wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In this paper, we show that these two goals of ensemble learning, namely improving accuracy and privacy , directly conflict with each other . Using a wide range of datasets and model architectures, we empirically demonstrate the trade-off between privacy and accuracy in deep ensemble learning.
Revised:  In this paper, we show that these two goals of ensemble learning, namely improving accuracy and privacy (in terms of membership inference attacks), in deep ensembles . Using a wide range of datasets and model architectures, we empirically demonstrate the trade-off between privacy and accuracy in deep ensemble learning.
Edit intent: meaning-changed
Original:  In this paper, we show that these two goals of ensemble learning, namely improving accuracy and privacy , directly conflict with each other . Using a wide range of datasets and model architectures, we empirically demonstrate the trade-off between privacy and accuracy in deep ensemble learning. We find that ensembling can improve either privacy or accuracy, but not both simultaneously -- when ensembling improves the classification accuracy, the effectiveness of the MI attack also increases.
Revised:  In this paper, we show that these two goals of ensemble learning, namely improving accuracy and privacy , directly conflict with each other . Using a wide range of datasets and model architectures, we show that the effectiveness of membership inference attacks also increases when ensembling improves the classification accuracy, the effectiveness of the MI attack also increases.
Edit intent: clarity
Original:  We find that ensembling can improve either privacy or accuracy, but not both simultaneously -- when ensembling improves the classification accuracy, the effectiveness of the MI attack also increases. We analyze various factors that contribute to such privacy leakage in ensembling such as prediction confidence and agreement between models that constitute the ensemble.
Revised:  We find that ensembling can improve either privacy or accuracy, but not both simultaneously -- when ensembling improves accuracy. To better understand this trade-off, we study the impact of various factors such as prediction confidence and agreement between models that constitute the ensemble.
Edit intent: clarity
Original:  In this paper, we introduce theoneshot mechanism , a fast, low-distortion, and differentially private primitive for the top-k problem. Compared with existing approaches in the literature, our algorithm adds Laplace noise to the counts and releases the top-k noisy counts and their estimates in a oneshot fashion, thereby substantially reducing the computational cost while maintaining satisfying utility. Our /}\eps proof of privacy for this mechanism relies on a novel coupling technique that is of independent theoretical interest .
Revised:  In this paper, we introduce theoneshot mechanism , a fast, low-distortion, and differentially private primitive for the top-k elements. We show that the oneshot Laplace mechanism with a noise level of /}\eps proof of privacy for this mechanism relies on a novel coupling technique that is of independent theoretical interest .
Edit intent: coherence
Original:  Our /}\eps proof of privacy for this mechanism relies on a novel coupling technique that is of independent theoretical interest .
Revised:  Our /}\eps) is approximately differentially private. Compared to the previous peeling approach of running Report Noisy Max k times, the oneshot Laplace mechanism only adds noises and computes the top k elements once, hence much more efficient for large k. In addition, our proof of privacy for this mechanism relies on a novel coupling technique that is of independent theoretical interest .
Edit intent: meaning-changed
Original: The increasing concerns about data privacy and security drives the emergence of a new field of studying privacy-preserving machine learning from isolated data sources, i.e., federated learning . Vertical federated learning,  where different parties hold different features for common users, has a great potential of driving a more variety of business cooperation among enterprises in different fields.
Revised: The increasing concerns about data privacy and security drive an emerging field of studying privacy-preserving machine learning from isolated data sources, i.e., federated learning . Vertical federated learning, where different parties hold different features for common users, has a great potential of driving a more variety of business cooperation among enterprises in different fields.
Edit intent: clarity
Original: The increasing concerns about data privacy and security drives the emergence of a new field of studying privacy-preserving machine learning from isolated data sources, i.e., federated learning . Vertical federated learning,  where different parties hold different features for common users, has a great potential of driving a more variety of business cooperation among enterprises in different fields.
Revised: The increasing concerns about data privacy and security drives the emergence of a new field of studying privacy-preserving machine learning from isolated data sources, i.e., . Vertical federated learning, where different parties hold different features for common users, has a great potential of driving a more variety of business cooperation among enterprises in different fields.
Edit intent: fluency
Original: The increasing concerns about data privacy and security drives the emergence of a new field of studying privacy-preserving machine learning from isolated data sources, i.e., federated learning . Vertical federated learning,  where different parties hold different features for common users, has a great potential of driving a more variety of business cooperation among enterprises in different fields.
Revised: The increasing concerns about data privacy and security drives the emergence of a new field of studying privacy-preserving machine learning from isolated data sources, i.e., federated learning federated learning. A class of federated learning, where different parties hold different features for common users, has a great potential of driving a more variety of business cooperation among enterprises in different fields.
Edit intent: meaning-changed
Original: The increasing concerns about data privacy and security drives the emergence of a new field of studying privacy-preserving machine learning from isolated data sources, i.e., federated learning . Vertical federated learning,  where different parties hold different features for common users, has a great potential of driving a more variety of business cooperation among enterprises in different fields.
Revised: The increasing concerns about data privacy and security drives the emergence of a new field of studying privacy-preserving machine learning from isolated data sources, i.e., federated learning . Vertical federated learning, vertical federated learning, where different parties hold different features for common users, has a great potential of driving a more variety of business cooperation among enterprises in different fields.
Edit intent: meaning-changed
Original: The increasing concerns about data privacy and security drives the emergence of a new field of studying privacy-preserving machine learning from isolated data sources, i.e., federated learning . Vertical federated learning,  where different parties hold different features for common users, has a great potential of driving a more variety of business cooperation among enterprises in different fields. Decision tree models especially decision tree ensembles are a class of widely applied powerful machine learning models with high interpretability and modeling efficiency.
Revised: The increasing concerns about data privacy and security drives the emergence of a new field of studying privacy-preserving machine learning from isolated data sources, i.e., federated learning . Vertical federated learning, where different parties hold different features for common users, has a great potential of driving a more variety of business cooperation among enterprises in many fields. In machine learning, decision tree ensembles are a class of widely applied powerful machine learning models with high interpretability and modeling efficiency.
Edit intent: clarity
Original:  However, the interpretability are compromised in these works such as SecureBoost since the feature names are not exposed to avoid possible data breaches due to the unprotected decision path.
Revised:  However, the interpretability is compromised in state-of-the-art vertical federated learning frameworks such as SecureBoost since the feature names are not exposed to avoid possible data breaches due to the unprotected decision path.
Edit intent: meaning-changed
Original:  However, the interpretability are compromised in these works such as SecureBoost since the feature names are not exposed to avoid possible data breaches due to the unprotected decision path. In this paper, we shall propose Fed-EINI , an efficient and interpretable inference framework for federated decision tree models with only one round of multi-party communication.
Revised:  However, the interpretability are compromised in these works such as SecureBoost since the feature names are not exposed to avoid possible data breaches . To address this issue in the inference process, in this paper, we shall propose Fed-EINI , an efficient and interpretable inference framework for federated decision tree models with only one round of multi-party communication.
Edit intent: clarity
Original:  In this paper, we shall propose Fed-EINI , an efficient and interpretable inference framework for federated decision tree models with only one round of multi-party communication. We shall compute the candidate sets of leaf nodes based on the local data at each party in parallel, followed by securely computing the weight of the only leaf node in the intersection of the candidate sets. We propose to protect the decision path by the efficient additively homomorphic encryption method, which allows the disclosure of feature names and thus makes the federated decision trees interpretable .
Revised:  In this paper, we shall propose Fed-EINI to protect data privacy and allow the disclosure of feature names and thus makes the federated decision trees interpretable .
Edit intent: clarity
Original: Deep learning based approaches have proven promising to model omicsdata. However, one of the current limitations compared to statistical and traditional machine learning approaches is the lack of explainability, which not only reduces the reliability, but limits the potential for acquiring novel knowledge from unpicking the " black-box "  models.
Revised: The lack of explainability is one of the most prominent disadvantages of deep learning applications in omics. This " black-box " models.
Edit intent: clarity
Original:  However, one of the current limitations compared to statistical and traditional machine learning approaches is the lack of explainability, which not only reduces the reliability, but limits the potential for acquiring novel knowledge from unpicking the " black-box "  models.
Revised:  However, one of the current limitations compared to statistical and traditional machine learning approaches is the lack of explainability, which not only reduces the reliability, but limits the potential for acquiring novel knowledge from unpicking the " black box " models.
Edit intent: fluency
Original:  However, one of the current limitations compared to statistical and traditional machine learning approaches is the lack of explainability, which not only reduces the reliability, but limits the potential for acquiring novel knowledge from unpicking the " black-box "  models.
Revised:  However, one of the current limitations compared to statistical and traditional machine learning approaches is the lack of explainability, which not only reduces the reliability, but limits the potential for acquiring novel knowledge from unpicking the " black-box " problem can undermine the credibility and limit the practical implementation of biomedical deep learning models.
Edit intent: meaning-changed
Original:  To the best of our knowledge, XOmiVAE is one of the first activated-based deep learning interpretation method to explain novel clusters generated by variational autoencoders. The results generated by XOmiVAE were validated by both the biomedical knowledge and the performance of downstream tasks . XOmiVAE explanations of deep learning based cancer classification and clustering aligned with current domain knowledge including biological annotation and  literature, which shows great potential for novel biomedical knowledge discovery from deep learning models .
Revised:  To the best of our knowledge, XOmiVAE is one of the first activated-based deep learning interpretation method to explain novel clusters generated by VAE. The explainable results generated by XOmiVAE were validated by both the biomedical knowledge and the performance of downstream tasks . XOmiVAE explanations of deep learning based cancer classification and clustering aligned with current domain knowledge including biological annotation and literature, which shows great potential for novel biomedical knowledge discovery from deep learning models .
Edit intent: clarity
Original:  The results generated by XOmiVAE were validated by both the biomedical knowledge and the performance of downstream tasks . XOmiVAE explanations of deep learning based cancer classification and clustering aligned with current domain knowledge including biological annotation and  literature, which shows great potential for novel biomedical knowledge discovery from deep learning models .
Revised:  The results generated by XOmiVAE were validated by both the performance of downstream tasks . XOmiVAE explanations of deep learning based cancer classification and clustering aligned with current domain knowledge including biological annotation and literature, which shows great potential for novel biomedical knowledge discovery from deep learning models .
Edit intent: clarity
Original:  The results generated by XOmiVAE were validated by both the biomedical knowledge and the performance of downstream tasks . XOmiVAE explanations of deep learning based cancer classification and clustering aligned with current domain knowledge including biological annotation and  literature, which shows great potential for novel biomedical knowledge discovery from deep learning models .
Revised:  The results generated by XOmiVAE were validated by both the biomedical knowledge and the performance of downstream tasks and the biomedical knowledge. In our experiments, XOmiVAE explanations of deep learning based cancer classification and clustering aligned with current domain knowledge including biological annotation and literature, which shows great potential for novel biomedical knowledge discovery from deep learning models .
Edit intent: coherence
Original:  The results generated by XOmiVAE were validated by both the biomedical knowledge and the performance of downstream tasks . XOmiVAE explanations of deep learning based cancer classification and clustering aligned with current domain knowledge including biological annotation and  literature, which shows great potential for novel biomedical knowledge discovery from deep learning models .
Revised:  The results generated by XOmiVAE were validated by both the biomedical knowledge and the performance of downstream tasks . XOmiVAE explanations of deep learning based cancer classification and clustering aligned with current domain knowledge including biological annotation and academic literature, which shows great potential for novel biomedical knowledge discovery from deep learning models .
Edit intent: clarity
Original:  The results generated by XOmiVAE were validated by both the biomedical knowledge and the performance of downstream tasks . XOmiVAE explanations of deep learning based cancer classification and clustering aligned with current domain knowledge including biological annotation and  literature, which shows great potential for novel biomedical knowledge discovery from deep learning models . The top XOmiVAE selected genes and dimensions shown significant influence to the performance of cancer classification. Additionally, we offer important steps to consider when interpreting deep learning models for tumour classification. For instance, we demonstrate the importance of choosing background samples that makes biological sense and the limitations of connection weight based methods to explain latent dimensions .
Revised:  The results generated by XOmiVAE were validated by both the biomedical knowledge and the performance of downstream tasks . XOmiVAE explanations of deep learning based cancer classification and clustering aligned with current domain knowledge including biological annotation and literature, which shows great potential for novel biomedical knowledge discovery from deep learning models .
Edit intent: coherence
Original:  In this work, we first deploy a prototype  recommender system on Taobao display advertising platform for constant bid and crowd optimization. Then, we propose a novel recommender system for dynamic bidding strategy recommendation, which models the advertiser' s strategy recommendation problem as a contextual bandit problem .
Revised:  In this work, we first deploy a prototype recommender system on Taobao display advertising platform , recommending bid prices and targeted users to advertisers. We further augment this prototype system by directly revealing the advertising performance, and then infer the advertisers' marketing objectives through their adoptions of different recommending advertising performance .
Edit intent: meaning-changed
Original:  We use a neural network as the agent to predict the  advertisers' demands based on their profile and historical adoption behaviors. Based on the estimated demand, we apply simulated bidding to derive the optimal bidding strategy for recommendation and interact with the advertiser by displaying the possible advertising performance. To solve the exploration/exploitation dilemma, we use Dropout to represent the uncertainty of the network, which approximately equals to conduct Thompson sampling for efficient strategy exploration.
Revised:  We use a neural network as the agent to predict the advertisers' marketing objectives and the recommending strategies.
Edit intent: clarity
Original:  Online evaluations show that the  system can optimize the advertisers' advertising performance , and advertisers are willing to open the system, select and adopt the suggestions, which further increases the platform's revenue income . Simulation experiments based on Alibaba online bidding data prove that the agent can effectively optimize the  adoption rate of advertisers , and Thompson sampling can better balance exploration and exploitation to further optimize the performance of the model .
Revised:  Online evaluations show that the designed advertising strategy recommender system can optimize the advertisers' advertising performance , and advertisers are willing to open the system, select and adopt the suggestions, which further increases the platform's revenue income . Simulation experiments based on Alibaba online bidding data prove that the agent can effectively optimize the adoption rate of advertisers , and Thompson sampling can better balance exploration and exploitation to further optimize the performance of the model .
Edit intent: meaning-changed
Original:  Online evaluations show that the  system can optimize the advertisers' advertising performance , and advertisers are willing to open the system, select and adopt the suggestions, which further increases the platform's revenue income . Simulation experiments based on Alibaba online bidding data prove that the agent can effectively optimize the  adoption rate of advertisers , and Thompson sampling can better balance exploration and exploitation to further optimize the performance of the model .
Revised:  Online evaluations show that the system can optimize the advertisers' advertising performance and increase the platform's revenue income . Simulation experiments based on Alibaba online bidding data prove that the agent can effectively optimize the adoption rate of advertisers , and Thompson sampling can better balance exploration and exploitation to further optimize the performance of the model .
Edit intent: clarity
Original:  Online evaluations show that the  system can optimize the advertisers' advertising performance , and advertisers are willing to open the system, select and adopt the suggestions, which further increases the platform's revenue income . Simulation experiments based on Alibaba online bidding data prove that the agent can effectively optimize the  adoption rate of advertisers , and Thompson sampling can better balance exploration and exploitation to further optimize the performance of the model .
Revised:  Online evaluations show that the system can optimize the advertisers' advertising performance , and advertisers are willing to open the system, select and adopt the suggestions, which further increases the platform's revenue . Simulation experiments based on Alibaba online bidding data prove that the agent can effectively optimize the adoption rate of advertisers , and Thompson sampling can better balance exploration and exploitation to further optimize the performance of the model .
Edit intent: clarity
Original:  Online evaluations show that the  system can optimize the advertisers' advertising performance , and advertisers are willing to open the system, select and adopt the suggestions, which further increases the platform's revenue income . Simulation experiments based on Alibaba online bidding data prove that the agent can effectively optimize the  adoption rate of advertisers , and Thompson sampling can better balance exploration and exploitation to further optimize the performance of the model .
Revised:  Online evaluations show that the system can optimize the advertisers' advertising performance , and advertisers are willing to open the system, select and adopt the suggestions, which further increases the platform's revenue income . Simulation experiments based on Taobao online bidding data prove that the agent can effectively optimize the adoption rate of advertisers , and Thompson sampling can better balance exploration and exploitation to further optimize the performance of the model .
Edit intent: meaning-changed
Original:  Online evaluations show that the  system can optimize the advertisers' advertising performance , and advertisers are willing to open the system, select and adopt the suggestions, which further increases the platform's revenue income . Simulation experiments based on Alibaba online bidding data prove that the agent can effectively optimize the  adoption rate of advertisers , and Thompson sampling can better balance exploration and exploitation to further optimize the performance of the model .
Revised:  Online evaluations show that the system can optimize the advertisers' advertising performance , and advertisers are willing to open the system, select and adopt the suggestions, which further increases the platform's revenue income . Simulation experiments based on Alibaba online bidding data show that the designed contextual bandit algorithm can effectively optimize the adoption rate of advertisers , and Thompson sampling can better balance exploration and exploitation to further optimize the performance of the model .
Edit intent: meaning-changed