"hyperparameter tuning" Papers
20 papers found
Conference
A Clean Slate for Offline Reinforcement Learning
Matthew T Jackson, Uljad Berdica, Jarek Liesen et al.
AutoEdit: Automatic Hyperparameter Tuning for Image Editing
Chau Pham, Quan Dao, Mahesh Bhosale et al.
Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter Tuning
Lequan Lin, Dai Shi, Andi Han et al.
Empirical Study on Robustness and Resilience in Cooperative Multi-Agent Reinforcement Learning
Simin Li, Zihao Mao, Hanxiao Li et al.
Lessons and Insights from a Unifying Study of Parameter-Efficient Fine-Tuning (PEFT) in Visual Recognition
Zheda Mai, Ping Zhang, Cheng-Hao Tu et al.
Model Selection for Off-policy Evaluation: New Algorithms and Experimental Protocol
Pai Liu, Lingfeng Zhao, Shivangi Agarwal et al.
Power Lines: Scaling laws for weight decay and batch size in LLM pre-training
Shane Bergsma, Nolan Dey, Gurpreet Gosal et al.
Private Hyperparameter Tuning with Ex-Post Guarantee
Badih Ghazi, Pritish Kamath, Alexander Knop et al.
Problem-Parameter-Free Decentralized Bilevel Optimization
Zhiwei Zhai, Wenjing Yan, Ying-Jun Zhang
The Primacy of Magnitude in Low-Rank Adaptation
Zicheng Zhang, Haoran Li, Yifeng Zhang et al.
Towards General-Purpose Model-Free Reinforcement Learning
Scott Fujimoto, Pierluca D'Oro, Amy Zhang et al.
Towards hyperparameter-free optimization with differential privacy
Ruixuan Liu, Zhiqi Bu
ACM-MILP: Adaptive Constraint Modification via Grouping and Selection for Hardness-Preserving MILP Instance Generation
Ziao Guo, Yang Li, Chang Liu et al.
HyperFast: Instant Classification for Tabular Data
David Bonet, Daniel Mas Montserrat, Xavier Giró-i-Nieto et al.
Improved Modelling of Federated Datasets using Mixtures-of-Dirichlet-Multinomials
Jonathan Scott, Aine E Cahill
Kernel-Based Evaluation of Conditional Biological Sequence Models
Pierre Glaser, Steffan Paul, Alissa M. Hummer et al.
Posterior Sampling-Based Bayesian Optimization with Tighter Bayesian Regret Bounds
Shion Takeno, Yu Inatsu, Masayuki Karasuyama et al.
Reinforcement Learning Meets Visual Odometry
Nico Messikommer, Giovanni Cioffi, Mathias Gehrig et al.
Should we be going MAD? A Look at Multi-Agent Debate Strategies for LLMs
Andries Smit, Nathan Grinsztajn, Paul Duckworth et al.
Tuning-Free Stochastic Optimization
Ahmed Khaled, Chi Jin