8 citations · 36 across the 17 of their papers we have counts for
17 papers
Learning across Data Owners with Joint Differential Privacy
Yangsibo Huang, Haotian Jiang, Daogao Liu +3
In this paper, we study the setting in which data owners train machine learning models collaboratively under a privacy notion called joint differential privacy [Kearns et al., 2018…
NN-Adapter: Efficient Domain Adaptation for Black-Box Language Models
Yangsibo Huang, Daogao Liu, Zexuan Zhong +2
Fine-tuning a language model on a new domain is standard practice for domain adaptation. However, it can be infeasible when it comes to modern large-scale language models such as G…
Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks
Arun Ganesh, Daogao Liu, Sewoong Oh +1
We consider the problem of minimizing a non-convex objective while preserving the privacy of the examples in the training data. Building upon the previous variance-reduced algorith…
Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler
Sivakanth Gopi, Yin Tat Lee, Daogao Liu +2
The development of efficient sampling algorithms catering to non-Euclidean geometries has been a challenging endeavor, as discretization techniques which succeed in the Euclidean s…
ReSQueing Parallel and Private Stochastic Convex Optimization
Yair Carmon, Arun Jambulapati, Yujia Jin +4
We introduce a new tool for stochastic convex optimization (SCO): a Reweighted Stochastic Query (ReSQue) estimator for the gradient of a function convolved with a (Gaussian) probab…
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…
Multi-token Markov Game with Switching Costs
Jian Li, Daogao Liu
We study a general Markov game with metric switching costs: in each round, the player adaptively chooses one of several Markov chains to advance with the objective of minimizing th…
The Convergence Rate of SGD's Final Iterate: Analysis on Dimension Dependence
Daogao Liu, Zhou Lu
Stochastic Gradient Descent (SGD) is among the simplest and most popular methods in optimization. The convergence rate for SGD has been extensively studied and tight analyses have…
The Power of Sampling: Dimension-free Risk Bounds in Private ERM
Yin Tat Lee, Daogao Liu, Zhou Lu
Differentially private empirical risk minimization (DP-ERM) is a fundamental problem in private optimization. While the theory of DP-ERM is well-studied, as large-scale models beco…
Private Non-smooth Empirical Risk Minimization and Stochastic Convex Optimization in Subquadratic Steps
Janardhan Kulkarni, Yin Tat Lee, Daogao Liu
We study the differentially private Empirical Risk Minimization (ERM) and Stochastic Convex Optimization (SCO) problems for non-smooth convex functions. We get a (nearly) optimal b…
Super-resolution and Robust Sparse Continuous Fourier Transform in Any Constant Dimension: Nearly Linear Time and Sample Complexity
Yaonan Jin, Daogao Liu, Zhao Song
The ability to resolve detail in the object that is being imaged, named by resolution, is the core parameter of an imaging system. Super-resolution is a class of techniques that ca…
Algorithms and Adaptivity Gaps for Stochastic -TSP
Haotian Jiang, Jian Li, Daogao Liu +1
Given a metric and a , the classic $\textsf{$k$-TSP}$ problem is to find a tour originating at the of minimum length that visits at lea…