BLISS Seminar


The BLISS seminar (formerly NCD seminar) is co-sponsored by generous grants from Microsoft Research and Qualcomm, and 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 from is maintained here. Feel free to add it to your own.

Fall 2019
Location: 400 Cory Hall
Regular seminar time: Wednesday 3-4PM

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To give a talk at the seminar, contact Tom Courtade, Vipul Gupta, or Ashwin Pananjady.

Fall 2019 Talks

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

Aug 28 Ramya Vinayak (UW Seattle) Learning from Sparse Data details
Sep 18 Arya Mazumdar (UMass) Sample complexity of mixture of sparse linear regressions details
Sep 25 Feng Ruan (UC Berkeley) Searching for Interactions in Linear Time details
Oct 2 Amir Gholami (UC Berkeley) Systematic Quantization of Neural Networks Through Second-Order Information details
Oct 9 Nhat Ho (UC Berkeley) Statistical and computational perspective of mixture and hierarchical models details
Oct 18 Jiaming Xu (Duke) Spectral graph matching and regularized quadratic relaxations details
Oct 30 Cheuk Ting Li (UC Berkeley) One-shot Information Theory via Poisson Processes details
Nov 6 Yuejie Chi (CMU) Distributed Stochastic Optimization with Variance Reduction and Gradient Tracking details
Nov 13 Guy Bresler (MIT) Towards an Average-case Complexity of High-dimensional Statistics details
Nov 20 Victoria Kostina (Caltech) Towards a Theory of Information for Dynamical Systems details
Dec 4 Animesh Kumar (IIT Bombay) On sampling and inference of spatial fields from samples taken by a location-unaware mobile sensor details
Dec 6 Jingbo Liu (MIT) Gaussian limits in two inference problems details