Search Results for author: Chao Yan

Found 14 papers, 2 papers with code

Private Everlasting Prediction

no code implementations16 May 2023 Moni Naor, Kobbi Nissim, Uri Stemmer, Chao Yan

We observe that when answering a stream of queries, a predictor must modify the hypothesis it uses over time, and, furthermore, that it must use the queries for this modification, hence introducing potential privacy risks with respect to the queries themselves.

Distillation to Enhance the Portability of Risk Models Across Institutions with Large Patient Claims Database

no code implementations6 Jul 2022 Steve Nyemba, Chao Yan, Ziqi Zhang, Amol Rajmane, Pablo Meyer, Prithwish Chakraborty, Bradley Malin

We further show that the transfer learning approach based on the BAN produces models that are better than those trained on just a single institution's data.

Readmission Prediction Transfer Learning

Fast Wireless Sensor Anomaly Detection based on Data Stream in Edge Computing Enabled Smart Greenhouse

no code implementations28 Jul 2021 Yihong Yang, Sheng Ding, YuWen Liu, Shunmei Meng, Xiaoxiao Chi, Rui Ma, Chao Yan

However, traditional anomaly detection algorithms originally designed for anomaly detection in static data have not properly considered the inherent characteristics of data stream produced by wireless sensor such as infiniteness, correlations and concept drift, which may pose a considerable challenge on anomaly detection based on data stream, and lead to low detection accuracy and efficiency.

Anomaly Detection Decision Making +1

Two-stage Robust Energy Storage Planning with Probabilistic Guarantees: A Data-driven Approach

no code implementations30 Mar 2021 Chao Yan, Xinbo Geng, Zhaohong Bie, Le Xie

Furthermore, the column-and-constraint generation algorithm is used to solve the two-stage robust planning problem and tighten theoretical guarantees.

Decision Making

The Sample Complexity of Distribution-Free Parity Learning in the Robust Shuffle Model

no code implementations29 Mar 2021 Kobbi Nissim, Chao Yan

We provide a lowerbound on the sample complexity of distribution-free parity learning in the realizable case in the shuffle model of differential privacy.

BART based semantic correction for Mandarin automatic speech recognition system

no code implementations26 Mar 2021 Yun Zhao, Xuerui Yang, Jinchao Wang, Yongyu Gao, Chao Yan, Yuanfu Zhou

Although automatic speech recognition (ASR) systems achieved significantly improvements in recent years, spoken language recognition error occurs which can be easily spotted by human beings.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Flocking and Collision Avoidance for a Dynamic Squad of Fixed-Wing UAVs Using Deep Reinforcement Learning

no code implementations20 Jan 2021 Chao Yan, Xiaojia Xiang, Chang Wang, Zhen Lan

Developing the flocking behavior for a dynamic squad of fixed-wing UAVs is still a challenge due to kinematic complexity and environmental uncertainty.

Decision Making reinforcement-learning +1

Generating Electronic Health Records with Multiple Data Types and Constraints

no code implementations17 Mar 2020 Chao Yan, Ziqi Zhang, Steve Nyemba, Bradley A. Malin

Sharing electronic health records (EHRs) on a large scale may lead to privacy intrusions.

Finding Needles in a Moving Haystack: Prioritizing Alerts with Adversarial Reinforcement Learning

no code implementations20 Jun 2019 Liang Tong, Aron Laszka, Chao Yan, Ning Zhang, Yevgeniy Vorobeychik

We then use these in a double-oracle framework to obtain an approximate equilibrium of the game, which in turn yields a robust stochastic policy for the defender.

Intrusion Detection reinforcement-learning +1

Get Your Workload in Order: Game Theoretic Prioritization of Database Auditing

no code implementations22 Jan 2018 Chao Yan, Bo Li, Yevgeniy Vorobeychik, Aron Laszka, Daniel Fabbri, Bradley Malin

For enhancing the privacy protections of databases, where the increasing amount of detailed personal data is stored and processed, multiple mechanisms have been developed, such as audit logging and alert triggers, which notify administrators about suspicious activities; however, the two main limitations in common are: 1) the volume of such alerts is often substantially greater than the capabilities of resource-constrained organizations, and 2) strategic attackers may disguise their actions or carefully choosing which records they touch, making incompetent the statistical detection models.

Cannot find the paper you are looking for? You can Submit a new open access paper.