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Generalization Bounds
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이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Information-Theoretic Generalization Bounds of Replay-based Continual Learning
Tuning without Peeking: Provable Privacy and Generalization Bounds for LLM Post-Training
Tight Generalization Error Bounds for Stochastic Gradient Descent in Non-convex Learning
Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters
Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective
Neural Drift Estimation for Ergodic Diffusions: Non-parametric Analysis and Numerical Exploration
Epistemic Errors of Imperfect Multitask Learners When Distributions Shift
Learning Where to Learn: Training Distribution Selection for Provable OOD Performance
Operator Learning for Schrödinger Equation: Unitarity, Error Bounds, and Time Generalization
Generalization Through Growth: Hidden Dynamics Controls Depth Dependence
Fast Rate Bounds for Multi-Task and Meta-Learning with Different Sample Sizes
Generalization Bounds for Quantum Learning via Rényi Divergences
Graph Representational Learning: When Does More Expressivity Hurt Generalization?
Efficient End-to-End Learning for Decision-Making: A Meta-Optimization Approach
Generalization Bounds and Stopping Rules for Learning with Self-Selected Data
Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws
Generalization Analysis for Contrastive Representation Learning under Non-IID Settings
Generalization Guarantees for Multi-View Representation Learning and Application to Regularization via Gaussian Product Mixture Prior
Achieving Distributive Justice in Federated Learning via Uncertainty Quantification
Compute-Optimal LLMs Provably Generalize Better With Scale
Generalization Bounds in Hybrid Quantum-Classical Machine Learning Models
Architecture independent generalization bounds for overparametrized deep ReLU networks
Improving Equivariant Networks with Probabilistic Symmetry Breaking
Non-vacuous Generalization Bounds for Deep Neural Networks without any modification to the trained models
Generalizability of Neural Networks Minimizing Empirical Risk Based on Expressive Ability
Stability-based Generalization Analysis of Randomized Coordinate Descent for Pairwise Learning
Generalization Bounds for Equivariant Networks on Markov Data
Towards Auto-Regressive Next-Token Prediction: In-Context Learning Emerges from Generalization
Improved Margin Generalization Bounds for Voting Classifiers
Tight Generalization Bounds for Large-Margin Halfspaces
SeWA: Selective Weight Average via Probabilistic Masking
Scaling Law for Stochastic Gradient Descent in Quadratically Parameterized Linear Regression
Understanding the Generalization Error of Markov algorithms through Poissonization
Adversarial Transform Particle Filters
Data-Driven Neural Certificate Synthesis
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