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20242026
most citedAdaptive and Robust Watermark for Generative Tabular Data

1 citations · 1 across the 1 of their papers we have counts for

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13 papers

cs.CR20261 cited

Adaptive and Robust Watermark for Generative Tabular Data

Dung Daniel Ngo, Archan Ray, Akshay Seshadri +6

In recent years, watermarking generative tabular data has become a prominent framework to protect against the misuse of synthetic data. However, while most prior work in watermarki…

math.OC2025

Distributionally and Adversarially Robust Logistic Regression via Intersecting Wasserstein Balls

Aras Selvi, Eleonora Kreacic, Mohsen Ghassemi +3

Adversarially robust optimization (ARO) has emerged as the *de facto* standard for training models that hedge against adversarial attacks in the test stage. While these models are…

cs.LG2025

LETS-C: Leveraging Text Embedding for Time Series Classification

Rachneet Kaur, Zhen Zeng, Tucker Balch +1

Recent advancements in language modeling have shown promising results when applied to time series data. In particular, fine-tuning pre-trained large language models (LLMs) for time…

cs.CV2025

TADACap: Time-series Adaptive Domain-Aware Captioning

Elizabeth Fons, Rachneet Kaur, Zhen Zeng +4

While image captioning has gained significant attention, the potential of captioning time-series images, prevalent in areas like finance and healthcare, remains largely untapped. E…

cs.AI2024

LAW: Legal Agentic Workflows for Custody and Fund Services Contracts

William Watson, Nicole Cho, Nishan Srishankar +7

Legal contracts in the custody and fund services domain govern critical aspects such as key provider responsibilities, fee schedules, and indemnification rights. However, it is cha…

cs.AI2024

AdaptAgent: Adapting Multimodal Web Agents with Few-Shot Learning from Human Demonstrations

Gaurav Verma, Rachneet Kaur, Nishan Srishankar +3

State-of-the-art multimodal web agents, powered by Multimodal Large Language Models (MLLMs), can autonomously execute many web tasks by processing user instructions and interacting…