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Learning Theory

5312 directly classified papers

Papers per year

Papers

PAC Learning and Stabilizing Hedonic Games: Towards a Unifying Approach. AAAI 2023
Constraint Optimization over Semirings AAAI 2023
Tree Learning: Optimal Sample Complexity and Algorithms AAAI 2023
Rapid Adaptation in Online Continual Learning: Are We Evaluating It Right? ICCV 2023
Discovering Language Model Behaviors with Model-Written Evaluations ACL 2023
Learning good interventions in causal graphs via covering UAI 2023
Mnemonist: Locating Model Parameters that Memorize Training Examples UAI 2023
Spatial-frequency channels, shape bias, and adversarial robustness NIPS 2023
Why do networks have inhibitory/negative connections? ICCV 2023
A Parameterized Theory of PAC Learning AAAI 2023
Double Doubly Robust Thompson Sampling for Generalized Linear Contextual Bandits AAAI 2023
Gradient-based Wang-Landau Algorithm: A Novel Sampler for Output Distribution of Neural Networks over the Input Space ICML 2023
Will Large-scale Generative Models Corrupt Future Datasets? ICCV 2023
Exploring Model Transferability through the Lens of Potential Energy ICCV 2023
What do neural networks learn in image classification? A frequency shortcut perspective ICCV 2023
Environment-Invariant Curriculum Relation Learning for Fine-Grained Scene Graph Generation ICCV 2023
Compression, Generalization and Learning JMLR 2023
The Art of BART: Minimax Optimality over Nonhomogeneous Smoothness in High Dimension JMLR 2023
Revisiting minimum description length complexity in overparameterized models JMLR 2023
Understanding Representation Learnability of Nonlinear Self-Supervised Learning AAAI 2023
On Discovering Interesting Combinatorial Integer Sequences IJCAI 2023
Understanding Hessian Alignment for Domain Generalization ICCV 2023
Contextual Reliability: When Different Features Matter in Different Contexts ICML 2023
The Importance of Human-Labeled Data in the Era of LLMs IJCAI 2023
Simplicity Bias in Transformers and their Ability to Learn Sparse Boolean Functions ACL 2023