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Stochastic Two Points Method for Deep Model Zeroth-order Optimization
The theoretical properties also shed light on a faster and more stable S2P variant, Accelerated S2P (AS2P), through exploiting our new convergence properties that better represent the dynamics of deep models in training.
Cross-modality debiasing: using language to mitigate sub-population shifts in imaging
Specifically, in the context of the distributional robustness of CLIP, we propose to leverage natural language inputs to debias the image feature representations, to improve worst-case performance on sub-populations.
RUSH: Robust Contrastive Learning via Randomized Smoothing
In this paper, we show a surprising fact that contrastive pre-training has an interesting yet implicit connection with robustness, and such natural robustness in the pre trained representation enables us to design a powerful robust algorithm against adversarial attacks, RUSH, that combines the standard contrastive pre-training and randomized smoothing.
Proficiency Constrained Multi-Agent Reinforcement Learning for Environment-Adaptive Multi UAV-UGV Teaming
Due to heterogeneous robots inside a team and the resilient capabilities of robots, it is challenging to perform a task with an optimal balance between reasonable task allocations and maximum utilization of robot capability.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
