989 citations · 4.1k across the 80 of their papers we have counts for
12 papers · 1 filter
Information-Directed Exploration for Deep Reinforcement Learning
Nikolay Nikolov, Johannes Kirschner, Felix Berkenkamp +1
Efficient exploration remains a major challenge for reinforcement learning. One reason is that the variability of the returns often depends on the current state and action, and is…
A domain agnostic measure for monitoring and evaluating GANs
Paulina Grnarova, Kfir Y Levy, Aurelien Lucchi +4
Generative Adversarial Networks (GANs) have shown remarkable results in modeling complex distributions, but their evaluation remains an unsettled issue. Evaluations are essential f…
The Lyapunov Neural Network: Adaptive Stability Certification for Safe Learning of Dynamical Systems
Spencer M. Richards, Felix Berkenkamp, Andreas Krause
Learning algorithms have shown considerable prowess in simulation by allowing robots to adapt to uncertain environments and improve their performance. However, such algorithms are…
Discrete Sampling using Semigradient-based Product Mixtures
Alkis Gotovos, Hamed Hassani, Andreas Krause +1
We consider the problem of inference in discrete probabilistic models, that is, distributions over subsets of a finite ground set. These encompass a range of well-known models in m…
Teaching Multiple Concepts to a Forgetful Learner
Anette Hunziker, Yuxin Chen, Oisin Mac Aodha +5
How can we help a forgetful learner learn multiple concepts within a limited time frame? While there have been extensive studies in designing optimal schedules for teaching a singl…
Optimal DR-Submodular Maximization and Applications to Provable Mean Field Inference
An Bian, Joachim M. Buhmann, Andreas Krause
Mean field inference in probabilistic models is generally a highly nonconvex problem. Existing optimization methods, e.g., coordinate ascent algorithms, can only generate local opt…