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07/24/2023
On the Connection between Pre-training Data Diversity and Fine-tuning Robustness
Pre-training has been widely adopted in deep learning to improve model p...
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07/20/2023
Private Federated Learning with Autotuned Compression
We propose new techniques for reducing communication in private federate...
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07/19/2023
Improving Multimodal Datasets with Image Captioning
Massive web datasets play a key role in the success of large vision-lang...
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05/29/2023
Unleashing the Power of Randomization in Auditing Differentially Private ML
We present a rigorous methodology for auditing differentially private ma...
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05/20/2023
Can Public Large Language Models Help Private Cross-device Federated Learning?
We study (differentially) private federated learning (FL) of language mo...
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04/27/2023
DataComp: In search of the next generation of multimodal datasets
Large multimodal datasets have been instrumental in recent breakthroughs...
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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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02/06/2023
One-shot Empirical Privacy Estimation for Federated Learning
Privacy auditing techniques for differentially private (DP) algorithms a...
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01/30/2023
Near Optimal Private and Robust Linear Regression
We study the canonical statistical estimation problem of linear regressi...
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01/16/2023
Machine Learning-Aided Efficient Decoding of Reed-Muller Subcodes
Reed-Muller (RM) codes achieve the capacity of general binary-input memo...
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11/20/2022
Learning to Generate Image Embeddings with User-level Differential Privacy
Small on-device models have been successfully trained with user-level di...
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10/14/2022
Zonotope Domains for Lagrangian Neural Network Verification
Neural network verification aims to provide provable bounds for the outp...
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10/12/2022
Few-shot Backdoor Attacks via Neural Tangent Kernels
In a backdoor attack, an attacker injects corrupted examples into the tr...
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10/02/2022
Stochastic optimization on matrices and a graphon McKean-Vlasov limit
We consider stochastic gradient descents on the space of large symmetric...
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10/01/2022
CRISP: Curriculum based Sequential Neural Decoders for Polar Code Family
Polar codes are widely used state-of-the-art codes for reliable communic...
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08/10/2022
Quality Not Quantity: On the Interaction between Dataset Design and Robustness of CLIP
Web-crawled datasets have enabled remarkable generalization capabilities...
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06/01/2022
Bring Your Own Algorithm for Optimal Differentially Private Stochastic Minimax Optimization
We study differentially private (DP) algorithms for smooth stochastic mi...
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05/27/2022
DP-PCA: Statistically Optimal and Differentially Private PCA
We study the canonical statistical task of computing the principal compo...
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05/24/2022
Towards a Defense against Backdoor Attacks in Continual Federated Learning
Backdoor attacks are a major concern in federated learning (FL) pipeline...
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02/07/2022
MAML and ANIL Provably Learn Representations
Recent empirical evidence has driven conventional wisdom to believe that...
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01/19/2022
Lifted Primal-Dual Method for Bilinearly Coupled Smooth Minimax Optimization
We study the bilinearly coupled minimax problem: min_xmax_y f(x) + y^⊤ A...
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11/18/2021
Gradient flows on graphons: existence, convergence, continuity equations
Wasserstein gradient flows on probability measures have found a host of ...
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11/12/2021
Differential privacy and robust statistics in high dimensions
We introduce a universal framework for characterizing the statistical ef...
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10/28/2021
Gradient Inversion with Generative Image Prior
Federated Learning (FL) is a distributed learning framework, in which th...
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08/29/2021
KO codes: Inventing Nonlinear Encoding and Decoding for Reliable Wireless Communication via Deep-learning
Landmark codes underpin reliable physical layer communication, e.g., Ree...
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08/16/2021
Reducing the Communication Cost of Federated Learning through Multistage Optimization
A central question in federated learning (FL) is how to design optimizat...
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06/15/2021
Divergence Frontiers for Generative Models: Sample Complexity, Quantization Level, and Frontier Integral
The spectacular success of deep generative models calls for quantitative...
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05/18/2021
Sample Efficient Linear Meta-Learning by Alternating Minimization
Meta-learning synthesizes and leverages the knowledge from a given set o...
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04/22/2021
SPECTRE: Defending Against Backdoor Attacks Using Robust Statistics
Modern machine learning increasingly requires training on a large collec...
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02/18/2021
Robust and Differentially Private Mean Estimation
Differential privacy has emerged as a standard requirement in a variety ...
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02/12/2021
Efficient Algorithms for Federated Saddle Point Optimization
We consider strongly convex-concave minimax problems in the federated se...
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02/08/2021
Eliminating Sharp Minima from SGD with Truncated Heavy-tailed Noise
The empirical success of deep learning is often attributed to SGD's myst...
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02/02/2021
Reed-Muller Subcodes: Machine Learning-Aided Design of Efficient Soft Recursive Decoding
Reed-Muller (RM) codes are conjectured to achieve the capacity of any bi...
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10/05/2020
Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method
We consider the classical setting of optimizing a nonsmooth Lipschitz co...
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08/18/2020
Deepcode and Modulo-SK are Designed for Different Settings
We respond to [1] which claimed that "Modulo-SK scheme outperforms Deepc...
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06/17/2020
Robust Meta-learning for Mixed Linear Regression with Small Batches
A common challenge faced in practical supervised learning, such as medic...
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02/20/2020
Meta-learning for mixed linear regression
In modern supervised learning, there are a large number of tasks, but ma...
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11/08/2019
Turbo Autoencoder: Deep learning based channel codes for point-to-point communication channels
Designing codes that combat the noise in a communication medium has rema...
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10/05/2019
Proof-of-Stake Longest Chain Protocols Revisited
The Nakamoto longest chain protocol has served Bitcoin well in its decad...
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09/18/2019
Barracuda: The Power of ℓ-polling in Proof-of-Stake Blockchains
A blockchain is a database of sequential events that is maintained by a ...
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09/06/2019
Privacy-Utility Tradeoffs in Routing Cryptocurrency over Payment Channel Networks
Payment channel networks (PCNs) are viewed as one of the most promising ...
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08/28/2019
Optimal transport mapping via input convex neural networks
In this paper, we present a novel and principled approach to learn the o...
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07/02/2019
Efficient Algorithms for Smooth Minimax Optimization
This paper studies first order methods for solving smooth minimax optimi...
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06/14/2019
InfoGAN-CR: Disentangling Generative Adversarial Networks with Contrastive Regularizers
Training disentangled representations with generative adversarial networ...
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06/09/2019
Robust conditional GANs under missing or uncertain labels
Matching the performance of conditional Generative Adversarial Networks ...
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06/06/2019
Learning in Gated Neural Networks
Gating is a key feature in modern neural networks including LSTMs, GRUs ...
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05/24/2019
Minimax Rates of Estimating Approximate Differential Privacy
Differential privacy has become a widely accepted notion of privacy, lea...
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03/06/2019
DeepTurbo: Deep Turbo Decoder
Present-day communication systems routinely use codes that approach the ...
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12/01/2018
