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 biasvariance tradeoff 
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 QLearning in Repeated Symmetric Games: a Mean Field Theoretic Approach 
Shuyue Hu, ChinWing Leung, Hofung 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 
SuperSamples 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 simulationbased 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 NonConvex Problems 
Panayotis Mertikopoulos, Nadav Hallak, Ali Kavis & Volkan Cevher 

10/07/20 
Kate 
Nonparametric Variational Autoencoders for Hierarchical Representation Learning 
Prasoon Goyal et al. 

17/07/20 
Harrison 
Bayesian Probabilistic Numerical Integration with TreeBased Models 
Harrison Zhu et al. 

24/07/20 
Zaf 
Deep active inference agents using MonteCarlo methods 
Zafeirios Fountas et al. 

31/07/20 
Kai 
Transforming task representations to allow deep learning models to perform novel tasks 
Andrew K. Lampinen & James L. McClelland 
Slides 
07/08/20 
Aravind 
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests 
Stefan Wager & Susan Athey 

14/08/20 
Summer break 



21/08/20 
Summer break 



28/08/20 
Adriaan 
Monte Carlo (importance) sampling within a benders decomposition algorithm for stochastic linear programs 
Gerd Infanger 
Whiteboard notes 
04/09/20 
Jonathan 
NGBoost: Natural Gradient Boosting for Probabilistic Prediction 
Tony Duan et al. 

11/09/20 
Chatura 
Probabilistic ValueDeviationBounded Integer Codes for Approximate Communication 
Phillip StanleyMarbell & Paul Hurley 

18/09/20 




25/09/20 
Janith 
On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groups 
Risi Kondor & Shubhendu Trivedi 
