Viktor Bengs
18 papers · 2020–2024 · 7 venues · across top CS/AI venues
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🐣 Hot Topic Early Bird 🗺️ Taxonomy Completionist (28) 🌍 Conference Polyglot · Silver 🌉 Interdisciplinary Bridge · Silver 🤝 Dynamic Duo (18)
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(18)
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Research topics
Machine Learning
(17)
Optimization & Theory
(12)
Learning Types
(9)
Artificial Intelligence
(7)
Mathematics & Optimization
(7)
Optimization
(7)
Core AI
(6)
Core Methods
(3)
Computer Science
(2)
Bayesian & Probabilistic · Machine Learning
(2)
Foundations
(2)
Reinforcement Learning
(1)
Application Areas
(1)
Applications
(1)
Bayesian & Probabilistic · Artificial Intelligence
(1)
Keywords
multi-armed bandit
(7)
dueling bandit
(5)
epistemic uncertainty
(4)
uncertainty quantification
(3)
preference learning
(3)
hyperparameter optimization
(3)
sample complexity
(3)
loss minimisation
(2)
combinatorial optimization
(2)
online learning
(2)
algorithm configuration
(2)
parameter tuning
(2)
plackett-luce model
(2)
aleatoric uncertainty
(2)
second-order learner
(2)
regret minimization
(1)
feature attribution
(1)
ensemble learning
(1)
probabilistic modeling
(1)
learning theory
(1)
Papers
Approximating the Shapley Value without Marginal Contributions
AAAI 2024
Best Arm Identification with Retroactively Increased Sampling Budget for More Resource-Efficient HPO
IJCAI 2024
Identifying Copeland Winners in Dueling Bandits with Indifferences
AISTATS 2024
Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods?
ICML 2024
Second-Order Uncertainty Quantification: A Distance-Based Approach
ICML 2024
AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration
AAAI 2023
A Survey of Methods for Automated Algorithm Configuration (Extended Abstract)
IJCAI 2023
On Second-Order Scoring Rules for Epistemic Uncertainty Quantification
ICML 2023
On the Calibration of Probabilistic Classifier Sets
AISTATS 2023
Finding Optimal Arms in Non-stochastic Combinatorial Bandits with Semi-bandit Feedback and Finite Budget
NeurIPS 2022
Machine Learning for Online Algorithm Selection under Censored Feedback
AAAI 2022
Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation
NeurIPS 2022
Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models
ICML 2022
Identification of the Generalized Condorcet Winner in Multi-dueling Bandits
NeurIPS 2021
Preference-based Online Learning with Dueling Bandits: A Survey
JMLR 2021
Single Player Monte-Carlo Tree Search Based on the Plackett-Luce Model
AAAI 2021
Testification of Condorcet Winners in dueling bandits
UAI 2021
Preselection Bandits
ICML 2020