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Machine learning reading group

Imperial College London

Welcome to the homepage of the Imperial College London machine learing reading group. We meet Fridays at 13:00 in Huxley 658. Suggested papers can be found here.

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Schedule

Date Presenter Paper Author(s) Notes
17/10/19 George Approximate Inference Turns Deep Networks into Gaussian Processes Mohammad Emtiyaz Khan et al.  
24/10/19 Onur Random Tessellation Forests Shufei Ge et al.  
31/10/19 Arinbjörn A Scalable Laplace Approximation for Neural Networks Hippolyt Ritter, Aleksandar Botev and David Barber  
07/11/19 Kate Reconciling modern machine learning practice and the bias-variance trade-off Mikhail Belkin, Daniel Hsu, Siyuan Ma and Soumik Mandal  
14/11/19 Alex An explicit link between Gaussian fields and Gaussian Markov random fields: The SPDE approach Finn Lindgren, Håvard Rue and Johan Lindström  
21/11/19 Sesh Lost Relatives of the Gumbel Trick Matej Balog et al.  
Break for workshop        
05/12/19 Daniel Modelling the Dynamics of Multiagent Q-Learning in Repeated Symmetric Games: a Mean Field Theoretic Approach Shuyue Hu, Chin-Wing Leung, Ho-fung Leung  
12/12/19 Tim A Model to Search for Synthesizable Molecules John Bradshaw et al.  
Break        
20/01/20 Jonathan The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions Raj Agrawal et al.  
31/01/20 Emily Boosting Variational Inference Guo et al.  
07/02/20 George Determinantal point processes for machine learning (chapters 2 and 4) Alex Kulesza and Ben Taskar  
14/02/20 Kate On Bayesian new edge prediction and anomaly detection in computer networks Silvia Metelli and Nicholas Heard  
21/02/20        
28/02/20 Adriaan A scalable bootstrap for massive data Ariel Kleiner et al.  
06/03/20 Isak Super-Samples from Kernel Herding Yutian Chen, Max Welling and Alex Smola  
13/03/20 break      
20/03/20 Jonathan & Arinbjörn Benchmarking Bayesian Deep Learning with Diabetic Retinopathy Diagnosis Angelos Filos et al.  
27/03/20 Kate Integrated Clustering and Anomaly Detection (INCAD) for Streaming Data Sreelekha Guggilam et al.  
03/04/20 Emily Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders Nat Dilokthanakul et al.  
10/04/20 break      
17/04/20 Daniel Exploring Generalization in Deep Learning Neyshabur et al.  
24/04/20 Jonathan Unsupervised Data Augmentation for Consistency Training Qizhe Xie et al.  
01/05/20 Kai Discovering the Compositional Structure of Vector Representations with Role Learning Networks Paul Soulos et al. Slides
08/05/20 Bank holiday break      
15/05/20 Sesh Alleviating Label Switching with Optimal Transport Pierre Monteiller et al.  
22/05/20 Hans A latent Markov model for detecting patterns of criminal activity Francesco Bartolucci et al.  
29/05/20 Isak Introduction to Coresets: Accurate Coresets Ibrahim Jubran et al. Slides
05/06/20 Adriaan Weight Uncertainty in Neural Networks Charles Blundell et al.  
12/06/20 Kai The frontier of simulation-based inference Kyle Cranmer et al. Slides
19/06/20 Daniel Sharp Minima Can Generalize For Deep Nets Laurent Dinh et al.  
26/06/20 Janith A General Theory of Equivariant CNNs on Homogeneous Spaces Taco S. Cohen, Mario Geiger & Maurice Weiler  
03/07/20 Alex On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems Panayotis Mertikopoulos, Nadav Hallak, Ali Kavis & Volkan Cevher  
10/07/20 Kate Nonparametric Variational Auto-encoders for Hierarchical Representation Learning Prasoon Goyal et al.  
17/07/20 Harrison Bayesian Probabilistic Numerical Integration with Tree-Based Models Harrison Zhu et al.  
24/07/20 Zaf      
31/07/20 Kai      
07/08/20 Aravind      
14/08/20        
21/08/20        
28/08/20