8 citations · 36 across the 17 of their papers we have counts for
6 papers · 1 filter
Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation
Ziqi Wang, Yuexin Wu, Frederick Liu +5
Knowledge distillation is one of the primary methods of transferring knowledge from large to small models. However, it requires massive task-specific data, which may not be plausib…
Pandora Box Problem with Nonobligatory Inspection: Hardness and Approximation Scheme
Hu Fu, Jiawei Li, Daogao Liu
Weitzman (1979) introduced the Pandora Box problem as a model for sequential search with inspection costs, and gave an elegant index-based policy that attains provably optimal expe…
Private Convex Optimization in General Norms
Sivakanth Gopi, Yin Tat Lee, Daogao Liu +2
We propose a new framework for differentially private optimization of convex functions which are Lipschitz in an arbitrary norm . Our algorithms are based on a regulariz…
When Does Differentially Private Learning Not Suffer in High Dimensions?
Xuechen Li, Daogao Liu, Tatsunori Hashimoto +4
Large pretrained models can be privately fine-tuned to achieve performance approaching that of non-private models. A common theme in these results is the surprising observation tha…
Private Convex Optimization via Exponential Mechanism
Sivakanth Gopi, Yin Tat Lee, Daogao Liu
In this paper, we study private optimization problems for non-smooth convex functions on . We show that modifying the exponential mechanism…
Better Private Algorithms for Correlation Clustering
Daogao Liu
In machine learning, correlation clustering is an important problem whose goal is to partition the individuals into groups that correlate with their pairwise similarities as much a…