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Abstract We present ultraviolet/optical/near-infrared observations and modeling of Type II supernovae (SNe II) whose early time (δt< 2 days) spectra show transient, narrow emission lines from shock ionization of confined (r< 10¹⁵cm) circumstellar material (CSM). The observed electron-scattering broadened line profiles (i.e., IIn-like) of Hi, Hei/ii, Civ, and Niii/iv/vfrom the CSM persist on a characteristic timescale (t_IIn) that marks a transition to a lower-density CSM and the emergence of Doppler-broadened features from the fast-moving SN ejecta. Our sample, the largest to date, consists of 39 SNe with early time IIn-like features in addition to 35 “comparison” SNe with no evidence of early time IIn-like features, all with ultraviolet observations. The total sample includes 50 unpublished objects with a total of 474 previously unpublished spectra and 50 multiband light curves, collected primarily through the Young Supernova Experiment and Global Supernova Project collaborations. For all sample objects, we find a significant correlation between peak ultraviolet brightness and botht_IInand the rise time, as well as evidence for enhanced peak luminosities in SNe II with IIn-like features. We quantify mass-loss rates and CSM density for the sample through the matching of peak multiband absolute magnitudes, rise times,t_IIn, and optical SN spectra with a grid of radiation hydrodynamics and non-local thermodynamic equilibrium radiative-transfer simulations. For our grid of models, all with the same underlying explosion, there is a trend between the duration of the electron-scattering broadened line profiles and inferred mass-loss rate: $t_{\mathrm{IIn}}\approx 3.8[\dot{M}/$(0.01M_⊙yr⁻¹)] days. 

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 user 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. 

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 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 recent advancements in the active learning domain can be used to implement powerful model extraction attacks, and investigate possible defense strategies. 

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. This 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.