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38 papers · 1 filter
Riemannian Continuous Normalizing Flows
Emile Mathieu, Maximilian Nickel
Normalizing flows have shown great promise for modelling flexible probability distributions in a computationally tractable way. However, whilst data is often naturally described on…
Equitable and Optimal Transport with Multiple Agents
Meyer Scetbon, Laurent Meunier, Jamal Atif +1
We introduce an extension of the Optimal Transport problem when multiple costs are involved. Considering each cost as an agent, we aim to share equally between agents the work of t…
Variational Auto-Regressive Gaussian Processes for Continual Learning
Sanyam Kapoor, Theofanis Karaletsos, Thang D. Bui
Through sequential construction of posteriors on observing data online, Bayes' theorem provides a natural framework for continual learning. We develop Variational Auto-Regressive G…
Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization
Samuel Daulton, Maximilian Balandat, Eytan Bakshy
In many real-world scenarios, decision makers seek to efficiently optimize multiple competing objectives in a sample-efficient fashion. Multi-objective Bayesian optimization (BO) i…
A Simple Convergence Proof of Adam and Adagrad
Alexandre Défossez, Léon Bottou, Francis Bach +1
We provide a simple proof of convergence covering both the Adam and Adagrad adaptive optimization algorithms when applied to smooth (possibly non-convex) objective functions with b…
Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization
Benjamin Letham, Roberto Calandra, Akshara Rai +1
Bayesian optimization (BO) is a popular approach to optimize expensive-to-evaluate black-box functions. A significant challenge in BO is to scale to high-dimensional parameter spac…