David Rügamer
25 papers · 2022–2025 · 11 conferences · across top CS/AI conferences
Achievements
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🗺️ Taxonomy Completionist (18) 🧭 Keyword Pioneer 🌍 Conference Polyglot (11) 🐝 Cross-Pollinator (13) 🌈 Renaissance Researcher (6)
🌉
Interdisciplinary Bridge
👥
Mega-Team
(25)
⚡
Prolific Year
(9)
❓
The Questioner
(2)
💎
Century Club
(25)
Conferences
ICML (9)
ICLR (4)
AISTATS (2)
UAI (2)
WACV (2)
CONLL (1)
EMNLP (1)
ICCV (1)
NAACL (1)
NIPS (1)
SEMEVAL (1)
Top co-authors
Keywords
contrastive learning
(2)
sentence transformer
(2)
text classification
(2)
uncertainty quantification
(2)
data augmentation
(1)
machine-generated text detection
(1)
image reconstruction
(1)
handwriting recognition
(1)
functional data analysis
(1)
magnetic resonance imaging
(1)
cell tracking
(1)
multivariate time series
(1)
probabilistic model
(1)
additive model
(1)
downstream task
(1)
interpretable model
(1)
cross-entropy loss
(1)
pre-trained language model
(1)
similarity loss
(1)
mask optimization
(1)
Papers
Additive Model Boosting: New Insights and Path(ologie)s
AISTATS 2025
Paths and Ambient Spaces in Neural Loss Landscapes
AISTATS 2025
Hybrid Bernstein Normalizing Flows for Flexible Multivariate Density Regression with Interpretable Marginals
UAI 2025
Position: The Future of Bayesian Prediction Is Prior-Fitted
ICML 2025
Revisiting Unbiased Implicit Variational Inference
ICML 2025
How To Make Your Cell Tracker Say "I dunno!"
ICCV 2025
Adjustment for Confounding using Pre-Trained Representations
ICML 2025
Calibrating LLMs with Information-Theoretic Evidential Deep Learning
ICLR 2025
Microcanonical Langevin Ensembles: Advancing the Sampling of Bayesian Neural Networks
ICLR 2025
Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries
ICLR 2025
Can Transformers Learn Full Bayesian Inference in Context?
ICML 2025
Constrained Probabilistic Mask Learning for Task-Specific Undersampled MRI Reconstruction
WACV 2024
Position: Why We Must Rethink Empirical Research in Machine Learning
ICML 2024
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
ICML 2024
Generalizing Orthogonalization for Models with Non-Linearities
ICML 2024
Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks?
ICML 2024
Team MGTD4ADL at SemEval-2024 Task 8: Leveraging (Sentence) Transformer Models with Contrastive Learning for Identifying Machine-Generated Text
NAACL 2024
Team MGTD4ADL at SemEval-2024 Task 8: Leveraging (Sentence) Transformer Models with Contrastive Learning for Identifying Machine-Generated Text
SEMEVAL 2024
How Inverse Conditional Flows Can Serve as a Substitute for Distributional Regression
UAI 2024
A Functional Extension of Semi-Structured Networks
NIPS 2024
Baby’s CoThought: Leveraging Large Language Models for Enhanced Reasoning in Compact Models
CONLL 2023
A New PHO-rmula for Improved Performance of Semi-Structured Networks
ICML 2023
Approximate Bayesian Inference with Stein Functional Variational Gradient Descent
ICLR 2023
Baby’s CoThought: Leveraging Large Language Models for Enhanced Reasoning in Compact Models
EMNLP 2023
Joint Classification and Trajectory Regression of Online Handwriting Using a Multi-Task Learning Approach
WACV 2022