University College London (COMP0168)
This course is designed to introduce students to “trending” topics within the last five years as represented in international machine learning conferences. The backbone of the course will be a series of lectures on a given set of selected topics. This will be supplemented by seminar-style class work, where current research papers are read in common, discussed, and presented.
Syllabus
- Gaussian Processes (please use Acrobat Reader for the animations) — Marc Deisenroth
- Bayesian Optimization — Marc Deisenroth
- Bayesian Deep Learning — Brooks Paige
- Integration in Machine Learning — Marc Deisenroth
- Meta Learning — Brooks Paige
Lectures
The course will be delivered 100% online. Lecture recordings will be available for viewing at home. We will have live Q&A in allocated time slots. We will have discussion forums and virtual social gathering opportunities as well as peer mentoring.
Teaching Assistants
- Yicheng Luo
- Janith Petangoda
Resources
Gaussian Processes
- Pathwise Conditioning of Gaussian Processes.
- Sparse Orthogonal Variational Inference for Gaussian Processes
- Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters
- Deep Gaussian Processes
- Healing Products of Gaussian Process Experts
- Variational Learning of Inducing Variables in Sparse Gaussian Processes
- Variational Fourier Features for Gaussian Processes
- Deep Kernel Learning
- Exact Gaussian Processes on a Million Data Points
- Infinite-Horizon Gaussian Processes
- A Unifying View of Sparse Approximate Gaussian Process Regression
- Approximations for Binary Gaussian Process Classification
- Gaussian Process Modulated Cox Processes under Linear Inequality Constraints
- Convolutional Gaussian Processes
- Conditional Neural Processes
- Inter-domain Gaussian Processes for Sparse Inference using Inducing Features
- Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
- Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies
- Randomly Projected Additive Gaussian Processes for Regression
- Sparse Gaussian Processes with Spherical Harmonic Features
- Parametric Gaussian Process Regressors
- Inter-domain Deep Gaussian Processes
- State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes
- Gaussian Processes for Data-Efficient Learning in Robotics and Control
- Matern Gaussian Processes on Riemannian Manifolds
- Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences
- Learning Invariances using the Marginal Likelihood
Bayesian Optimization
- Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design
- A General Framework for Constrained Bayesian Optimization using Information-based Search
- Bayesian Optimization with Inequality Constraints
- Bayesian Optimization in a Billion Dimensions via Random Embeddings
- Bayesian Optimization with Unknown Constraints
- Entropy Search for Information-Efficient Global Optimization
- An Efficient Approach for Assessing Hyperparameter Importance
- Scalable Bayesian Optimization Using Deep Neural Networks
- Freeze-Thaw Bayesian Optimization
- Maximizing Acquisition Functions for Bayesian Optimization
- Modulating Surrogates for Bayesian Optimization
- Projective Preferential Bayesian Optimization
- Multi-objective Bayesian Optimization using Pareto-frontier Entropy
- A General Framework for Multi-fidelity Bayesian Optimization with Gaussian Processes
- Multi-objective Bayesian optimisation with preferences over objectives
- Stagewise Safe Bayesian Optimization with Gaussian Processes
Integration in Machine Learning
- Unscented Filtering and Nonlinear Estimation
- Normalizing Flows on Tori and Spheres
- Riemannian Continuous Normalizing Flows
- Probabilistic Numerics and Uncertainty in Computations
- Classical Quadrature Rules via Gaussian Processes
- Optimal Monte Carlo Integration on Closed Manifolds
- On the Relation Between Gaussian Process Quadratures and Sigma-Point Methods
- Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees
- Bayesian quadrature for ratios
- Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature
- Bandit Based Monte-Carlo Planning
- Monte Carlo Gradient Estimation in Machine Learning
- Spatiotemporal Learning via Infinite-Dimensional Bayesian Filtering and Smoothing: A Look at Gaussian Process Regression Through Kalman Filtering
- On Sequential Monte Carlo Sampling Methods for Bayesian Filtering