37 citations · 78 across the 38 of their papers we have counts for
6 papers · 1 filter
Always-On Experimentation
Ricardo J. Sandoval, David Arbour, Avi Feller +1
Generative AI has dramatically accelerated the rate at which new treatments---from novel pharmaceuticals to online marketing campaigns---can be conceived and deployed. As a result,…
Toward Latent Language Model Skills Steering and Optimization: An Empirical Study
Xunyi Jiang, Junda Wu, Yuxin Xiong +6
Skills, as a useful abstraction for the procedural capabilities of large language models (LLMs), capture how models perform structured, multi-step reasoning and program execution.…
BACON: Budgeted Human Calibration for Modeling and Evaluation with Multiple AI Judges
Lei Shi, Anlan Zhang, Rita Lyu +6
AI judges offer a scalable, low-cost alternative to human evaluation, but their outputs can be biased relative to human preferences and highly item-dependent, varying across judges…
The Curse of Multiple Mediators: Hidden Interaction Effects in Activation Patching
Sankaran Vaidyanathan, David Arbour, Aaron Mueller +2
Activation patching is the primary tool in mechanistic interpretability. It attributes causal responsibility for a model behavior to each of its individual components by estimating…
AI-Assisted Variance Reduction in Randomized Experiments
David Arbour, Eli Ben-Michael, Avi Feller +2
Generative AI and large language models can produce realistic predictions of human behavior from rich, unstructured inputs with little to no task-specific training data. Recent wor…
Experimentation Accelerator: Interpretable Insights and Creative Recommendations for A/B Testing with Content-Aware ranking
Zhengmian Hu, Lei Shi, Ritwik Sinha +2
Modern online experimentation faces two bottlenecks: scarce traffic forces tough choices on which variants to test, and post-hoc insight extraction is manual, inconsistent, and oft…