Summary of Robust Preference Optimization Through Reward Model Distillation, by Adam Fisch et al.
Robust Preference Optimization through Reward Model Distillationby Adam Fisch, Jacob Eisenstein, Vicky Zayats, Alekh Agarwal,…
Summary of Provable Contrastive Continual Learning, by Yichen Wen et al.
Provable Contrastive Continual Learningby Yichen Wen, Zhiquan Tan, Kaipeng Zheng, Chuanlong Xie, Weiran HuangFirst submitted…
Summary of Fedhpl: Efficient Heterogeneous Federated Learning with Prompt Tuning and Logit Distillation, by Yuting Ma et al.
FedHPL: Efficient Heterogeneous Federated Learning with Prompt Tuning and Logit Distillationby Yuting Ma, Lechao Cheng,…
Summary of Em Distillation For One-step Diffusion Models, by Sirui Xie et al.
EM Distillation for One-step Diffusion Modelsby Sirui Xie, Zhisheng Xiao, Diederik P Kingma, Tingbo Hou,…
Summary of Enhancing Consistency-based Image Generation Via Adversarialy-trained Classification and Energy-based Discrimination, by Shelly Golan et al.
Enhancing Consistency-Based Image Generation via Adversarialy-Trained Classification and Energy-Based Discriminationby Shelly Golan, Roy Ganz, Michael…
Summary of Score Distillation Via Reparametrized Ddim, by Artem Lukoianov et al.
Score Distillation via Reparametrized DDIMby Artem Lukoianov, Haitz Sáez de Ocáriz Borde, Kristjan Greenewald, Vitor…
Summary of Interpretable and Editable Programmatic Tree Policies For Reinforcement Learning, by Hector Kohler et al.
Interpretable and Editable Programmatic Tree Policies for Reinforcement Learningby Hector Kohler, Quentin Delfosse, Riad Akrour,…
Summary of Sfddm: Single-fold Distillation For Diffusion Models, by Chi Hong et al.
SFDDM: Single-fold Distillation for Diffusion modelsby Chi Hong, Jiyue Huang, Robert Birke, Dick Epema, Stefanie…
Summary of Recurrent Early Exits For Federated Learning with Heterogeneous Clients, by Royson Lee et al.
Recurrent Early Exits for Federated Learning with Heterogeneous Clientsby Royson Lee, Javier Fernandez-Marques, Shell Xu…
Summary of Gift: Unlocking Full Potential Of Labels in Distilled Dataset at Near-zero Cost, by Xinyi Shang et al.
GIFT: Unlocking Full Potential of Labels in Distilled Dataset at Near-zero Costby Xinyi Shang, Peng…