900 citations
- GSI Helmholtz Centre for Heavy Ion ResearchDE399 papers
- Centre National de la Recherche ScientifiqueFR156 papers
- Max Planck Institute for Nuclear PhysicsDE125 papers
- Michigan State UniversityUS124 papers
- Heidelberg UniversityDE110 papers
- Université Paris-SaclayFR82 papers
- Goethe University FrankfurtDE80 papers
- Commissariat à l'Énergie Atomique et aux Énergies AlternativesFR77 papers
- Lawrence Berkeley National LaboratoryUS77 papers
- Argonne National LaboratoryUS73 papers
- Chinese Academy of SciencesCN70 papers
- Johannes Gutenberg University MainzDE69 papers
10 papers · 2 filters
PAC-Bayes Bounds for Bandit Problems: A Survey and Experimental Comparison
Hamish Flynn, David Reeb, Melih Kandemir +1
PAC-Bayes has recently re-emerged as an effective theory with which one can derive principled learning algorithms with tight performance guarantees. However, applications of PAC-Ba…
Does CLIP Know My Face?
Dominik Hintersdorf, Lukas Struppek, Manuel Brack +3
With the rise of deep learning in various applications, privacy concerns around the protection of training data have become a critical area of research. Whereas prior studies have…
NeurIPS'22 Cross-Domain MetaDL competition: Design and baseline results
Dustin Carrión-Ojeda, Hong Chen, Adrian El Baz +6
We present the design and baseline results for a new challenge in the ChaLearn meta-learning series, accepted at NeurIPS'22, focusing on "cross-domain" meta-learning. Meta-learning…
Combining Predictions under Uncertainty: The Case of Random Decision Trees
Florian Busch, Moritz Kulessa, Eneldo Loza Mencía +1
A common approach to aggregate classification estimates in an ensemble of decision trees is to either use voting or to average the probabilities for each class. The latter takes un…
PAC-Bayesian Lifelong Learning For Multi-Armed Bandits
Hamish Flynn, David Reeb, Melih Kandemir +1
We present a PAC-Bayesian analysis of lifelong learning. In the lifelong learning problem, a sequence of learning tasks is observed one-at-a-time, and the goal is to transfer infor…
Integrating Contrastive Learning with Dynamic Models for Reinforcement Learning from Images
Bang You, Oleg Arenz, Youping Chen +1
Recent methods for reinforcement learning from images use auxiliary tasks to learn image features that are used by the agent's policy or Q-function. In particular, methods based on…