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Found 7 papers, 2 papers with code
HCVP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalization
Domain Generalization (DG) endeavors to create machine learning models that excel in unseen scenarios by learning invariant features.
How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary Investigation
We further investigate its adaptability to controlled data perturbations and examine the efficacy of in-context learning as a tool to enhance its adaptation.
Emerging Synergies in Causality and Deep Generative Models: A Survey
In the field of artificial intelligence (AI), the quest to understand and model data-generating processes (DGPs) is of paramount importance.
Learning to Infer Counterfactuals: Meta-Learning for Estimating Multiple Imbalanced Treatment Effects
We regularly consider answering counterfactual questions in practice, such as "Would people with diabetes take a turn for the better had they choose another medication?".
Contrastive Counterfactual Learning for Causality-aware Interpretable Recommender Systems
Recognizing that confounders may be elusive, we propose a contrastive self-supervised learning to minimize exposure bias, employing inverse propensity scores and expanding the positive sample set.
Cycle-Balanced Representation Learning For Counterfactual Inference
With the widespread accumulation of observational data, researchers obtain a new direction to learn counterfactual effects in many domains (e. g., health care and computational advertising) without Randomized Controlled Trials(RCTs).
A Survey of Deep Reinforcement Learning in Recommender Systems: A Systematic Review and Future Directions
In light of the emergence of deep reinforcement learning (DRL) in recommender systems research and several fruitful results in recent years, this survey aims to provide a timely and comprehensive overview of the recent trends of deep reinforcement learning in recommender systems.
