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WMLG Reading Group
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Warwick Machine Learning Reading Group

Our reading group takes place biweekly during term time between 3 and 4pm on Thursdays, and is currently hosted on Microsoft Teams. This usually consists of short talks on a broad range of Machine Learning topics.

To sign up to the mailing list please visit the webpage.

Organizers

(2021-2022) Harita Dellaporta & Maud Lemercier
(2020-2021) Maud Lemercier
(2019-2020) Omer Deniz Akyildiz

Past reading groups

31 March 2022, Thursday, at 3PM, by Connor Duffin

The paper/topic: Statistical finite element methods for nonlinear PDEs


17 March 2022, Thursday, at 3PM, by Jan Povala

The paper/topic: Variational Bayesian Approximation of Inverse Problems using Sparse Precision Matrices


3 March 2022, Thursday, at 3PM, by Oscar Key

The paper/topic: Composite Goodness-of-fit Tests with Kernels


17 February 2022, Thursday, at 3PM, by Fabio Zennaro

The paper/topic: Abstracting Causal Structural Models


2 December 2021, Thursday, at 3PM, by Harrison Zhu

The paper/topic: Multi-resolution Spatial Regression for Aggregated Data with an Application to Crop Yield Prediction


25 November 2021, Thursday, at 3PM, by Takuo Matsubara

The paper/topic: Robust Generalised Bayesian Inference for Intractable Likelihoods


11 November 2021, Thursday, at 3PM, by Ollie Hamelijnck

The paper/topic: Spatio-Temporal Variational Gaussian Processes


21 October 2021, Thursday, at 3PM, by Patrick O'Hara

The paper/topic: Software toolkit for machine learning projects


17 June 2021, Thursday, at 3PM, by Alan Chau

The paper/topic: BayeSIMP: Uncertainty Quantification for Causal Data Fusion


3 June 2021, Thursday, at 3PM, by Lorenzo Pacchiardi

The paper/topic: Generalized Bayesian Likelihood-Free Inference Using Scoring Rules Estimators


18 March 2021, Thursday, at 3PM, by Nicola Branchini

The paper/topic: Causal discovery with continuous optimization


4 March 2021, Thursday, at 3PM, by Patrick O'Hara

The paper/topic: Kernels on Graphs


18 February 2021, Thursday, at 3PM, by Cris Salvi

The paper/topic: Computing the untruncated signature kernel as the solution of a Goursat problem


21 January 2021, Thursday, at 3PM, by Patrick Kidger

The paper/topic: Neural Controlled Differential Equations: continuous RNNs, irregular time series, GANs


3 December 2020, Thursday, at 3PM, by Jeremias Knoblauch

The paper/topic: Optimal Continual Learning has Perfect Memory and is NP-hard


5 November 2020, Thursday, at 3PM, by Ayman Boustati

The paper/topic: Generalized Bayesian Filtering via Sequential Monte Carlo


22 October 2020, Thursday, at 3PM, by Nicola Branchini

The paper/topic: Optimized Auxiliary Particle Filters


1 October 2020, Thursday, at 3PM, by Nicola Branchini

The paper/topic: Gaussian Processes for Aggregated Data


17 September 2020, Thursday, at 3PM, by Maud Lemercier

The paper/topic: Sparse Gaussian Processes with Spherical Harmonic Features (Paper reading)


3 September 2020, Thursday, at 3PM, by Virgina Aglietti

The paper/topic: Multi-task Causal Learning with Gaussian Processes


20 August 2020, Thursday, at 3PM, by Juan Maroñas

The paper/topic: Transforming Gaussian Processes With Normalizing Flows


27 February 2020, Thursday, at 3PM, George Wynne, @Lovelace room, 1st floor.

The paper/topic: Convergence Guarantees for Gaussian Process Approximations Under Several Observation Models


20 February 2020, Thursday, at 3PM, Neil Dhir, @Turingery room, 4th floor.

The paper/topic: Gaussian Processes, Lions, Robots, Parkinson’s disease, Causal Inference, Cramer–Rao Bound and Empowerment - how I stopped worrying and learned to love Bayesian nonparametrics.


13 February 2020, Thursday, at 3PM, None

Cancelled by the speaker.


6 February 2020, Thursday, at 3PM, None

Cancelled due to the ICML deadline.


30 January 2020, Thursday, at 3PM, Virginia Aglietti

The paper/topic: Causal Bayesian Optimisation


23 January 2020, Thursday, at 3PM, James Walsh.

The paper/topic: The Conjugate Gradient Method.

Cancelled.


16 January 2020, Thursday, at 3PM, Omer Deniz Akyildiz.

(Merged with Convex Optimization group this week, Reading material: Chapter 3, Convex Optimization, S. Boyd).


12 December 2019, Thursday, at 3PM, by Ayman Boustati.

The paper/topic: Nonlinear Multitask Learning with Deep GPs


5 December 2019, Thursday, at 3PM, by Ayman Boustati.

The paper/topic: Amortized Variance Reduction for Doubly Stochastic Objectives


21 November 2019, Thursday, at 3PM, by Kathryn Leeming.

The paper/topic: Forecasting traffic speeds in Bristol using time series methods


14 November 2019, Thursday, at 3PM, by Michael Smith.

The paper/topic: Gaussian Processes for Low cost Air Pollution monitoring in Kampala


7 November 2019, Thursday, at 3PM, Daniel J. Tait.

The paper/topic: Constrained Variational Inference


24 October 2019, Thursday, at 3PM, by Theo Damoulas.

The paper: Bayesian analysis of binary and polychotomous response data [pdf]


17 October 2019, Thursday, at 3PM, by Patrick O'Hara.

1st Convex Optimization Reading Group, the book: Convex Optimization, Stephen Boyd.


3 October 2019, Thursday, at 3PM, by Ollie Hamelijnck.

The paper: Variational Fourier Features for Gaussian Processes


25 September 2019, Wednesday, at 3PM, by Omer Deniz Akyildiz.

The paper: Convergence rates for optimised adaptive importance samplers


29 July 2019, Monday, at 4PM, by Daniel J. Tait.

The paper: The Statistical Finite Element Method (Paper reading)


22 July 2019, Monday, at 4PM, by Daniel J. Tait.

The paper: The Statistical Finite Element Method (Cont.)

Background: Introduction to PDEs (2)


15 July 2019, Monday, at 4PM, by Daniel J. Tait.

The paper: The Statistical Finite Element Method

Background: Introduction to PDEs


8 July 2019, Monday, at 4PM, by Omer Deniz Akyildiz.

The paper: Integral Transforms from Finite Data: An Application of Gaussian Process Regression to Fourier Analysis


1 April 2019, at 3PM, by Ollie Hamelijnck.

Papers

  1. State-Space Inference and Learning with Gaussian Processes

  2. Identification of Gaussian Process State Space Models


1-30 March 2019, by Omer Deniz Akyildiz.

The book: Bayesian Filtering and Smoothing, Chapters 2-5 and 8