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 30 papers at ICML 2024:
Improving Neural Additive Models with Bayesian Principles.
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Transformers Implement Functional Gradient Descent to Learn Non-Linear Functions In Context.
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Provably Better Explanations with Optimized Aggregation of Feature Attributions.
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Position: Insights from Survey Methodology can Improve Training Data.
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Uncertainty for Active Learning on Graphs.
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KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions.
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Simplicity Bias via Global Convergence of Sharpness Minimization.
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Can Looped Transformers Learn to Implement Multi-step Gradient Descent for In-context Learning?.
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Position: Why We Must Rethink Empirical Research in Machine Learning.
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Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods?.
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Beyond the Calibration Point: Mechanism Comparison in Differential Privacy.
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Position: Embracing Negative Results in Machine Learning.
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Position: A Call to Action for a Human-Centered AutoML Paradigm.
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Robustness of Deep Learning for Accelerated MRI: Benefits of Diverse Training Data.
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Position: Future Directions in the Theory of Graph Machine Learning.
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Position: Bayesian Deep Learning in the Age of Large-Scale AI.
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Generalizing orthogonalization for models with non-linearities.
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Second-Order Uncertainty Quantification: A Distance-Based Approach.
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Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environments.
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Connecting the Dots: Is Mode Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks?.
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Sample Complexity Bounds for Estimating Probability Divergences under Invariances.
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A Universal Class of Sharpness-Aware Minimization Algorithms.
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Causal Effect Identification in LiNGAM Models with Latent Confounders.
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Expressivity and Generalization: Fragment-Biases for Molecular GNNs.
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20.07.2024
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