19.07.2024
24 Accepted Papers (17 Main, and 7 Workshops)
41st International Conference on Machine Learning, Vienna, Austria, Jul 21-27, 2024
We are happy to announce that MCML researchers have contributed a total of 24 papers to ICML 2024: 17 Main, and 7 Workshop papers. Congrats to our researchers!
Main Track (17 papers)
K. Bouchiat • A. Immer • H. Yèche • G. Ratsch • V. Fortuin
Improving Neural Additive Models with Bayesian Principles.
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
Improving Neural Additive Models with Bayesian Principles.
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
M. Bini • K. Roth • Z. Akata • A. Khoreva
ETHER: Efficient Finetuning of Large-Scale Models with Hyperplane Reflections.
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL GitHub
ETHER: Efficient Finetuning of Large-Scale Models with Hyperplane Reflections.
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL GitHub
T. Decker • A. R. Bhattarai • J. Gu • V. Tresp • F. Buettner
Provably Better Explanations with Optimized Aggregation of Feature Attributions.
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
Provably Better Explanations with Optimized Aggregation of Feature Attributions.
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
S. Eckman • B. Plank • F. Kreuter
Position: Insights from Survey Methodology can Improve Training Data.
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
Position: Insights from Survey Methodology can Improve Training Data.
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
D. Frauen • V. Melnychuk • S. Feuerriegel
Fair Off-Policy Learning from Observational Data.
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
Fair Off-Policy Learning from Observational Data.
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
F. Fumagalli • M. Muschalik • P. Kolpaczki • E. Hüllermeier • B. Hammer
KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions.
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions.
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
M. Herrmann • F. J. D. Lange • K. Eggensperger • G. Casalicchio • M. Wever • M. Feurer • D. Rügamer • E. Hüllermeier • A.-L. Boulesteix • B. Bischl
Position: Why We Must Rethink Empirical Research in Machine Learning.
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
Position: Why We Must Rethink Empirical Research in Machine Learning.
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
G. Kaissis • S. Kolek • B. Balle • J. Hayes • D. Rückert
Beyond the Calibration Point: Mechanism Comparison in Differential Privacy.
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
Beyond the Calibration Point: Mechanism Comparison in Differential Privacy.
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
F. Karl • M. Kemeter • G. Dax • P. Sierak
Position: Embracing Negative Results in Machine Learning.
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
Position: Embracing Negative Results in Machine Learning.
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
M. Lindauer • F. Karl • A. Klier • J. Moosbauer • A. Tornede • A. C. Mueller • F. Hutter • M. Feurer • B. Bischl
Position: A Call to Action for a Human-Centered AutoML Paradigm.
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
Position: A Call to Action for a Human-Centered AutoML Paradigm.
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
T. Papamarkou • M. Skoularidou • K. Palla • L. Aitchison • J. Arbel • D. Dunson • M. Filippone • V. Fortuin • P. Hennig • J. M. Hernández-Lobato • A. Hubin • A. Immer • T. Karaletsos • M. E. Khan • A. Kristiadi • Y. Li • S. Mandt • C. Nemeth • M. A. Osborne • T. G. J. Rudner • D. Rügamer • Y. W. Teh • M. Welling • A. G. Wilson • R. Zhang
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI.
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI.
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
D. Rügamer • C. Kolb • T. Weber • L. Kook • T. Nagler
Generalizing orthogonalization for models with non-linearities.
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
Generalizing orthogonalization for models with non-linearities.
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
Y. Sale • V. Bengs • M. Caprio • E. Hüllermeier
Second-Order Uncertainty Quantification: A Distance-Based Approach.
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
Second-Order Uncertainty Quantification: A Distance-Based Approach.
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
Y. Shen • N. Daheim • B. Cong • P. Nickl • G. M. Marconi • C. Bazan • R. Yokota • I. Gurevych • D. Cremers • M. E. Khan • T. Möllenhoff
Variational Learning is Effective for Large Deep Networks.
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL GitHub
Variational Learning is Effective for Large Deep Networks.
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL GitHub
J. Schweisthal • D. Frauen • M. Van der Schaar • S. Feuerriegel
Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environments.
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environments.
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
E. Sommer • L. Wimmer • T. Papamarkou • L. Bothmann • B. Bischl • D. Rügamer
Connecting the Dots: Is Mode Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks?
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
Connecting the Dots: Is Mode Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks?
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
D. Tramontano • Y. Kivva • S. Salehkaleybar • M. Drton • N. Kiyavash
Causal Effect Identification in LiNGAM Models with Latent Confounders.
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
Causal Effect Identification in LiNGAM Models with Latent Confounders.
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
Workshops (7 papers)
M. Dani • M. J. Prakash • Z. Akata • S. Liebe
SemioLLM: Assessing Large Language Models for Semiological Analysis in Epilepsy Research.
AI4Science @ICML 2024 - AI for Science Workshop at the 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
SemioLLM: Assessing Large Language Models for Semiological Analysis in Epilepsy Research.
AI4Science @ICML 2024 - AI for Science Workshop at the 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
P. Foth • L. Gosch • S. Geisler • L. Schwinn • S. Günnemann
Relaxing Graph Transformers for Adversarial Attacks.
Differentiable Almost Everything @ICML 2024 - Workshop Differentiable Almost Everything: Differentiable Relaxations, Algorithms, Operators, and Simulators at the 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. PDF
Relaxing Graph Transformers for Adversarial Attacks.
Differentiable Almost Everything @ICML 2024 - Workshop Differentiable Almost Everything: Differentiable Relaxations, Algorithms, Operators, and Simulators at the 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. PDF
X. Feng • Z. Jiang • T. Kaufmann • E. Hüllermeier • P. Weng • Y. Zhu
Comparing Comparisons: Informative and Easy Human Feedback with Distinguishability Queries.
MHFAIA @ICML 2024 - Workshop on Models of Human Feedback for AI Alignment at the 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
Comparing Comparisons: Informative and Easy Human Feedback with Distinguishability Queries.
MHFAIA @ICML 2024 - Workshop on Models of Human Feedback for AI Alignment at the 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
U. Fischer Abaigar • C. Kern • F. Kreuter
The Missing Link: Allocation Performance in Causal Machine Learning.
Workshop Humans, Algorithmic Decision-Making and Society @ICML 2024 - Workshop Humans, Algorithmic Decision-Making and Society: Modeling Interactions and Impact at the 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. arXiv URL
The Missing Link: Allocation Performance in Causal Machine Learning.
Workshop Humans, Algorithmic Decision-Making and Society @ICML 2024 - Workshop Humans, Algorithmic Decision-Making and Society: Modeling Interactions and Impact at the 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. arXiv URL
P. Hofman • Y. Sale • E. Hüllermeier
Quantifying Aleatoric and Epistemic Uncertainty: A Credal Approach.
SPIGM @ICML 2024 - Workshop on Structured Probabilistic Inference & Generative Modeling at the 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
Quantifying Aleatoric and Epistemic Uncertainty: A Credal Approach.
SPIGM @ICML 2024 - Workshop on Structured Probabilistic Inference & Generative Modeling at the 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
Y. Sun • J. Liu • Z. Wu • Z. Ding • Y. Ma • T. Seidl • V. Tresp
SA-DQAS: Self-attention Enhanced Differentiable Quantum Architecture Search.
Differentiable Almost Everything @ICML 2024 - Workshop Differentiable Almost Everything: Differentiable Relaxations, Algorithms, Operators, and Simulators at the 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. PDF
SA-DQAS: Self-attention Enhanced Differentiable Quantum Architecture Search.
Differentiable Almost Everything @ICML 2024 - Workshop Differentiable Almost Everything: Differentiable Relaxations, Algorithms, Operators, and Simulators at the 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. PDF
T. Uscidda • L. Eyring • K. Roth • F. J. Theis • Z. Akata • M. Cuturi
Disentangled Representation Learning through Geometry Preservation with the Gromov-Monge Gap.
SPIGM @ICML 2024 - Workshop on Structured Probabilistic Inference & Generative Modeling at the 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. arXiv
Disentangled Representation Learning through Geometry Preservation with the Gromov-Monge Gap.
SPIGM @ICML 2024 - Workshop on Structured Probabilistic Inference & Generative Modeling at the 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. arXiv
Related
18.12.2025
"See, Don’t Assume": Revealing and Reducing Gender Bias in AI
ICLR 2025 research led by Zeynep Akata’s team reveals and reduces gender bias in popular vision-language AI models.
16.12.2025
Fabian Theis Featured in Handelsblatt on the Future of AI in Precision Medicine
MCML PI Fabian Theis discusses AI-driven precision medicine and its growing impact on individualized healthcare and biomedical research.
16.12.2025
Gitta Kutyniok Featured in VDI Nachrichten on AI Ethics
Gitta Kutyniok discusses measurable criteria for ethical AI, promoting safe and responsible autonomous decision-making.
16.12.2025
Hinrich Schütze Featured in WirtschaftsWoche on Innovative AI Approaches
Hinrich Schütze discusses Giotto.ai’s efficient AI models, highlighting memory separation and context-aware decoding to improve robustness.
