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

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