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most citedAre Data-driven Explanations Robust against Out-of-distribution Data?

2 citations · 6 across the 9 of their papers we have counts for

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cs.LG2024

Beyond Accuracy: On the Effects of Fine-tuning Towards Vision-Language Model's Prediction Rationality

Qitong Wang, Tang Li, Kien X. Nguyen +1

Vision-Language Models (VLMs), such as CLIP, have already seen widespread applications. Researchers actively engage in further fine-tuning VLMs in safety-critical domains. In these…

cs.LG2024★ 1 cited

Beyond Accuracy: Ensuring Correct Predictions With Correct Rationales

Tang Li, Mengmeng Ma, Xi Peng

Large pretrained foundation models demonstrate exceptional performance and, in some high-stakes applications, even surpass human experts. However, most of these models are currentl…

cs.LG2024

Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clients

Mengmeng Ma, Tang Li, Xi Peng

Federated Learning is widely employed to tackle distributed sensitive data. Existing methods primarily focus on addressing in-federation data heterogeneity. However, we observed th…

cs.LG2023★ 2 cited

Are Data-driven Explanations Robust against Out-of-distribution Data?

Tang Li, Fengchun Qiao, Mengmeng Ma +1

As black-box models increasingly power high-stakes applications, a variety of data-driven explanation methods have been introduced. Meanwhile, machine learning models are constantl…

cs.LG2021★ 1 cited

Deep Learning for Spatiotemporal Modeling of Urbanization

Tang Li, Jing Gao, Xi Peng

Urbanization has a strong impact on the health and wellbeing of populations across the world. Predictive spatial modeling of urbanization therefore can be a useful tool for effecti…