7 papers
Reasoning with Sampling: Cutting at Decision Points
Felix Zhou, Anay Mehrotra, Quanquan C. Liu
Frontier reasoning models are produced by posttraining base language models with reinforcement learning. Recent work has challenged this by showing that sampling from a sharpened v…
Differentially Private Language Generation and Identification in the Limit
Anay Mehrotra, Grigoris Velegkas, Xifan Yu +1
We initiate the study of language generation in the limit, a model recently introduced by Kleinberg and Mullainathan [KM24], under the constraint of differential privacy. We consid…
Differentially Private Matchings
Michael Dinitz, George Z. Li, Quanquan C. Liu +1
Computing matchings in graphs is a foundational algorithmic task. Despite extensive interest in differentially private (DP) graph analysis, work on privately computing matching sol…
Pointwise Lipschitz Continuous Graph Algorithms
Quanquan C. Liu, Grigoris Velegkas, Yuichi Yoshida +1
In many real-world applications, it is undesirable to drastically change the problem solution after a small perturbation in the input, as unstable outputs can lead to costly transa…
Private Training & Data Generation by Clustering Embeddings
Felix Zhou, Samson Zhou, Vahab Mirrokni +2
Deep neural networks often use large, high-quality datasets to achieve high performance on many machine learning tasks. When training involves potentially sensitive data, this proc…
Sublinear Space Graph Algorithms in the Continual Release Model
Alessandro Epasto, Quanquan C. Liu, Tamalika Mukherjee +1
The graph continual release model of differential privacy seeks to produce differentially private solutions to graph problems under a stream of edge updates where new private solut…