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