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Found 5 papers, 3 papers with code
Adversarial Attacks on Combinatorial Multi-Armed Bandits
Contrary to prior understanding of multi-armed bandits, our work reveals a surprising fact that the attackability of a specific CMAB instance also depends on whether the bandit instance is known or unknown to the adversary.
Unified Off-Policy Learning to Rank: a Reinforcement Learning Perspective
Building upon this, we leverage offline RL techniques for off-policy LTR and propose the Click Model-Agnostic Unified Off-policy Learning to Rank (CUOLR) method, which could be easily applied to a wide range of click models.
Adversarial Attacks on Online Learning to Rank with Stochastic Click Models
We propose the first study of adversarial attacks on online learning to rank.
Contrastive Learning for Object Detection
Contrastive learning is commonly used as a method of self-supervised learning with the "anchor" and "positive" being two random augmentations of a given input image, and the "negative" is the set of all other images.
Contrastive Learning for OOD in Object detection
Contrastive learning is commonly applied to self-supervised learning, and has been shown to outperform traditional approaches such as the triplet loss and N-pair loss.
