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Creators/Authors contains: "Yan, S."

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  1. ; ; ; ; ( , ArXivorg)
    Free, publicly-accessible full text available June 16, 2023
  2. ; ; ; ; ; ; ; ; ; ( , Materials Today Chemistry)
    Free, publicly-accessible full text available June 1, 2023
  3. ; ; ; ; ; ; ; ; ; ; et al ( , ACS applied materials interfaces)
    Free, publicly-accessible full text available March 25, 2023
  4. ; ; ; ; ( , Usenxix security (submitted))
    Machine learning is being increasingly used by individuals, research institutions, and corporations. This has resulted in the surge of Machine Learning-as-a-Service (MLaaS) - cloud services that provide (a) tools and resources to learn the model, and (b) a user-friendly query interface to access the model. However, such MLaaS systems raise privacy concerns such as model extraction. In model extraction attacks, adversaries mali- ciously exploit the query interface to steal the model. More precisely, in a model extraction attack, a good approximation of a sensitive or propri- etary model held by the server is extracted (i.e. learned) by a dishonest usermore »who interacts with the server only via the query interface. This attack was introduced by Tramèr et al. at the 2016 USENIX Security Symposium, where practical attacks for various models were shown. We believe that better understanding the efficacy of model extraction attacks is paramount to designing secure MLaaS systems. To that end, we take the first step by (a) formalizing model extraction and discussing possible defense strategies, and (b) drawing parallels between model extraction and established area of active learning. In particular, we show that re- cent advancements in the active learning domain can be used to imple- ment powerful model extraction attacks, and investigate possible defense strategies.« less
    Accepted Manuscript Full Text Available
  5. ; ; ; ; ; ; ; ; ; ; et al ( , Few-Body Systems)
    Free, publicly-accessible full text available June 1, 2023
  6. ; ; ; ; ; ; ; ; ; ; et al ( , ACS Nano)
    Full Text Available 
  7. ; ; ; ; ( , Proceedings of the USENIX Conference)
    Machine learning is being increasingly used by individu- als, research institutions, and corporations. This has resulted in the surge of Machine Learning-as-a-Service (MLaaS) - cloud services that provide (a) tools and resources to learn the model, and (b) a user-friendly query interface to access the model. However, such MLaaS systems raise concerns such as model extraction. In model extraction attacks, adversaries maliciously exploit the query interface to steal the model. More precisely, in a model extraction attack, a good approxi- mation of a sensitive or proprietary model held by the server is extracted (i.e. learned) by a dishonest user whomore »interacts with the server only via the query interface. This attack was introduced by Tramèr et al. at the 2016 USENIX Security Symposium, where practical attacks for various models were shown. We believe that better understanding the efficacy of model extraction attacks is paramount to designing secure MLaaS systems. To that end, we take the first step by (a) formalizing model extraction and discussing possible defense strategies, and (b) drawing parallels between model extraction and established area of active learning. In particular, we show that recent advancements in the active learning domain can be used to implement powerful model extraction attacks, and investigate possible defense strategies.« less
    Accepted Manuscript Full Text Available
  8. ; ; ; ; ( , Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL))
    Accepted Manuscript Full Text Available
  9. ; ; ; ; ; ; ; ; ; ; et al ( , The Astrophysical Journal)
    Full Text Available 
  10. ; ; ( , Advances in neural information processing systems)
    Counterfactual learning from observational data involves learning a classifier on an entire population based on data that is observed conditioned on a selection policy. This work considers this problem in an active setting, where the learner additionally has access to unlabeled examples and can choose to get a subset of these labeled by an oracle. Prior work on this problem uses disagreement-based active learning, along with an importance weighted loss estimator to account for counterfactuals, which leads to a high label complexity. We show how to instead incorporate a more efficient counterfactual risk minimizer into the active learning algorithm. Thismore »requires us to modify both the counterfactual risk to make it amenable to active learning, as well as the active learning process to make it amenable to the risk. We provably demonstrate that the result of this is an algorithm which is statistically consistent as well as more label-efficient than prior work.« less
    Accepted Manuscript Full Text Available