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Distributed Personalized Empirical Risk Minimization
To mitigate the negative effect of data heterogeneity (non-IIDness), two common approaches are clustering and personalization.
Divide, Evaluate, and Refine: Evaluating and Improving Text-to-Image Alignment with Iterative VQA Feedback Jaskirat Singh
To address these problems, in this paper we explore a simple yet effective decompositional approach towards both evaluation and improvement of text-to-image alignment.
Few-Shot Task Learning through Inverse Generative Modeling
Learning the intents of an agent, defined by its goals or motion style, is often extremely challenging from just a few examples.
TransAgent: Transfer Vision-Language Foundation Models with Heterogeneous Agent Collaboration
Unfortunately, these models are "isolated agents" with heterogeneous
Scale-invariant Optimal Sampling for Rare-events Data with Sparse Models
Rare-events data refer to binary-response data that are highly imbalanced, i.e., the number of zeros