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Found 22 papers, 4 papers with code
Pre-training Differentially Private Models with Limited Public Data
The superior performance of large foundation models relies on the use of massive amounts of high-quality data, which often contain sensitive, private and copyrighted material that requires formal protection.
Boundary Exploration for Bayesian Optimization With Unknown Physical Constraints
In this paper, we observe that in such scenarios optimal solution typically lies on the boundary between feasible and infeasible regions of the design space, making it considerably more difficult than that with interior optima.
Pre-trained Trojan Attacks for Visual Recognition
This paper aims to raise awareness of the potential threats associated with applying PVMs in practical scenarios.
Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach
In our work, we propose a new error-feedback (EF) DP algorithm as an alternative to DPSGD-GC, which not only offers a diminishing utility bound without inducing a constant clipping bias, but more importantly, it allows for an arbitrary choice of clipping threshold that is independent of the problem.
On semi-supervised estimation using exponential tilt mixture models
Consider a semi-supervised setting with a labeled dataset of binary responses and predictors and an unlabeled dataset with only the predictors.
A new method of modeling the multi-stage decision-making process of CRT using machine learning with uncertainty quantification
Uncertainty quantification from Ensemble 1 allowed for multi-stage decision-making to determine if the acquisition of SPECT data for a patient is necessary.
A new method using deep transfer learning on ECG to predict the response to cardiac resynchronization therapy
The model was fine-tuned to extract relevant features from the ECG images, and then tested on our dataset of CRT patients to predict their response.
A new method using deep learning to predict the response to cardiac resynchronization therapy
The DL model outperformed the ML models, showcasing the additional predictive benefit of utilizing SPECT MPI polarmaps.
Persistently Trained, Diffusion-assisted Energy-based Models
Maximum likelihood (ML) learning for energy-based models (EBMs) is challenging, partly due to non-convergence of Markov chain Monte Carlo. Several variations of ML learning have been proposed, but existing methods all fail to achieve both post-training image generation and proper density estimation.
GLASU: A Communication-Efficient Algorithm for Federated Learning with Vertically Distributed Graph Data
In this paper, we propose a model splitting method that splits a backbone GNN across the clients and the server and a communication-efficient algorithm, GLASU, to train such a model.
Enabling Deep Learning-based Physical-layer Secret Key Generation for FDD-OFDM Systems in Multi-Environments
Deep learning-based physical-layer secret key generation (PKG) has been used to overcome the imperfect uplink/downlink channel reciprocity in frequency division duplexing (FDD) orthogonal frequency division multiplexing (OFDM) systems.
Grow and Merge: A Unified Framework for Continuous Categories Discovery
Although a number of studies are devoted to novel category discovery, most of them assume a static setting where both labeled and unlabeled data are given at once for finding new categories.
Automatic Context Pattern Generation for Entity Set Expansion
In addition, we propose the GAPA, a novel ESE framework that leverages the aforementioned GenerAted PAtterns to expand target entities.
Understanding A Class of Decentralized and Federated Optimization Algorithms: A Multi-Rate Feedback Control Perspective
Distributed algorithms have been playing an increasingly important role in many applications such as machine learning, signal processing, and control.
Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy
Recently, there has been a line of work on incorporating the formal privacy notion of differential privacy with FL.
A method using deep learning to discover new predictors of CRT response from mechanical dyssynchrony on gated SPECT MPI
Correlation analysis was used to explain the relationships between new parameters with conventional LVMD parameters.
Hybrid Federated Learning: Algorithms and Implementation
To the best of our knowledge, this is the first formulation and algorithm developed for the hybrid FL.
Privacy-Preserving Technology to Help Millions of People: Federated Prediction Model for Stroke Prevention
It adopts federated averaging during the model training process, without patient data being taken out of the hospitals during the whole process of model training and forecasting.
FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Data
Aiming at designing FL algorithms that are provably fast and require as few assumptions as possible, we propose a new algorithm design strategy from the primal-dual optimization perspective.
Distributed Learning in the Non-Convex World: From Batch to Streaming Data, and Beyond
In particular, we {provide a selective review} about the recent techniques developed for optimizing non-convex models (i. e., problem classes), processing batch and streaming data (i. e., data types), over the networks in a distributed manner (i. e., communication and computation paradigm).
A Communication Efficient Collaborative Learning Framework for Distributed Features
We introduce a collaborative learning framework allowing multiple parties having different sets of attributes about the same user to jointly build models without exposing their raw data or model parameters.
Semi-supervised Logistic Learning Based on Exponential Tilt Mixture Models
Consider semi-supervised learning for classification, where both labeled and unlabeled data are available for training.
