most citedTrieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow

21 citations · 21 across the 7 of their papers we have counts for

collaborators

6 papers

cs.LG2023

Robust Best-arm Identification in Linear Bandits

Wei Wang, Sattar Vakili, Ilija Bogunovic

We study the robust best-arm identification problem (RBAI) in the case of linear rewards. The primary objective is to identify a near-optimal robust arm, which involves selecting a…

stat.ML2023

Adversarial Contextual Bandits Go Kernelized

Gergely Neu, Julia Olkhovskaya, Sattar Vakili

We study a generalization of the problem of online learning in adversarial linear contextual bandits by incorporating loss functions that belong to a reproducing kernel Hilbert spa…

cs.CV2023

Image generation with shortest path diffusion

Ayan Das, Stathi Fotiadis, Anil Batra +5

The field of image generation has made significant progress thanks to the introduction of Diffusion Models, which learn to progressively reverse a given image corruption. Recently,…

stat.ML202321 cited

Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow

Victor Picheny, Joel Berkeley, Henry B. Moss +13

We present Trieste, an open-source Python package for Bayesian optimization and active learning benefiting from the scalability and efficiency of TensorFlow. Our library enables th…

cs.LG2023

Sample Complexity of Kernel-Based Q-Learning

Sing-Yuan Yeh, Fu-Chieh Chang, Chang-Wei Yueh +3

Modern reinforcement learning (RL) often faces an enormous state-action space. Existing analytical results are typically for settings with a small number of state-actions, or simpl…

stat.ML2023

Delayed Feedback in Kernel Bandits

Sattar Vakili, Danyal Ahmed, Alberto Bernacchia +1

Black box optimisation of an unknown function from expensive and noisy evaluations is a ubiquitous problem in machine learning, academic research and industrial production. An abst…