1 citations · 2 across the 8 of their papers we have counts for
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DisCoMBO: Steering Expert-in-the-Loop Black-Box Optimization via Distributional Conformance
Jonas Seng, Bennet Wittelsbach, Kristian Kersting
Sequential Model-Based Optimization (SMBO) traditionally relies on Bayesian or ensembling surrogates for uncertainty quantification. While historically treated as fully data-driven…
Hyperparameter Optimization via Interacting with Probabilistic Circuits
Jonas Seng, Fabrizio Ventola, Zhongjie Yu +1
Despite the growing interest in designing truly interactive hyperparameter optimization (HPO) methods, to date, only a few allow to include human feedback. Existing interactive Bay…
Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks
Nick Kocher, Christian Wassermann, Leona Hennig +5
Neural Architecture Search (NAS) accelerates progress in deep learning through systematic refinement of model architectures. The downside is increasingly large energy consumption d…
Scaling Probabilistic Circuits via Data Partitioning
Jonas Seng, Florian Peter Busch, Pooja Prasad +3
Probabilistic circuits (PCs) enable us to learn joint distributions over a set of random variables and to perform various probabilistic queries in a tractable fashion. Though the t…
Systems with Switching Causal Relations: A Meta-Causal Perspective
Moritz Willig, Tim Nelson Tobiasch, Florian Peter Busch +3
Most work on causality in machine learning assumes that causal relationships are driven by a constant underlying process. However, the flexibility of agents' actions or tipping poi…
Tearing Apart NOTEARS: Controlling the Graph Prediction via Variance Manipulation
Jonas Seng, Matej Zečević, Devendra Singh Dhami +1
Simulations are ubiquitous in machine learning. Especially in graph learning, simulations of Directed Acyclic Graphs (DAG) are being deployed for evaluating new algorithms. In the…
FEATHERS: Federated Architecture and Hyperparameter Search
Jonas Seng, Pooja Prasad, Martin Mundt +2
Deep neural architectures have profound impact on achieved performance in many of today's AI tasks, yet, their design still heavily relies on human prior knowledge and experience.…