34 citations · 169 across the 29 of their papers we have counts for
5 papers · 1 filter
Knowing When to Intervene: Causal Evaluation of AI Agents
Ayush Sawarni, Wenqian Xing, Tijana Zrnic +2
Current evaluations of agentic AI systems primarily rely on benchmarks. However, the static nature of benchmarks makes them uninformative about the value of auditing interventions…
Reliable AI-Assisted Decisions via Selective Auditing
Isaac Gibbs, Elise L. Han, Ryan J. Tibshirani +1
We consider online monitoring of an AI system that produces a sequence of decisions. At each round, the system outputs a proposed decision together with a confidence score estimati…
High-Value Training Data from Noisy, Diverse Sources
Hedi Driss, Evan Frick, Anastasios N. Angelopoulos +4
Training state-of-the-art models increasingly requires learning from large, heterogeneous datasets whose examples vary in quality, domain relevance, and source of supervision. Data…
Partially Performative Prediction
Jaewook Lee, Tijana Zrnic
Performative prediction studies feedback loops that arise when predictive models are deployed in consequential domains. In these settings, deploying a model can change the populati…
Valid Inference with Synthetic Data via Task Exchangeability
Lezhi Tan, Tijana Zrnic
There is a proliferation of work arguing for the use of synthetic data in scientific research. For example, social scientists are arguing for the use of LLM-generated "silicon samp…