Summary of Entropy Controllable Direct Preference Optimization, by Motoki Omura et al.
Entropy Controllable Direct Preference Optimizationby Motoki Omura, Yasuhiro Fujita, Toshiki KataokaFirst submitted to arxiv on:…
Summary of Exploring Multi-agent Reinforcement Learning For Unrelated Parallel Machine Scheduling, by Maria Zampella et al.
Exploring Multi-Agent Reinforcement Learning for Unrelated Parallel Machine Schedulingby Maria Zampella, Urtzi Otamendi, Xabier Belaunzaran,…
Summary of Efficient Adaptive Optimization Via Subset-norm and Subspace-momentum: Fast, Memory-reduced Training with Convergence Guarantees, by Thien Hang Nguyen et al.
Efficient Adaptive Optimization via Subset-Norm and Subspace-Momentum: Fast, Memory-Reduced Training with Convergence Guaranteesby Thien Hang…
Summary of Wassffed: Wasserstein Fair Federated Learning, by Zhongxuan Han et al.
WassFFed: Wasserstein Fair Federated Learningby Zhongxuan Han, Li Zhang, Chaochao Chen, Xiaolin Zheng, Fei Zheng,…
Summary of Non-adversarial Inverse Reinforcement Learning Via Successor Feature Matching, by Arnav Kumar Jain et al.
Non-Adversarial Inverse Reinforcement Learning via Successor Feature Matchingby Arnav Kumar Jain, Harley Wiltzer, Jesse Farebrother,…
Summary of General Framework For Online-to-nonconvex Conversion: Schedule-free Sgd Is Also Effective For Nonconvex Optimization, by Kwangjun Ahn et al.
General framework for online-to-nonconvex conversion: Schedule-free SGD is also effective for nonconvex optimizationby Kwangjun Ahn,…
Summary of Scientific Machine Learning in Ecological Systems: a Study on the Predator-prey Dynamics, by Ranabir Devgupta et al.
Scientific machine learning in ecological systems: A study on the predator-prey dynamicsby Ranabir Devgupta, Raj…
Summary of Neuromodulated Meta-learning, by Jingyao Wang et al.
Neuromodulated Meta-Learningby Jingyao Wang, Huijie Guo, Wenwen Qiang, Jiangmeng Li, Changwen Zheng, Hui Xiong, Gang…
Summary of Meta-learning Objectives For Preference Optimization, by Carlo Alfano et al.
Meta-Learning Objectives for Preference Optimizationby Carlo Alfano, Silvia Sapora, Jakob Nicolaus Foerster, Patrick Rebeschini, Yee…
Summary of An Energy-based Self-adaptive Learning Rate For Stochastic Gradient Descent: Enhancing Unconstrained Optimization with Vav Method, by Jiahao Zhang et al.
An Energy-Based Self-Adaptive Learning Rate for Stochastic Gradient Descent: Enhancing Unconstrained Optimization with VAV methodby…