hyperparameter selection

Towards Automated Self-Supervised Learning for Truly Unsupervised Graph Anomaly Detection (Abstract Reprint) AAAI 2026 Adaptive Learn-then-Test: Statistically Valid and Efficient Hyperparameter Selection ICML 2025 A Theoretical Framework for Preventing Class Collapse in Supervised Contrastive Learning AISTATS 2025 BanditSpec: Adaptive Speculative Decoding via Bandit Algorithms ICML 2025 Call for Rigor in Reporting Quality of Instruction Tuning Data ACL 2025 How Distributed Collaboration Influences the Diffusion Model Training? A Theoretical Perspective ICML 2025 The Polynomial Stein Discrepancy for Assessing Moment Convergence ICML 2025 PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization ICLR 2024 Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures NeurIPS 2023 On Pitfalls of Test-Time Adaptation ICML 2023 Prior Specification for Bayesian Matrix Factorization via Prior Predictive Matching JMLR 2023 RankMe: Assessing the Downstream Performance of Pretrained Self-Supervised Representations by Their Rank ICML 2023 Adapting the Linearised Laplace Model Evidence for Modern Deep Learning ICML 2022 Invariance Learning based on Label Hierarchy NeurIPS 2022 Differentially Private Regression and Classification with Sparse Gaussian Processes JMLR 2021 Hyperparameter Selection for Imitation Learning ICML 2021 Continual Learning With Extended Kronecker-Factored Approximate Curvature CVPR 2020 Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models L4DC 2020 Gaussian Process Random Fields NeurIPS 2015 Particle Swarm Model Selection JMLR 2009 Model Selection in Kernel Based Regression using the Influence Function JMLR 2008 Tighter PAC-Bayes Bounds NeurIPS 2006