25 citations · 38 across the 18 of their papers we have counts for
5 papers · 1 filter
ContinuousBench: Can Differentially Private Synthetic Text Improve Capabilities?
Peihan Liu, Lucas Rosenblatt, Weiwei Kong +7
Differentially private (DP) text synthesis promises to unlock sensitive corpora for model training, but it remains unclear whether DP synthetic data transmits genuinely new knowled…
Towards Provably Unbiased LLM Judges via Bias-Bounded Evaluation
Benjamin Feuer, Lucas Rosenblatt, Oussama Elachqar
As AI models progress beyond simple chatbots into more complex workflows, we draw ever closer to the event horizon beyond which AI systems will be utilized in autonomous, self-main…
Exactly Computing do-Shapley Values
R. Teal Witter, Álvaro Parafita, Tomas Garriga +4
Structural Causal Models (SCM) are a powerful framework for describing complicated dynamics across the natural sciences. A particularly elegant way of interpreting SCMs is do-Shapl…
Privately Fine-Tuned LLMs Preserve Temporal Dynamics in Tabular Data
Lucas Rosenblatt, Peihan Liu, Ryan McKenna +1
Research on differentially private synthetic tabular data has largely focused on independent and identically distributed rows where each record corresponds to a unique individual.…
Explanation Multiplicity in SHAP: Characterization and Assessment
Hyunseung Hwang, Seungeun Lee, Lucas Rosenblatt +2
SHAP explanations are widely used in high-stakes settings to justify decisions, yet they can differ substantially across repeated runs, even when the model, the input instance, and…