47 citations · 72 across the 5 of their papers we have counts for
6 papers
B3O: Scalable Boltzmann Batch Bayesian Optimization
Maximilian Bloor, Liyuan Xu, Hrvoje Stojic +1
Modern engineering workflows increasingly rely on massive parallel simulation, driving the need for scalable, large-batch Bayesian Optimization (BO). Existing batch BO methods, how…
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…
Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlow
John McLeod, Hrvoje Stojic, Vincent Adam +4
In the past decade, model-free reinforcement learning (RL) has provided solutions to challenging domains such as robotics. Model-based RL shows the prospect of being more sample-ef…
An empirical evaluation of active inference in multi-armed bandits
Dimitrije Markovic, Hrvoje Stojic, Sarah Schwoebel +1
A key feature of sequential decision making under uncertainty is a need to balance between exploiting--choosing the best action according to the current knowledge, and exploring--o…
Ranking-based rich-get-richer processes
Pantelis P. Analytis, Alexandros Gelastopoulos, Hrvoje Stojic
We study a discrete-time Markov process , for which the distribution of the future increments depends only on the relative ranking of its components (descending…
Diversity of preferences can increase collective welfare in sequential exploration problems
Pantelis P. Analytis, Hrvoje Stojic, Alexandros Gelastopoulos +1
In search engines, online marketplaces and other human-computer interfaces large collectives of individuals sequentially interact with numerous alternatives of varying quality. In…