34 citations · 169 across the 29 of their papers we have counts for
11 papers · 1 filter
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…
Robust Sampling for Active Statistical Inference
Puheng Li, Tijana Zrnic, Emmanuel Candès
Active statistical inference is a new method for inference with AI-assisted data collection. Given a budget on the number of labeled data points that can be collected and assuming…
Probably Approximately Correct Labels
Emmanuel J. Candès, Andrew Ilyas, Tijana Zrnic
Obtaining high-quality labeled datasets is often costly, requiring either human annotation or expensive experiments. In theory, powerful pre-trained AI models provide an opportunit…
Predictions as Surrogates: Revisiting Surrogate Outcomes in the Age of AI
Wenlong Ji, Lihua Lei, Tijana Zrnic
We establish a formal connection between the decades-old surrogate outcome model in biostatistics and economics and the emerging field of prediction-powered inference (PPI). The co…
A Flexible Defense Against the Winner's Curse
Tijana Zrnic, William Fithian
Across science and policy, decision-makers often need to draw conclusions about the best candidate among competing alternatives. For instance, researchers may seek to infer the eff…
A Note on the Prediction-Powered Bootstrap
Tijana Zrnic
We introduce PPBoot: a bootstrap-based method for prediction-powered inference. PPBoot is applicable to arbitrary estimation problems and is very simple to implement, essentially o…
Active Statistical Inference
Tijana Zrnic, Emmanuel J. Candès
Inspired by the concept of active learning, we propose active inference$\unicode{x2013}$a methodology for statistical inference with machine-learning-assisted data collection. Assu…
PPI++: Efficient Prediction-Powered Inference
Anastasios N. Angelopoulos, John C. Duchi, Tijana Zrnic
We present PPI++: a computationally lightweight methodology for estimation and inference based on a small labeled dataset and a typically much larger dataset of machine-learning pr…
Cross-Prediction-Powered Inference
Tijana Zrnic, Emmanuel J. Candès
While reliable data-driven decision-making hinges on high-quality labeled data, the acquisition of quality labels often involves laborious human annotations or slow and expensive s…
Prediction-Powered Inference
Anastasios N. Angelopoulos, Stephen Bates, Clara Fannjiang +2
Prediction-powered inference is a framework for performing valid statistical inference when an experimental dataset is supplemented with predictions from a machine-learning system.…