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Found 14 papers, 4 papers with code
Sub-Adjacent Transformer: Improving Time Series Anomaly Detection with Reconstruction Error from Sub-Adjacent Neighborhoods
By focusing the attention on the sub-adjacent areas, we make the reconstruction of anomalies more challenging, thereby enhancing their detectability.
Threshold KNN-Shapley: A Linear-Time and Privacy-Friendly Approach to Data Valuation
Data valuation aims to quantify the usefulness of individual data sources in training machine learning (ML) models, and is a critical aspect of data-centric ML research.
"Private Prediction Strikes Back!'' Private Kernelized Nearest Neighbors with Individual Renyi Filter
Most existing approaches of differentially private (DP) machine learning focus on private training.
Generalized PTR: User-Friendly Recipes for Data-Adaptive Algorithms with Differential Privacy
The ''Propose-Test-Release'' (PTR) framework is a classic recipe for designing differentially private (DP) algorithms that are data-adaptive, i. e. those that add less noise when the input dataset is nice.
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 private top-$k$ selection.
Optimal Accounting of Differential Privacy via Characteristic Function
Characterizing the privacy degradation over compositions, i. e., privacy accounting, is a fundamental topic in differential privacy (DP) with many applications to differentially private machine learning and federated learning.
JITuNE: Just-In-Time Hyperparameter Tuning for Network Embedding Algorithms
Although AutoML methods have been applied to the hyperparameter tuning of NE algorithms, the problem of how to tune hyperparameters in a given period of time is not studied for NE algorithms before.
Improving Sparse Vector Technique with Renyi Differential Privacy
The Sparse Vector Technique (SVT) is one of the most fundamental algorithmic tools in differential privacy (DP).
Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning
The Private Aggregation of Teacher Ensembles (PATE) framework is one of the most promising recent approaches in differentially private learning.
Voting-based Approaches For Differentially Private Federated Learning
Differentially Private Federated Learning (DPFL) is an emerging field with many applications.
Private-kNN: Practical Differential Privacy for Computer Vision
With increasing ethical and legal concerns on privacy for deep models in visual recognition, differential privacy has emerged as a mechanism to disguise membership of sensitive data in training datasets.
ClassyTune: A Performance Auto-Tuner for Systems in the Cloud
Performance tuning can improve the system performance and thus enable the reduction of cloud computing resources needed to support an application.
BestConfig: Tapping the Performance Potential of Systems via Automatic Configuration Tuning
To help users tap the performance potential of systems, we present BestConfig, a system for automatically finding a best configuration setting within a resource limit for a deployed system under a given application workload.
Performance Databases Distributed, Parallel, and Cluster Computing Software Engineering
ACTS in Need: Automatic Configuration Tuning with Scalability Guarantees
To support the variety of Big Data use cases, many Big Data related systems expose a large number of user-specifiable configuration parameters.
Distributed, Parallel, and Cluster Computing
