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ICML2022放榜了,很久没写博客了,今天摸鱼把ICML2022的接收列表翻了一遍,挑了一些自己感兴趣的下次读, 先放论文列表,有兴趣的自取。
Oral
- Head2Toe: Utilizing Intermediate Representations for Better Transfer Learning
- Bounding Training Data Reconstruction in Private (Deep) Learning
- Measuring Representational Robustness of Neural Networks Through Shared Invariances
- ModLaNets: Learning Generalisable Dynamics via Modularity and Physical Inductive Bias
- Hierarchical Shrinkage: Improving the accuracy and interpretability of tree-based models
Spotlights
- Does the Data Induce Capacity Control in Deep Learning?
- Accelerating Shapley Explanation via Contributive Cooperator Selection
- Prototype Based Classification from Hierarchy to Fairness
- Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments
- Fair Representation Learning through Implicit Path Alignment
- Feature selection using e-values
- Mitigating Neural Network Overconfidence with Logit Normalization
- Active Multi-Task Representation Learning
- Dataset Condensation with Contrastive Signals
- Extracting Latent State Representations with Linear Dynamics from Rich Observations
- How to Fill the Optimal Set? Population Gradient Descent with Harmless Diversity
- Fair and Fast k-Center Clustering for Data Summarization
- Channel Importance Matters in Few-Shot Image Classification
- Label-Free Explainability for Unsupervised Models
- A psychological theory of explainability
- From data to functa: Your data point is a function and you should treat it like one
- Understanding Robust Overfitting of Adversarial Training and Beyond
- Learning Stable Classifiers by Transferring Unstable Features
- Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings
- XAI for Transformers: Better Explanations through Conservative Propagation
- Role-based Multiplex Network Embedding
- Meaningfully debugging model mistakes using conceptual counterfactual explanations
- Forgetting-free Continual Learning with Winning Subnetworks
- Wide Neural Networks Forget Less Catastrophically
- Measuring dissimilarity with diffeomorphism invariance
- Efficient Learning of CNNs using Patch Based Features
- Multi-scale Feature Learning Dynamics: Insights for Double Descent
Accepted papers
- Achieving Fairness at No Utility Cost via Data Reweighing
- Confidence Score for Source-Free Unsupervised Domain Adaptation
- Probabilistic Bilevel Coreset Selection
- Transfer and Marginalize: Explaining Away Label Noise with Privileged Information
- On the Effects of Artificial Data Modification
- Provable Domain Generalization via Invariant-Feature Subspace Recovery
- More Than a Toy: Random Matrix Models Predict How Real-World Neural Representations Generalize
- Information-Intensive Dataset Condensation
- Datamodels: Understanding Predictions with Data and Data with Predictions
- Benefits of Deep and Wide Convolutional Residual Networks: Function Approximation under Smoothness Constraint
- What Can Linear Interpolation of Neural Network Loss Landscapes Tell Us?
- Understanding Instance-Level Impact of Fairness Constraints
- Disentangling Disease-related Representation from Obscure for Disease Prediction
- A new similarity measure for covariate shift with applications to nonparametric regression
- Representation Topology Divergence: A Method for Comparing Neural Network Representations
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