989 citations · 4.1k across the 80 of their papers we have counts for
12 papers · 1 filter
Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation
Ilija Bogunovic, Jonathan Scarlett, Andreas Krause +1
We present a new algorithm, truncated variance reduction (TruVaR), that treats Bayesian optimization (BO) and level-set estimation (LSE) with Gaussian processes in a unified fashio…
Guaranteed Non-convex Optimization: Submodular Maximization over Continuous Domains
Andrew An Bian, Baharan Mirzasoleiman, Joachim M. Buhmann +1
Submodular continuous functions are a category of (generally) non-convex/non-concave functions with a wide spectrum of applications. We characterize these functions and demonstrate…
Safe Exploration in Finite Markov Decision Processes with Gaussian Processes
Matteo Turchetta, Felix Berkenkamp, Andreas Krause
In classical reinforcement learning, when exploring an environment, agents accept arbitrary short term loss for long term gain. This is infeasible for safety critical applications,…
Horizontally Scalable Submodular Maximization
Mario Lucic, Olivier Bachem, Morteza Zadimoghaddam +1
A variety of large-scale machine learning problems can be cast as instances of constrained submodular maximization. Existing approaches for distributed submodular maximization have…
Actively Learning Hemimetrics with Applications to Eliciting User Preferences
Adish Singla, Sebastian Tschiatschek, Andreas Krause
Motivated by an application of eliciting users' preferences, we investigate the problem of learning hemimetrics, i.e., pairwise distances among a set of items that satisfy tria…
Tradeoffs for Space, Time, Data and Risk in Unsupervised Learning
Mario Lucic, Mesrob I. Ohannessian, Amin Karbasi +1
Faced with massive data, is it possible to trade off (statistical) risk, and (computational) space and time? This challenge lies at the heart of large-scale machine learning. Using…