1,088 research outputs found
Partial Consistency with Sparse Incidental Parameters
Penalized estimation principle is fundamental to high-dimensional problems.
In the literature, it has been extensively and successfully applied to various
models with only structural parameters. As a contrast, in this paper, we apply
this penalization principle to a linear regression model with a
finite-dimensional vector of structural parameters and a high-dimensional
vector of sparse incidental parameters. For the estimators of the structural
parameters, we derive their consistency and asymptotic normality, which reveals
an oracle property. However, the penalized estimators for the incidental
parameters possess only partial selection consistency but not consistency. This
is an interesting partial consistency phenomenon: the structural parameters are
consistently estimated while the incidental ones cannot. For the structural
parameters, also considered is an alternative two-step penalized estimator,
which has fewer possible asymptotic distributions and thus is more suitable for
statistical inferences. We further extend the methods and results to the case
where the dimension of the structural parameter vector diverges with but slower
than the sample size. A data-driven approach for selecting a penalty
regularization parameter is provided. The finite-sample performance of the
penalized estimators for the structural parameters is evaluated by simulations
and a real data set is analyzed
Selective Amnesia: On Efficient, High-Fidelity and Blind Suppression of Backdoor Effects in Trojaned Machine Learning Models
In this paper, we present a simple yet surprisingly effective technique to
induce "selective amnesia" on a backdoored model. Our approach, called SEAM,
has been inspired by the problem of catastrophic forgetting (CF), a long
standing issue in continual learning. Our idea is to retrain a given DNN model
on randomly labeled clean data, to induce a CF on the model, leading to a
sudden forget on both primary and backdoor tasks; then we recover the primary
task by retraining the randomized model on correctly labeled clean data. We
analyzed SEAM by modeling the unlearning process as continual learning and
further approximating a DNN using Neural Tangent Kernel for measuring CF. Our
analysis shows that our random-labeling approach actually maximizes the CF on
an unknown backdoor in the absence of triggered inputs, and also preserves some
feature extraction in the network to enable a fast revival of the primary task.
We further evaluated SEAM on both image processing and Natural Language
Processing tasks, under both data contamination and training manipulation
attacks, over thousands of models either trained on popular image datasets or
provided by the TrojAI competition. Our experiments show that SEAM vastly
outperforms the state-of-the-art unlearning techniques, achieving a high
Fidelity (measuring the gap between the accuracy of the primary task and that
of the backdoor) within a few minutes (about 30 times faster than training a
model from scratch using the MNIST dataset), with only a small amount of clean
data (0.1% of training data for TrojAI models)
An Empirical Study of Agricultural Insurance—Evidence from China
AbstractThis paper explores the factors that affect the farmers buying or not buying agricultural insurance so that the provider of insurance, state-owned agricultural insurance companies or commercial ones can adjust their strategic to suit the demand of famers based on our results and China's special characteristics in rural area, such as huge rural population and stated-owned land system. In this paper, we also provide some suggestion on how to develop the agricultural insurance in China for policy maker
The fabrication of 3-D photonic band gap structures
Thesis (Elec. E.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.Includes bibliographical references (leaves 85-88).by Xiaofeng Tang.Elec.E
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