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06/13/2023
(Amplified) Banded Matrix Factorization: A unified approach to private training
Matrix factorization (MF) mechanisms for differential privacy (DP) have ...
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05/22/2023
Faster Differentially Private Convex Optimization via Second-Order Methods
Differentially private (stochastic) gradient descent is the workhorse of...
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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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02/19/2023
Why Is Public Pretraining Necessary for Private Model Training?
In the privacy-utility tradeoff of a model trained on benchmark language...
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10/04/2022
Recycling Scraps: Improving Private Learning by Leveraging Intermediate Checkpoints
All state-of-the-art (SOTA) differentially private machine learning (DP ...
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04/04/2022
Langevin Diffusion: An Almost Universal Algorithm for Private Euclidean (Convex) Optimization
In this paper we revisit the problem of differentially private empirical...
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12/10/2021
How Compression and Approximation Affect Efficiency in String Distance Measures
Real-world data often comes in compressed form. Analyzing compressed dat...
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05/05/2021
Universal Algorithms for Clustering
This paper presents universal algorithms for clustering problems, includ...
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10/27/2020
Faster Differentially Private Samplers via Rényi Divergence Analysis of Discretized Langevin MCMC
Various differentially private algorithms instantiate the exponential me...
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10/04/2020
Privately Answering Counting Queries with Generalized Gaussian Mechanisms
We consider the problem of answering k counting (i.e. sensitivity-1) que...
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07/06/2020
Near-Linear Time Edit Distance for Indel Channels
We consider the following model for sampling pairs of strings: s_1 is a ...
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05/17/2020
Robust Algorithms for TSP and Steiner Tree
Robust optimization is a widely studied area in operations research, whe...
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11/02/2018
