yu (Princeton) Coding Theory for Large-scale Distributed Computing details
Feb 2 Yury Polyanskiy (MIT) Rates of convergence of Gaussian smoothed empirical measures in Wasserstein and KL distances details
Feb 9 Chenguang Zhu (MSR) How We Achieved Human Parity in CommonsenseQA – Fusing Knowledge into Language Models details
Feb 18, 9:30-10:30 AM PT Kangwook Lee (Wisconsin-Madison) Improving Fairness via Federated Learning details
Mar 2 Ashwin Pananjady (Georgia Tech) Sharp convergence guarantees for iterative algorithms in random optimization problems details
Mar 9 Sai Praneeth Karimireddy (UC Berkeley) Byzantine robust collaborative learning details
Mar 16 speaker title details
Mar 30 Yuansi Chen (Duke) Localization schemes: A framework for proving mixing bounds for Markov chains details
Apr 6 Yuanzhi Li (CMU) Deep learning when the data set has multiple features details
Apr 13 Vidya Muthukumar (Georgia Tech) Classification versus regression in overparameterized regimes: Does the loss function matter? details
Apr 27 Dylan Foster (Microsoft Research) The Statistical Complexity of Interactive Decision Making details
May 4 Quanquan Gu (UCLA) Stochastic Gradient Descent: Benign Overfitting and Implicit Regularization details