20.07.2024
MCML at ICML 2024
The 41st International Conference on Machine Learning (ICML 2024). Vienna, Austria, July 21-27, 2024
We are happy to announce that MCML researchers are represented with 29 papers at ICML 2024:
K. Bouchiat, A. Immer, H. Yèche, G. Ratsch and V. Fortuin.
Improving Neural Additive Models with Bayesian Principles.
URL.
Improving Neural Additive Models with Bayesian Principles.
URL.
X. Cheng, Y. Chen and S. Sra.
Transformers Implement Functional Gradient Descent to Learn Non-Linear Functions In Context.
URL.
Transformers Implement Functional Gradient Descent to Learn Non-Linear Functions In Context.
URL.
T. Decker, A. R. Bhattarai, J. Gu, V. Tresp and F. Buettner.
Provably Better Explanations with Optimized Aggregation of Feature Attributions.
URL.
Provably Better Explanations with Optimized Aggregation of Feature Attributions.
URL.
S. Eckman, B. Plank and F. Kreuter.
Position: Insights from Survey Methodology can Improve Training Data.
URL.
Position: Insights from Survey Methodology can Improve Training Data.
URL.
U. Fischer Abaigar, C. Kern and F. Kreuter.
The Missing Link: Allocation Performance in Causal Machine Learning. (Workshop paper).
arXiv.
The Missing Link: Allocation Performance in Causal Machine Learning. (Workshop paper).
arXiv.
D. Fuchsgruber, T. Wollschläger, B. Charpentier, A. Oroz and S. Günnemann.
Uncertainty for Active Learning on Graphs.
URL.
Uncertainty for Active Learning on Graphs.
URL.
F. Fumagalli, M. Muschalik, P. Kolpaczki, E. Hüllermeier and B. Hammer.
KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions.
URL.
KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions.
URL.
K. Gatmiry, Z. Li, S. J. Reddi and S. Jegelka.
Simplicity Bias via Global Convergence of Sharpness Minimization.
URL.
Simplicity Bias via Global Convergence of Sharpness Minimization.
URL.
K. Gatmiry, N. Saunshi, S. J. Reddi, S. Jegelka and S. Kumar.
Can Looped Transformers Learn to Implement Multi-step Gradient Descent for In-context Learning?.
URL.
Can Looped Transformers Learn to Implement Multi-step Gradient Descent for In-context Learning?.
URL.
M. Herrmann, F. J. D. Lange, K. Eggensperger, G. Casalicchio, M. Wever, M. Feurer, D. Rügamer, E. Hüllermeier, A.-L. Boulesteix and B. Bischl.
Position: Why We Must Rethink Empirical Research in Machine Learning.
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Position: Why We Must Rethink Empirical Research in Machine Learning.
URL.
M. Juergens, N. Meinert, V. Bengs, E. Hüllermeier and W. Waegeman.
Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods?.
URL.
Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods?.
URL.
G. Kaissis, S. Kolek, B. Balle, J. Hayes and D. Rückert.
Beyond the Calibration Point: Mechanism Comparison in Differential Privacy.
URL.
Beyond the Calibration Point: Mechanism Comparison in Differential Privacy.
URL.
M. Lindauer, F. Karl, A. Klier, J. Moosbauer, A. Tornede, A. C. Mueller, F. Hutter, M. Feurer and B. Bischl.
Position: A Call to Action for a Human-Centered AutoML Paradigm.
URL.
Position: A Call to Action for a Human-Centered AutoML Paradigm.
URL.
K. Lin and R. Heckel.
Robustness of Deep Learning for Accelerated MRI: Benefits of Diverse Training Data.
URL.
Robustness of Deep Learning for Accelerated MRI: Benefits of Diverse Training Data.
URL.
C. Morris, F. Frasca, N. Dym, H. Maron, I. I. Ceylan, R. Levie, D. Lim, M. M. Bronstein, M. Grohe and S. Jegelka.
Position: Future Directions in the Theory of Graph Machine Learning.
URL.
Position: Future Directions in the Theory of Graph Machine Learning.
URL.
T. Papamarkou, M. Skoularidou, K. Palla, L. Aitchison, J. Arbel, D. Dunson, M. Filippone, V. Fortuin, P. Hennig, J. M. H. Lobato, A. Hubin, A. Immer, T. Karaletsos, M. E. Khan, A. Kristiadi, Y. , S. Mandt, C. Nemeth, M. A. Osborne, T. G. J. Rudner, D. Rügamer, Y. W. Teh, M. Welling, A. G. Wilson and R. Zhang.
Position: Bayesian Deep Learning in the Age of Large-Scale AI.
URL.
Position: Bayesian Deep Learning in the Age of Large-Scale AI.
URL.
D. Rügamer, C. Kolb, T. Weber, L. Kook and T. Nagler.
Generalizing orthogonalization for models with non-linearities.
URL.
Generalizing orthogonalization for models with non-linearities.
URL.
Y. Sale, V. Bengs, M. Caprio and E. Hüllermeier.
Second-Order Uncertainty Quantification: A Distance-Based Approach.
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Second-Order Uncertainty Quantification: A Distance-Based Approach.
URL.
J. Schweisthal, D. Frauen, M. van der Schaar and S. Feuerriegel.
Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environments.
URL.
Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environments.
URL.
E. Sommer, L. Wimmer, T. Papamarkou, L. Bothmann, B. Bischl and D. Rügamer.
Connecting the Dots: Is Mode Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks?.
URL.
Connecting the Dots: Is Mode Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks?.
URL.
Y. Sun, J. Liu, Z. Wu, Z. Ding, Y. Ma, T. Seidl and V. Tresp.
SA-DQAS: Self-attention Enhanced Differentiable Quantum Architecture Search. (Workshop paper).
arXiv.
SA-DQAS: Self-attention Enhanced Differentiable Quantum Architecture Search. (Workshop paper).
arXiv.
B. Tahmasebi and S. Jegelka.
Sample Complexity Bounds for Estimating Probability Divergences under Invariances.
URL.
Sample Complexity Bounds for Estimating Probability Divergences under Invariances.
URL.
B. Tahmasebi, A. Soleymani, D. Bahri, S. Jegelka and P. Jaillet.
A Universal Class of Sharpness-Aware Minimization Algorithms.
URL.
A Universal Class of Sharpness-Aware Minimization Algorithms.
URL.
D. Tramontano, Y. Kivva, S. Salehkaleybar, M. Drton and N. Kiyavash.
Causal Effect Identification in LiNGAM Models with Latent Confounders.
URL.
Causal Effect Identification in LiNGAM Models with Latent Confounders.
URL.
T. Wollschläger, N. Kemper, L. Hetzel, J. Sommer and S. Günnemann.
Expressivity and Generalization: Fragment-Biases for Molecular GNNs.
URL.
Expressivity and Generalization: Fragment-Biases for Molecular GNNs.
URL.
20.07.2024
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