1 citations · 1 across the 7 of their papers we have counts for
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Matryoshka Concept Bottleneck Models
Ziye Chen, Hongbin Lin, Jie Li +1
Concept Bottleneck Models (CBMs) have emerged as a prominent paradigm for interpretable deep learning, learning by grounding predictions in human-understandable concepts. However,…
AR1-ZO: Topology-Aware Rank-1 Zeroth-Order Queries for High-Rank LoRA Fine-Tuning
Ziye Chen, Hongbin Lin, Chenyu Zhang +3
Zeroth-order (ZO) optimization enables large-language-model fine-tuning without storing backpropagation activations, while LoRA supplies compact trainable adapters. Combining them…
Algorithmic Recourse of In-Context Learning for Tabular Data
Wenshuo Dong, Jiaming Zhang, Shaopeng Fu +3
As predictive models are increasingly deployed in high-stakes settings such as credit approval, there is a growing need for post-hoc methods that provide recourse to affected indiv…
Controllable Concept Bottleneck Models
Hongbin Lin, Chenyang Ren, Juangui Xu +7
Concept Bottleneck Models (CBMs) have garnered much attention for their ability to elucidate the prediction process through a human-understandable concept layer. However, most prev…