Zoltán Szabó: machine learning external seminar organizer @ Gatsby Unit (2014-2016; more on external seminars). Invited speakers, guests:

2016:
Paul Fearnhead. Continuous-time MCMC and super-efficient Monte Carlo for big data. [Nov. 2]
Clayton Scott. Mixture Proportion Estimation for Weakly Supervised Learning. [Oct. 12]
Lester Mackey. Measuring Sample Quality with Stein's Method. [Oct. 3]
Richard Wilkinson. Gaussian process accelerated ABC. [Sept. 21]
Hanna Wallach. Bayesian Poisson Tensor Decomposition for International Relations. [Sept. 7]
Daniel Hsu. Interactive machine learning via reductions to supervised learning. [July 27]
Le Song. Discriminative Embeddings of Latent Variable Models for Structured Data. [July 21]
Amir Globerson. Kernels for deep learning - with and without tricks. [July 20]
Kirthevasan Kandasamy. Multi-fidelity Bandit Optimisation. [July 12]
Sanmi Koyejo. From Probabilistic Models to Decision Theory and Back Again. [July 5]
Piotr Indyk. Fast Algorithms for Structured Sparsity. [May 16]
Bharath Sriperumbudur. Minimax Estimation of Kernel Mean Embeddings. [May 4]
Manfred Opper. Score matching and nonparametric estimators of drift functions for stochastic differential equations. [Apr. 13]
Francois Caron. Sparse and modular networks using exchangeable random measures. [Apr. 6]
Joan Bruna. Convolutional Neural Networks against the curse of dimensionality. [Mar. 21]
Kamalika Chaudhuri. Challenges in Privacy-Preserving Data Analysis. [Mar. 2]
Eric Moulines. Sampling from log-concave non-smooth densities, when Moreau meets Langevin. [Feb. 24]
Julien Mairal. A Universal Catalyst for First-Order Optimization. [Jan. 26]
Lorenzo Rosasco. Less is more: optimal learning with subsampling regularization. [Jan. 13]
Pradeep Ravikumar. The Distributional Rank Aggregation Problem, and an Axiomatic Analysis. [Jan. 5]
2015:
Tamara Broderick. Statistical and computational trade-offs in Bayesian learning. [Dec. 16]
Csaba Szepesvári. (Bandit) Convex Optimization with Biased Noisy Gradient Oracles. [Nov. 10]
Gilles Blanchard. Convergence rates of spectral regularization methods for statistical inverse learning problems. [Nov. 4]
Patricia Reynaud-Bouret, Magalie Fromont Renoir. Estimation of local independence graphs via Hawkes processes to unravel functional neuronal connectivity. [Nov. 2]
Sebastian Nowozin. Bayesian Time-of-Flight for Realtime Shape, Illumination and Albedo. [Oct. 21]
Cynthia Rudin. On Using Predictive Models for Decisions. [Sept. 16]
Pedro Ortega. Information-Theoretic Bounded Rationality. [Sept. 8]
Kristen Grauman. Learning the right thing with visual attributes. [July 29]
Maria-Florina Balcan. Learning Submodular Functions. [July 28]
Sophie Achard. Hubs of brain functional networks are radically reorganized in comatose patients. [June 23]
Barnabás Póczos. Machine Learning on Functional Data. [June 9]
Sinead Williamson. Bayesian nonparametric models for prediction in networks. [May 20]
Nick Whiteley. Particle filtering subject to interaction constraints. [Apr. 28]
Max Welling. Bayesian Inference in Complex Generative Models. [Apr. 15]
Emma Brunskill. Faster Learning for Better Decisions [Mar. 18]
Cordelia Schmid. Weakly supervised learning from images and videos [Mar. 11]
Madalina Fiterau. Ensembles for Discovery of Compact Structures and Learning Back-propagation Forests [Mar. 9]
Claire Monteleoni. Climate Informatics: Recent Advances and Challenge Problems for Machine Learning in Climate Science [Mar. 6]
Anthony Lee. Perfect simulation using atomic regeneration with application to Sequential Monte Carlo. [Feb. 11]
Jonas Peters. Causal Inference using Invariant Prediction. [Jan. 28]
Gérard Biau. Distributed Statistical Algorithms. [Jan. 13]
2014:
Motonobu Kanagawa. Monte Carlo Filtering using Kernel Embedding of Distributions. [Nov. 19]
Neil Lawrence. Approximate Inference in Deep GPs. [Oct. 23]
Le Song. Scalable Kernel Embedding of Latent Variable Models. [Oct. 21]
Marco Cuturi. The Wasserstein Barycenter Problem: Formulation, Computation and Applications. [Sept. 23]
Guillaume Obozinski. Tight convex relaxations for sparse matrix factorization. [Sept. 10]
Franz Király. Learning with Cross-Kernels and Ideal PCA. [July 30]
Mark Plumbley. Sustainable Software for Reproducible Research in Audio and Music. [July 25]
Geoff Gordon. A tutorial on spectral and predictive state learning. [July 16]
Volkan Cevher. Composite Self-concordant Minimization. [June 12]
Jun Zhang. Regularized Learning in Reproducing Kernel Banach Spaces. [May 7]