Counterfactual Debiasing for Fact Verification - OpenReview 016 namely CLEVER, which is augmentation-free 017 and mitigates biases on the inference stage 018 Specifically, we train a claim-evidence fusion 019 model and a claim-only model independently 020 Then, we obtain the final prediction via sub-021 tracting output of the claim-only model from 022 output of the claim-evidence fusion model,
Weakly-Supervised Affordance Grounding Guided by Part-Level. . . In this work, we focus on the task of weakly supervised affordance grounding, where a model is trained to identify affordance regions on objects using human-object interaction images and egocentric object images without dense labels
LLMOPT: Learning to Define and Solve General Optimization Problems. . . Optimization problems are prevalent across various scenarios Formulating and then solving optimization problems described by natural language often requires highly specialized human expertise, which could block the widespread application of optimization-based decision making
Probabilistic Learning to Defer: Handling Missing Expert. . . Recent progress in machine learning research is gradually shifting its focus towards *human-AI cooperation* due to the advantages of exploiting the reliability of human experts and the efficiency of AI models
Thieves on Sesame Street! Model Extraction of BERT-based APIs Finally, we study two defense strategies against model extraction—membership classification and API watermarking—which while successful against some adversaries can also be circumvented by more clever ones