"hyperparameter optimization" Papers
19 papers found
Conference
Accelerating neural network training: An analysis of the AlgoPerf competition
Priya Kasimbeg, Frank Schneider, Runa Eschenhagen et al.
Adaptive Kernel Design for Bayesian Optimization Is a Piece of CAKE with LLMs
Richard Suwandi, Feng Yin, Juntao Wang et al.
A Multi-Power Law for Loss Curve Prediction Across Learning Rate Schedules
Kairong Luo, Haodong Wen, Shengding Hu et al.
A Stochastic Approach to Bi-Level Optimization for Hyperparameter Optimization and Meta Learning
Minyoung Kim, Timothy Hospedales
Differentially Private Bilevel Optimization: Efficient Algorithms with Near-Optimal Rates
Andrew Lowy, Daogao Liu
FreSh: Frequency Shifting for Accelerated Neural Representation Learning
Adam Kania, Marko Mihajlovic, Sergey Prokudin et al.
Learn2Synth: Learning Optimal Data Synthesis Using Hypergradients for Brain Image Segmentation
Xiaoling Hu, Xiangrui Zeng, Oula Puonti et al.
(Mis)Fitting Scaling Laws: A Survey of Scaling Law Fitting Techniques in Deep Learning
Margaret Li, Sneha Kudugunta, Luke Zettlemoyer
qNBO: quasi-Newton Meets Bilevel Optimization
Sheng Fang, Yongjin Liu, Wei Yao et al.
Sequential Multi-Agent Dynamic Algorithm Configuration
Chen Lu, Ke Xue, Lei Yuan et al.
The Effectiveness of Curvature-Based Rewiring and the Role of Hyperparameters in GNNs Revisited
Floriano Tori, Vincent Holst, Vincent Ginis
ULTHO: Ultra-Lightweight yet Efficient Hyperparameter Optimization in Deep Reinforcement Learning
Mingqi Yuan, Bo Li, Xin Jin et al.
Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs
Aldo Pareja, Nikhil Shivakumar Nayak, Hao Wang et al.
A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization
Ashwinee Panda, Xinyu Tang, Saeed Mahloujifar et al.
In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization
Herilalaina Rakotoarison, Steven Adriaensen, Neeratyoy Mallik et al.
Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learning
Joseph Giovanelli, Alexander Tornede, Tanja Tornede et al.
Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence Rates
Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo
Prior Specification for Bayesian Matrix Factorization via Prior Predictive Matching
Eliezer de Souza da Silva, Tomasz Kuśmierczyk, Marcelo Hartmann et al.
Provably Convergent Federated Trilevel Learning
Yang Jiao, Kai YANG, Tiancheng Wu et al.