65 citations · 115 across the 19 of their papers we have counts for
21 papers
Bayesian Optimization of CVaR with Unknown Hyperparameters
Valentin Margraf, Jonas Hanselle, Viktor Bengs +3
We study Bayesian optimization of the Conditional Value-at-Risk (CVaR) of an expensive black-box function that depends on a decision and an environmental variable. Existing no-regr…
Challenges and Solutions for Bandits in the Wild: Warm-Started Mixture Bandits for Cross-Cohort Slate Recommendation
Serafima Lebedeva, Sumantrak Mukherjee, Ali Arshad Sadal +8
Many recommender services repeatedly encounter cold-start cohorts, where new users arrive with little or no interaction history. This creates two challenges: learning user preferen…
Calibrated Preference Learning: The Case of Label Ranking
Santo M. A. R. Thies, Viktor Bengs, Timo Kaufmann +2
Calibration, the alignment of predicted probabilities with true outcome frequencies, is essential for reliable decision-making. While extensively studied for classification and reg…
Linear-LLM-SCM: Benchmarking LLMs for Coefficient Elicitation in Linear-Gaussian Causal Models
Kanta Yamaoka, Sumantrak Mukherjee, Thomas Gärtner +5
Large language models (LLMs) have shown potential in identifying qualitative causal relations, but their ability to perform quantitative causal reasoning---estimating effect sizes…
Co-Exploration and Co-Exploitation via Shared Structure in Multi-Task Bandits
Sumantrak Mukherjee, Serafima Lebedeva, Valentin Margraf +6
We propose a novel Bayesian framework for efficient exploration in contextual multi-task multi-armed bandit settings, where the context is only observed partially and dependencies…
A calibration test for evaluating set-based epistemic uncertainty representations
Mira Jürgens, Thomas Mortier, Eyke Hüllermeier +2
The accurate representation of epistemic uncertainty is a challenging yet essential task in machine learning. A widely used representation corresponds to convex sets of probabilist…
Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods?
Mira Jürgens, Nis Meinert, Viktor Bengs +2
Trustworthy ML systems should not only return accurate predictions, but also a reliable representation of their uncertainty. Bayesian methods are commonly used to quantify both ale…
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…
AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration
Jasmin Brandt, Elias Schede, Viktor Bengs +3
We study the algorithm configuration (AC) problem, in which one seeks to find an optimal parameter configuration of a given target algorithm in an automated way. Recently, there ha…
On the Calibration of Probabilistic Classifier Sets
Thomas Mortier, Viktor Bengs, Eyke Hüllermeier +2
Multi-class classification methods that produce sets of probabilistic classifiers, such as ensemble learning methods, are able to model aleatoric and epistemic uncertainty. Aleator…
Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation
Viktor Bengs, Eyke Hüllermeier, Willem Waegeman
Uncertainty quantification has received increasing attention in machine learning in the recent past. In particular, a distinction between aleatoric and epistemic uncertainty has be…
Non-Stationary Dueling Bandits
Patrick Kolpaczki, Viktor Bengs, Eyke Hüllermeier
We study the non-stationary dueling bandits problem with arms, where the time horizon consists of stationary segments, each of which is associated with its own preferen…
Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models
Viktor Bengs, Aadirupa Saha, Eyke Hüllermeier
We consider the regret minimization task in a dueling bandits problem with context information. In every round of the sequential decision problem, the learner makes a context-depen…
Finding Optimal Arms in Non-stochastic Combinatorial Bandits with Semi-bandit Feedback and Finite Budget
Jasmin Brandt, Viktor Bengs, Björn Haddenhorst +1
We consider the combinatorial bandits problem with semi-bandit feedback under finite sampling budget constraints, in which the learner can carry out its action only for a limited n…
A Survey of Methods for Automated Algorithm Configuration
Elias Schede, Jasmin Brandt, Alexander Tornede +4
Algorithm configuration (AC) is concerned with the automated search of the most suitable parameter configuration of a parametrized algorithm. There is currently a wide variety of A…
Machine Learning for Online Algorithm Selection under Censored Feedback
Alexander Tornede, Viktor Bengs, Eyke Hüllermeier
In online algorithm selection (OAS), instances of an algorithmic problem class are presented to an agent one after another, and the agent has to quickly select a presumably best al…