2 citations · 2 across the 1 of their papers we have counts for
6 papers
Adversarial Causal Bayesian Optimization
Scott Sussex, Pier Giuseppe Sessa, Anastasiia Makarova +1
In Causal Bayesian Optimization (CBO), an agent intervenes on an unknown structural causal model to maximize a downstream reward variable. In this paper, we consider the generaliza…
Safe Risk-averse Bayesian Optimization for Controller Tuning
Christopher Koenig, Miks Ozols, Anastasia Makarova +3
Controller tuning and parameter optimization are crucial in system design to improve both the controller and underlying system performance. Bayesian optimization has been establish…
Cherry-Picking Gradients: Learning Low-Rank Embeddings of Visual Data via Differentiable Cross-Approximation
Mikhail Usvyatsov, Anastasia Makarova, Rafael Ballester-Ripoll +3
We propose an end-to-end trainable framework that processes large-scale visual data tensors by looking at a fraction of their entries only. Our method combines a neural network enc…
Automatic Termination for Hyperparameter Optimization
Anastasia Makarova, Huibin Shen, Valerio Perrone +5
Bayesian optimization (BO) is a widely popular approach for the hyperparameter optimization (HPO) in machine learning. At its core, BO iteratively evaluates promising configuration…
Hierarchical Image Classification using Entailment Cone Embeddings
Ankit Dhall, Anastasia Makarova, Octavian Ganea +3
Image classification has been studied extensively, but there has been limited work in using unconventional, external guidance other than traditional image-label pairs for training.…
Mixed-Variable Bayesian Optimization
Erik Daxberger, Anastasia Makarova, Matteo Turchetta +1
The optimization of expensive to evaluate, black-box, mixed-variable functions, i.e. functions that have continuous and discrete inputs, is a difficult and yet pervasive problem in…