research
∙
08/30/2023
Threshold KNN-Shapley: A Linear-Time and Privacy-Friendly Approach to Data Valuation
Data valuation, a critical aspect of data-centric ML research, aims to q...
share
research
∙
07/06/2023
Age-of-Information Dependent Random Access for Periodic Updating
This paper considers an uplink Internet of Things system with synchronou...
research
∙
06/12/2023
"Private Prediction Strikes Back!” Private Kernelized Nearest Neighbors with Individual Renyi Filter
Most existing approaches of differentially private (DP) machine learning...
research
∙
12/31/2022
Generalized PTR: User-Friendly Recipes for Data-Adaptive Algorithms with Differential Privacy
The ”Propose-Test-Release” (PTR) framework is a classic recipe for desig...
research
∙
03/30/2022
Adaptive Private-K-Selection with Adaptive K and Application to Multi-label PATE
We provide an end-to-end Renyi DP based-framework for differentially pri...
research
∙
06/16/2021
Optimal Accounting of Differential Privacy via Characteristic Function
Characterizing the privacy degradation over compositions, i.e., privacy ...
research
∙
01/16/2021
JITuNE: Just-In-Time Hyperparameter Tuning for Network Embedding Algorithms
Network embedding (NE) can generate succinct node representations for ma...
research
∙
12/30/2020
When Load Rebalancing Does Not Work for Distributed Hash Table
Distributed hash table (DHT) is the foundation of many widely used stora...
research
∙
11/06/2020
Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning
The Private Aggregation of Teacher Ensembles (PATE) framework is one of ...
research
∙
10/09/2020
Voting-based Approaches For Differentially Private Federated Learning
While federated learning (FL) enables distributed agents to collaborativ...
research
∙
10/12/2019
ClassyTune: A Performance Auto-Tuner for Systems in the Cloud
Performance tuning can improve the system performance and thus enable th...
research
∙
10/10/2017