I have moved to France (Sept., 2016); my new webpage.

Zoltán Szabó: SPC, organizing activity, workshop/masterclass attendance. For reviewing activities (conference/journal) see my CV.

SPC:
AISTATS-2017, UAI-2016.
Organizing:
Adaptive and Scalable Nonparametric Methods in Machine Learning workshop @ NIPS-2016. Co-organizers: Aaditya Ramdas, Bharath K. Sriperumbudur, Han Liu, John Lafferty, Mladen Kolar, Samory Kpotufe.
I served as a workflow chair at AISTATS-2016 with Rodolphe Jenatton.
Modern Nonparametrics 3: Automating the Learning Pipeline @ NIPS-2014. Co-organizers: Arthur Gretton, Mladen Kolar, Samory Kpotufe, Han Liu, Andrew G. Wilson, Eric Xing, Le Song.
ML external seminars at the Gatsby Unit (2014-2016).
Attending (Dagstuhl Seminar):
New Directions for Learning with Kernels and Gaussian Processes. [Nov. 27 - Dec. 2, 2016]
Attending (Turing Lecture):
The Intersection of Mathematics, Statistics and Computation (Andrea Bertozzi, Mark Girolami) [July 8, 2016]
Attending (workshop):
A Day of Ethical AI [@Oxford; June 8, 2016]
Gaussian Processes for Global Optimization [@Sheffield; Sept. 17, 2015]
Autonomous Citizens: Algorithms for Tomorrow's Society [@Warwick; Sept. 3-4, 2015]
Big Data and Computational Scalability [@Warwick; July 1, 2015]
Bayesian Inference for Big Data workshop [BIBiD@Oxford; June 15, 2015]
Attending (masterclass, @London):
Empirical process theory tools for statistics (Jon A. Wellner) [Oct. 29-30, 2015]
Machine Learning at Scale: Big Data with Small Clusters (Carlos Guestrin) [July 2, 2015]
Leveraging Optimization Techniques to Scale Bayesian Inference (Emily Fox) [July 2, 2015]
How much computation is required in order to achieve statistical efficiency? Tensor Decompositions for Learning Latent Variable Models. (Sham Kakade) [Nov. 3-5, 2014]
Clustering. Use of unlabeled and weakly labeled data. Efficient computations on well behaved inputs. (Shai Ben-David) [July 21-23, 2014]
Little Data: How traditional statistical ideas remain relevant in a big-data world. Parameterization and Bayesian modeling. Weakly informative priors. (Andrew Gelman) [Apr. 14-16, 2014]
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