65 citations · 115 across the 19 of their papers we have counts for
6 papers · 1 filter
Second-Order Uncertainty Quantification: A Distance-Based Approach
Yusuf Sale, Viktor Bengs, Michele Caprio +1
In the past couple of years, various approaches to representing and quantifying different types of predictive uncertainty in machine learning, notably in the setting of classificat…
A Survey of Reinforcement Learning from Human Feedback
Timo Kaufmann, Paul Weng, Viktor Bengs +1
Reinforcement learning from human feedback (RLHF) is a variant of reinforcement learning (RL) that learns from human feedback instead of relying on an engineered reward function. B…
Identifying Copeland Winners in Dueling Bandits with Indifferences
Viktor Bengs, Björn Haddenhorst, Eyke Hüllermeier
We consider the task of identifying the Copeland winner(s) in a dueling bandits problem with ternary feedback. This is an underexplored but practically relevant variant of the conv…
Iterative Deepening Hyperband
Jasmin Brandt, Marcel Wever, Dimitrios Iliadis +2
Hyperparameter optimization (HPO) is concerned with the automated search for the most appropriate hyperparameter configuration (HPC) of a parameterized machine learning algorithm.…
Approximating the Shapley Value without Marginal Contributions
Patrick Kolpaczki, Viktor Bengs, Maximilian Muschalik +1
The Shapley value, which is arguably the most popular approach for assigning a meaningful contribution value to players in a cooperative game, has recently been used intensively in…
On Second-Order Scoring Rules for Epistemic Uncertainty Quantification
Viktor Bengs, Eyke Hüllermeier, Willem Waegeman
It is well known that accurate probabilistic predictors can be trained through empirical risk minimisation with proper scoring rules as loss functions. While such learners capture…