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Found 24 papers, 12 papers with code
Predictions as Surrogates: Revisiting Surrogate Outcomes in the Age of AI
We further analyze the benefits of recalibration, both theoretically and numerically, in several common scenarios where machine learning predictions systematically deviate from the outcome of interest.
A Flexible Defense Against the Winner's Curse
Across science and policy, decision-makers often need to draw conclusions about the best candidate among competing alternatives.
Can Unconfident LLM Annotations Be Used for Confident Conclusions?
We introduce Confidence-Driven Inference: a method that combines LLM annotations and LLM confidence indicators to strategically select which human annotations should be collected, with the goal of producing accurate statistical estimates and provably valid confidence intervals while reducing the number of human annotations needed.
A Note on the Prediction-Powered Bootstrap
We introduce PPBoot: a bootstrap-based method for prediction-powered inference.
Active Statistical Inference
This means that for the same number of collected samples, active inference enables smaller confidence intervals and more powerful p-values.
PPI++: Efficient Prediction-Powered Inference
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 predictions.
Cross-Prediction-Powered Inference
We show that cross-prediction is consistently more powerful than an adaptation of prediction-powered inference in which a fraction of the labeled data is split off and used to train the model.
Plug-in Performative Optimization
A complementary family of solutions makes use of explicit \emph{models} for the feedback, such as best-response models in strategic classification, enabling faster rates.
Algorithmic Collective Action in Machine Learning
We initiate a principled study of algorithmic collective action on digital platforms that deploy machine learning algorithms.
Prediction-Powered Inference
Prediction-powered inference is a framework for performing valid statistical inference when an experimental dataset is supplemented with predictions from a machine-learning system.
Valid Inference After Causal Discovery
Causal discovery and causal effect estimation are two fundamental tasks in causal inference.
Regret Minimization with Performative Feedback
Our main contribution is an algorithm that achieves regret bounds scaling only with the complexity of the distribution shifts and not that of the reward function.
Who Leads and Who Follows in Strategic Classification?
In particular, by generalizing the standard model to allow both players to learn over time, we show that a decision-maker that makes updates faster than the agents can reverse the order of play, meaning that the agents lead and the decision-maker follows.
Individual Privacy Accounting via a Rényi Filter
In this work, we give a method for tighter privacy loss accounting based on the value of a personalized privacy loss estimate for each individual in each analysis.
Outside the Echo Chamber: Optimizing the Performative Risk
In performative prediction, predictions guide decision-making and hence can influence the distribution of future data.
Private Prediction Sets
Our method follows the general approach of split conformal prediction; we use holdout data to calibrate the size of the prediction sets but preserve privacy by using a privatized quantile subroutine.
Individual Privacy Accounting via a Renyi Filter
We consider a sequential setting in which a single dataset of individuals is used to perform adaptively-chosen analyses, while ensuring that the differential privacy loss of each participant does not exceed a pre-specified privacy budget.
Stochastic Optimization for Performative Prediction
In performative prediction, the choice of a model influences the distribution of future data, typically through actions taken based on the model's predictions.
Performative Prediction
When predictions support decisions they may influence the outcome they aim to predict.
The Power of Batching in Multiple Hypothesis Testing
One important partition of algorithms for controlling the false discovery rate (FDR) in multiple testing is into offline and online algorithms.
Natural Analysts in Adaptive Data Analysis
The source of these pessimistic bounds is a model that permits arbitrary, possibly adversarial analysts that optimally use information to bias results.
Asynchronous Online Testing of Multiple Hypotheses
We consider the problem of asynchronous online testing, aimed at providing control of the false discovery rate (FDR) during a continual stream of data collection and testing, where each test may be a sequential test that can start and stop at arbitrary times.
SAFFRON: an adaptive algorithm for online control of the false discovery rate
However, unlike older methods, SAFFRON's threshold sequence is based on a novel estimate of the alpha fraction that it allocates to true null hypotheses.
