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5 papers · 2 filters
Probabilistic Super-Resolution of Solar Magnetograms: Generating Many Explanations and Measuring Uncertainties
Xavier Gitiaux, Shane A. Maloney, Anna Jungbluth +7
Machine learning techniques have been successfully applied to super-resolution tasks on natural images where visually pleasing results are sufficient. However in many scientific do…
Fixed-Confidence Guarantees for Bayesian Best-Arm Identification
Xuedong Shang, Rianne de Heide, Emilie Kaufmann +2
We investigate and provide new insights on the sampling rule called Top-Two Thompson Sampling (TTTS). In particular, we justify its use for fixed-confidence best-arm identification…
Approximate Dynamic Programming with Neural Networks in Linear Discrete Action Spaces
Wouter van Heeswijk, Han La Poutré
Real-world problems of operations research are typically high-dimensional and combinatorial. Linear programs are generally used to formulate and efficiently solve these large decis…
Lipschitz Adaptivity with Multiple Learning Rates in Online Learning
Zakaria Mhammedi, Wouter M. Koolen, Tim van Erven
We aim to design adaptive online learning algorithms that take advantage of any special structure that might be present in the learning task at hand, with as little manual tuning b…
Robust Temporal Difference Learning for Critical Domains
Richard Klima, Daan Bloembergen, Michael Kaisers +1
We present a new Q-function operator for temporal difference (TD) learning methods that explicitly encodes robustness against significant rare events (SRE) in critical domains. The…