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05/25/2023
Learning across Data Owners with Joint Differential Privacy
In this paper, we study the setting in which data owners train machine l...
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02/21/2023
kNN-Adapter: Efficient Domain Adaptation for Black-Box Language Models
Fine-tuning a language model on a new domain is standard practice for do...
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02/20/2023
Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks
We consider the problem of minimizing a non-convex objective while prese...
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02/13/2023
Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler
The development of efficient sampling algorithms catering to non-Euclide...
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01/01/2023
ReSQueing Parallel and Private Stochastic Convex Optimization
We introduce a new tool for stochastic convex optimization (SCO): a Rewe...
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10/21/2022
Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation
Knowledge distillation is one of the primary methods of transferring kno...
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07/19/2022
Pandora Box Problem with Nonobligatory Inspection: Hardness and Approximation Scheme
Weitzman (1979) introduced the Pandora Box problem as a model for sequen...
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07/18/2022
Private Convex Optimization in General Norms
We propose a new framework for differentially private optimization of co...
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07/01/2022
When Does Differentially Private Learning Not Suffer in High Dimensions?
Large pretrained models can be privately fine-tuned to achieve performan...
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03/01/2022
Private Convex Optimization via Exponential Mechanism
In this paper, we study private optimization problems for non-smooth con...
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02/22/2022
Better Private Algorithms for Correlation Clustering
In machine learning, correlation clustering is an important problem whos...
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07/13/2021
Markov Game with Switching Costs
We study a general Markov game with metric switching costs: in each roun...
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06/28/2021
The Convergence Rate of SGD's Final Iterate: Analysis on Dimension Dependence
Stochastic Gradient Descent (SGD) is among the simplest and most popular...
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05/28/2021
Curse of Dimensionality in Unconstrained Private Convex ERM
We consider the lower bounds of differentially private empirical risk mi...
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03/29/2021
Private Non-smooth Empirical Risk Minimization and Stochastic Convex Optimization in Subquadratic Steps
We study the differentially private Empirical Risk Minimization (ERM) an...
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05/13/2020
A robust multi-dimensional sparse Fourier transform in the continuous setting
Sparse Fourier transform (Sparse FT) is the problem of learning an unkno...
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11/06/2019