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Papers
Harmonizing Generalization and Personalization in Federated Prompt Learning
ICML 2024
Harnessing Hierarchical Label Distribution Variations in Test Agnostic Long-tail Recognition
ICML 2024
How Do Nonlinear Transformers Learn and Generalize in In-Context Learning?
ICML 2024
How Graph Neural Networks Learn: Lessons from Training Dynamics
ICML 2024
How Uniform Random Weights Induce Non-uniform Bias: Typical Interpolating Neural Networks Generalize with Narrow Teachers
ICML 2024
Improving Neural Logic Machines via Failure Reflection
ICML 2024
In-Context Reinforcement Learning for Variable Action Spaces
ICML 2024
Investigating Pre-Training Objectives for Generalization in Vision-Based Reinforcement Learning
ICML 2024
INViT: A Generalizable Routing Problem Solver with Invariant Nested View Transformer
ICML 2024
Is Mamba Capable of In-Context Learning?
AutoML 2024
LCA-on-the-Line: Benchmarking Out of Distribution Generalization with Class Taxonomies
ICML 2024
Learning to Intervene on Concept Bottlenecks
ICML 2024
Learning to Scale Logits for Temperature-Conditional GFlowNets
ICML 2024
LEVI: Generalizable Fine-tuning via Layer-wise Ensemble of Different Views
ICML 2024
ManiWAV: Learning Robot Manipulation from In-the-Wild Audio-Visual Data
CoRL 2024
MMPareto: Boosting Multimodal Learning with Innocent Unimodal Assistance
ICML 2024
Neural Tangent Kernels Motivate Cross-Covariance Graphs in Neural Networks
ICML 2024
Non-rigid Relative Placement through 3D Dense Diffusion
CoRL 2024
On Statistical Learning Theory for Distributional Inputs
ICML 2024
PAPM: A Physics-aware Proxy Model for Process Systems
ICML 2024
Perfect Alignment May be Poisonous to Graph Contrastive Learning
ICML 2024
Position: Compositional Generative Modeling: A Single Model is Not All You Need
ICML 2024
Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining
ICML 2024
Position: Future Directions in the Theory of Graph Machine Learning
ICML 2024
Prediction-powered Generalization of Causal Inferences
ICML 2024