4 citations · 8 across the 26 of their papers we have counts for
8 papers · 1 filter
Self-Reinforcing Controllable Synthesis of Rare Relational Data via Bayesian Calibration
Chongsheng Zhang, Hao Wang, Zelong Yu +7
Imbalanced data are commonly present in real-world applications. While data synthesis can effectively mitigate data scarcity for rare classes, and LLMs have revolutionized text gen…
Incentive Aware AI Regulations: A Credal Characterisation
Anurag Singh, Julian Rodemann, Rajeev Verma +2
The rapid proliferation of AI applications has intensified debate on effective regulation of these black-box services. Effective regulation must balance two competing goals: (1) de…
Beyond Arrow: From Impossibility to Possibilities in Multi-Criteria Benchmarking
Polina Gordienko, Christoph Jansen, Julian Rodemann +1
Modern benchmarks such as HELM MMLU account for multiple metrics like accuracy, robustness and efficiency. When trying to turn these metrics into a single ranking, natural aggregat…
Generalization Bounds and Stopping Rules for Learning with Self-Selected Data
Julian Rodemann, James Bailie
Many learning paradigms self-select training data in light of previously learned parameters. Examples include active learning, semi-supervised learning, bandits, or boosting. Rodem…
How to Choose a Reinforcement-Learning Algorithm
Fabian Bongratz, Vladimir Golkov, Lukas Mautner +5
The field of reinforcement learning offers a large variety of concepts and methods to tackle sequential decision-making problems. This variety has become so large that choosing an…
Explaining Bayesian Optimization by Shapley Values Facilitates Human-AI Collaboration
Julian Rodemann, Federico Croppi, Philipp Arens +7
Bayesian optimization (BO) with Gaussian processes (GP) has become an indispensable algorithm for black box optimization problems. Not without a dash of irony, BO is often consider…