47 citations · 57 across the 4 of their papers we have counts for
8 papers
Bias-Robust Bayesian Optimization via Dueling Bandits
Johannes Kirschner, Andreas Krause
We consider Bayesian optimization in settings where observations can be adversarially biased, for example by an uncontrolled hidden confounder. Our first contribution is a reductio…
Efficient Pure Exploration for Combinatorial Bandits with Semi-Bandit Feedback
Marc Jourdan, Mojmír Mutný, Johannes Kirschner +1
Combinatorial bandits with semi-bandit feedback generalize multi-armed bandits, where the agent chooses sets of arms and observes a noisy reward for each arm contained in the chose…
Asymptotically Optimal Information-Directed Sampling
Johannes Kirschner, Tor Lattimore, Claire Vernade +1
We introduce a simple and efficient algorithm for stochastic linear bandits with finitely many actions that is asymptotically optimal and (nearly) worst-case optimal in finite time…
Distributionally Robust Bayesian Optimization
Johannes Kirschner, Ilija Bogunovic, Stefanie Jegelka +1
Robustness to distributional shift is one of the key challenges of contemporary machine learning. Attaining such robustness is the goal of distributionally robust optimization, whi…
Information Directed Sampling for Linear Partial Monitoring
Johannes Kirschner, Tor Lattimore, Andreas Krause
Partial monitoring is a rich framework for sequential decision making under uncertainty that generalizes many well known bandit models, including linear, combinatorial and dueling…
Stochastic Bandits with Context Distributions
Johannes Kirschner, Andreas Krause
We introduce a stochastic contextual bandit model where at each time step the environment chooses a distribution over a context set and samples the context from this distribution.…