7 papers
Language Model Representations for Efficient Few-Shot Tabular Classification
Inwon Kang, Parikshit Ram, Yi Zhou +2
The Web is a rich source of structured data in the form of tables, from product catalogs and knowledge bases to scientific datasets. However, the heterogeneity of the structure and…
Balancing Multi-modal Sensor Learning via Multi-objective Optimization
Heshan Fernando, Quan Xiao, Parikshit Ram +4
Learning-enabled control systems increasingly rely on multiple sensing modalities (e.g., vision, audio, language, etc.) for perception and decision support. A key challenge is that…
Reasoning Model Unlearning: Forgetting Traces, Not Just Answers, While Preserving Reasoning Skills
Changsheng Wang, Chongyu Fan, Yihua Zhang +5
Recent advances in large reasoning models (LRMs) have enabled strong chain-of-thought (CoT) generation through test-time computation. While these multi-step reasoning capabilities…
Invariance Makes LLM Unlearning Resilient Even to Unanticipated Downstream Fine-Tuning
Changsheng Wang, Yihua Zhang, Jinghan Jia +6
Machine unlearning offers a promising solution to privacy and safety concerns in large language models (LLMs) by selectively removing targeted knowledge while preserving utility. H…
On Learning Representations for Tabular Data Distillation
Inwon Kang, Parikshit Ram, Yi Zhou +2
Dataset distillation generates a small set of information-rich instances from a large dataset, resulting in reduced storage requirements, privacy or copyright risks, and computatio…
WAGLE: Strategic Weight Attribution for Effective and Modular Unlearning in Large Language Models
Jinghan Jia, Jiancheng Liu, Yihua Zhang +3
The need for effective unlearning mechanisms in large language models (LLMs) is increasingly urgent, driven by the necessity to adhere to data regulations and foster ethical genera…