109 citations · 504 across the 55 of their papers we have counts for
6 papers · 1 filter
Misspecified Gaussian Process Bandit Optimization
Ilija Bogunovic, Andreas Krause
We consider the problem of optimizing a black-box function based on noisy bandit feedback. Kernelized bandit algorithms have shown strong empirical and theoretical performance for…
Risk-averse Heteroscedastic Bayesian Optimization
Anastasiia Makarova, Ilnura Usmanova, Ilija Bogunovic +1
Many black-box optimization tasks arising in high-stakes applications require risk-averse decisions. The standard Bayesian optimization (BO) paradigm, however, optimizes the expect…
Contextual Games: Multi-Agent Learning with Side Information
Pier Giuseppe Sessa, Ilija Bogunovic, Andreas Krause +1
We formulate the novel class of contextual games, a type of repeated games driven by contextual information at each round. By means of kernel-based regularity assumptions, we model…
Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning
Barna Pásztor, Ilija Bogunovic, Andreas Krause
Learning in multi-agent systems is highly challenging due to several factors including the non-stationarity introduced by agents' interactions and the combinatorial nature of their…
Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning
Sebastian Curi, Ilija Bogunovic, Andreas Krause
In real-world tasks, reinforcement learning (RL) agents frequently encounter situations that are not present during training time. To ensure reliable performance, the RL agents nee…
Cost-Efficient Online Hyperparameter Optimization
Jingkang Wang, Mengye Ren, Ilija Bogunovic +2
Recent work on hyperparameters optimization (HPO) has shown the possibility of training certain hyperparameters together with regular parameters. However, these online HPO algorith…