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Found 7 papers, 5 papers with code
Bayesian Multi-Task Transfer Learning for Soft Prompt Tuning
We argue that when we extract knowledge from source tasks via training source prompts, we need to consider this correlation among source tasks for better transfer to target tasks.
FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning
FedSOL is designed to identify gradients of local objectives that are inherently orthogonal to directions affecting the proximal objective.
Toward Risk-based Optimistic Exploration for Cooperative Multi-Agent Reinforcement Learning
In this paper, we present a risk-based exploration that leads to collaboratively optimistic behavior by shifting the sampling region of distribution.
Distributional Reinforcement Learning
Multi-agent Reinforcement Learning
+2
Revisiting Intermediate Layer Distillation for Compressing Language Models: An Overfitting Perspective
Knowledge distillation (KD) is a highly promising method for mitigating the computational problems of pre-trained language models (PLMs).
Preservation of the Global Knowledge by Not-True Distillation in Federated Learning
In federated learning, a strong global model is collaboratively learned by aggregating clients' locally trained models.
Robust Streaming PCA
We consider streaming principal component analysis when the stochastic data-generating model is subject to perturbations.
Contextual Linear Bandits under Noisy Features: Towards Bayesian Oracles
This implies that classical approaches cannot guarantee a non-trivial regret bound.
