14 citations · 27 across the 10 of their papers we have counts for
7 papers · 1 filter
How to Enable Uncertainty Estimation in Proximal Policy Optimization
Eugene Bykovets, Yannick Metz, Mennatallah El-Assady +2
While deep reinforcement learning (RL) agents have showcased strong results across many domains, a major concern is their inherent opaqueness and the safety of such systems in real…
Statistical and computational thresholds for the planted -densest sub-hypergraph problem
Luca Corinzia, Paolo Penna, Wojciech Szpankowski +1
In this work, we consider the problem of recovery a planted -densest sub-hypergraph on -uniform hypergraphs. This fundamental problem appears in different contexts, e.g., com…
Continuous Submodular Function Maximization
Yatao Bian, Joachim M. Buhmann, Andreas Krause
Continuous submodular functions are a category of generally non-convex/non-concave functions with a wide spectrum of applications. The celebrated property of this class of function…
From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models
Aytunc Sahin, Yatao Bian, Joachim M. Buhmann +1
Submodular functions have been studied extensively in machine learning and data mining. In particular, the optimization of submodular functions over the integer lattice (integer su…
Variational Federated Multi-Task Learning
Luca Corinzia, Ami Beuret, Joachim M. Buhmann
In federated learning, a central server coordinates the training of a single model on a massively distributed network of devices. This setting can be naturally extended to a multi-…
Learning Counterfactual Representations for Estimating Individual Dose-Response Curves
Patrick Schwab, Lorenz Linhardt, Stefan Bauer +2
Estimating what would be an individual's potential response to varying levels of exposure to a treatment is of high practical relevance for several important fields, such as health…