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Found 9 papers, 2 papers with code
Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews
We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM).
Data-Driven Subgroup Identification for Linear Regression
To do this, simple parametric models are frequently used (e. g. coefficients of linear regression) but usually fitted on the whole dataset.
Provable Membership Inference Privacy
However, DP's strong theoretical guarantees often come at the cost of a large drop in its utility for machine learning, and DP guarantees themselves can be difficult to interpret.
Continuous-in-time Limit for Bayesian Bandits
The optimal policy for the limiting HJB equation can be explicitly obtained for several common bandit problems, and we give numerical methods to solve the HJB equation when an explicit solution is not available.
Importance Tempering: Group Robustness for Overparameterized Models
In this paper, we propose importance tempering to improve the decision boundary and achieve consistently better results for overparameterized models.
How to Learn when Data Gradually Reacts to Your Model
A recent line of work has focused on training machine learning (ML) models in the performative setting, i. e. when the data distribution reacts to the deployed model.
Dimensionality Reduction for Wasserstein Barycenter
In order to cope with this "curse of dimensionality," we study dimensionality reduction techniques for the Wasserstein barycenter problem.
How to Learn when Data Reacts to Your Model: Performative Gradient Descent
Performative distribution shift captures the setting where the choice of which ML model is deployed changes the data distribution.
Approximate Data Deletion from Machine Learning Models
Deleting data from a trained machine learning (ML) model is a critical task in many applications.
