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Found 19 papers, 7 papers with code
Synthetic Data Privacy Metrics
Recent advancements in generative AI have made it possible to create synthetic datasets that can be as accurate as real-world data for training AI models, powering statistical insights, and fostering collaboration with sensitive datasets while offering strong privacy guarantees.
Controllable Synthetic Clinical Note Generation with Privacy Guarantees
We conduct utility testing to evaluate the performance of machine learning models trained on the cloned datasets.
Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation
Our results demonstrate that our algorithm can achieve competitive performance with strong privacy levels.
Project Florida: Federated Learning Made Easy
Federated learning enables model training across devices and silos while the training data remains within its security boundary, by distributing a model snapshot to a client running inside the boundary, running client code to update the model, and then aggregating updated snapshots across many clients in a central orchestrator.
TrojanPuzzle: Covertly Poisoning Code-Suggestion Models
To achieve this, prior attacks explicitly inject the insecure code payload into the training data, making the poison data detectable by static analysis tools that can remove such malicious data from the training set.
Federated Multilingual Models for Medical Transcript Analysis
Federated Learning (FL) is a novel machine learning approach that allows the model trainer to access more data samples, by training the model across multiple decentralized data sources, while data access constraints are in place.
Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning
In this work, we propose a novel ensemble knowledge transfer method named Fed-ET in which small models (different in architecture) are trained on clients, and used to train a larger model at the server.
FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations
We compare the platform with other state-of-the-art platforms and describe available features of FLUTE for experimentation in core areas of active research, such as optimization, privacy, and scalability.
Differentially Private Fine-tuning of Language Models
For example, on the MNLI dataset we achieve an accuracy of $87. 8\%$ using RoBERTa-Large and $83. 5\%$ using RoBERTa-Base with a privacy budget of $\epsilon = 6. 7$.
Federated Survival Analysis with Discrete-Time Cox Models
Building machine learning models from decentralized datasets located in different centers with federated learning (FL) is a promising approach to circumvent local data scarcity while preserving privacy.
Efficient Per-Example Gradient Computations in Convolutional Neural Networks
One notable application comes from the field of differential privacy, where per-example gradients must be norm-bounded in order to limit the impact of each example on the aggregated batch gradient.
Approximate message-passing for convex optimization with non-separable penalties
We introduce an iterative optimization scheme for convex objectives consisting of a linear loss and a non-separable penalty, based on the expectation-consistent approximation and the vector approximate message-passing (VAMP) algorithm.
Entropy and mutual information in models of deep neural networks
We examine a class of deep learning models with a tractable method to compute information-theoretic quantities.
Streaming Bayesian inference: theoretical limits and mini-batch approximate message-passing
In statistical learning for real-world large-scale data problems, one must often resort to "streaming" algorithms which operate sequentially on small batches of data.
A Deterministic and Generalized Framework for Unsupervised Learning with Restricted Boltzmann Machines
Restricted Boltzmann machines (RBMs) are energy-based neural-networks which are commonly used as the building blocks for deep architectures neural architectures.
Multi-Layer Generalized Linear Estimation
We consider the problem of reconstructing a signal from multi-layered (possibly) non-linear measurements.
Inferring Sparsity: Compressed Sensing using Generalized Restricted Boltzmann Machines
In this work, we consider compressed sensing reconstruction from $M$ measurements of $K$-sparse structured signals which do not possess a writable correlation model.
Expectation Propagation
Variational inference is a powerful concept that underlies many iterative approximation algorithms; expectation propagation, mean-field methods and belief propagations were all central themes at the school that can be perceived from this unifying framework.
Sparse Estimation with the Swept Approximated Message-Passing Algorithm
Approximate Message Passing (AMP) has been shown to be a superior method for inference problems, such as the recovery of signals from sets of noisy, lower-dimensionality measurements, both in terms of reconstruction accuracy and in computational efficiency.
