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07/25/2023
Federated Heavy Hitter Recovery under Linear Sketching
Motivated by real-life deployments of multi-round federated analytics wi...
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07/19/2023
The importance of feature preprocessing for differentially private linear optimization
Training machine learning models with differential privacy (DP) has rece...
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07/10/2023
FedYolo: Augmenting Federated Learning with Pretrained Transformers
The growth and diversity of machine learning applications motivate a ret...
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03/01/2023
Subset-Based Instance Optimality in Private Estimation
We propose a new definition of instance optimality for differentially pr...
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02/14/2023
Concentration Bounds for Discrete Distribution Estimation in KL Divergence
We study the problem of discrete distribution estimation in KL divergenc...
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08/12/2022
Private Domain Adaptation from a Public Source
A key problem in a variety of applications is that of domain adaptation ...
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06/07/2022
Histogram Estimation under User-level Privacy with Heterogeneous Data
We study the problem of histogram estimation under user-level differenti...
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04/21/2022
Differentially Private Learning with Margin Guarantees
We present a series of new differentially private (DP) algorithms with d...
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03/09/2022
Correlated quantization for distributed mean estimation and optimization
We study the problem of distributed mean estimation and optimization und...
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03/07/2022
The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning
We consider the problem of training a d dimensional model with distribut...
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08/04/2021
FedJAX: Federated learning simulation with JAX
Federated learning is a machine learning technique that enables training...
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06/18/2021
On the benefits of maximum likelihood estimation for Regression and Forecasting
We advocate for a practical Maximum Likelihood Estimation (MLE) approach...
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05/11/2021
On the Renyi Differential Privacy of the Shuffle Model
The central question studied in this paper is Renyi Differential Privacy...
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04/06/2021
Communication-Efficient Agnostic Federated Averaging
In distributed learning settings such as federated learning, the trainin...
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03/04/2021
Remember What You Want to Forget: Algorithms for Machine Unlearning
We study the problem of forgetting datapoints from a learnt model. In th...
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02/23/2021
Learning with User-Level Privacy
We propose and analyze algorithms to solve a range of learning tasks und...
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11/24/2020
Wyner-Ziv Estimators: Efficient Distributed Mean Estimation with Side Information
Communication efficient distributed mean estimation is an important prim...
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11/03/2020
Robust hypothesis testing and distribution estimation in Hellinger distance
We propose a simple robust hypothesis test that has the same sample comp...
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08/25/2020
Multiple-Source Adaptation with Domain Classifiers
We consider the multiple-source adaptation (MSA) problem and improve a p...
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08/17/2020
Shuffled Model of Federated Learning: Privacy, Communication and Accuracy Trade-offs
We consider a distributed empirical risk minimization (ERM) optimization...
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08/08/2020
Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning
Federated learning is a challenging optimization problem due to the hete...
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07/27/2020
Learning discrete distributions: user vs item-level privacy
Much of the literature on differential privacy focuses on item-level pri...
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07/19/2020
A Theory of Multiple-Source Adaptation with Limited Target Labeled Data
We study multiple-source domain adaptation, when the learner has access ...
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06/26/2020
Relative Deviation Margin Bounds
We present a series of new and more favorable margin-based learning guar...
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02/25/2020
Three Approaches for Personalization with Applications to Federated Learning
The standard objective in machine learning is to train a single model fo...
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01/13/2020
Convergence of Chao Unseen Species Estimator
Support size estimation and the related problem of unseen species estima...
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12/10/2019
Advances and Open Problems in Federated Learning
Federated learning (FL) is a machine learning setting where many clients...
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11/18/2019
Can You Really Backdoor Federated Learning?
The decentralized nature of federated learning makes detecting and defen...
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10/14/2019
SCAFFOLD: Stochastic Controlled Averaging for On-Device Federated Learning
Federated learning is a key scenario in modern large-scale machine learn...
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10/08/2019
Differentially private anonymized histograms
For a dataset of label-count pairs, an anonymized histogram is the multi...
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10/08/2019
Federated Learning of N-gram Language Models
We propose algorithms to train production-quality n-gram language models...
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08/20/2019
AdaCliP: Adaptive Clipping for Private SGD
Privacy preserving machine learning algorithms are crucial for learning ...
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08/08/2019
Optimal multiclass overfitting by sequence reconstruction from Hamming queries
A primary concern of excessive reuse of test datasets in machine learnin...
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07/24/2019
Sampled Softmax with Random Fourier Features
The computational cost of training with softmax cross entropy loss grows...
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05/21/2019
Approximating probabilistic models as weighted finite automata
Weighted finite automata (WFA) are often used to represent probabilistic...
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02/01/2019
Agnostic Federated Learning
A key learning scenario in large-scale applications is that of federated...
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11/20/2018
WEST: Word Encoded Sequence Transducers
Most of the parameters in large vocabulary models are used in embedding ...
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05/27/2018
cpSGD: Communication-efficient and differentially-private distributed SGD
Distributed stochastic gradient descent is an important subroutine in di...
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11/15/2017
Lattice Rescoring Strategies for Long Short Term Memory Language Models in Speech Recognition
Recurrent neural network (RNN) language models (LMs) and Long Short Term...
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09/18/2017
Model-Powered Conditional Independence Test
We consider the problem of non-parametric Conditional Independence testi...
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02/18/2017
Sample complexity of population recovery
The problem of population recovery refers to estimating a distribution b...
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10/28/2016
Orthogonal Random Features
We present an intriguing discovery related to Random Fourier Features: i...
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11/23/2015
Estimating the number of unseen species: A bird in the hand is worth n in the bush
Estimating the number of unseen species is an important problem in many ...
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02/19/2014