Wednesday, June 30, 2021

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2021-07-01 / 11:00 ~ 12:00
IBS-KAIST 세미나 - 수리생물학: Statistical Inference with Neural Network Imputation for Item Nonresponse 인쇄
by 김재광(Iowa State University)
Abstract: We consider the problem of nonparametric imputation using neural network models. Neural network models can capture complex nonlinear trends and interaction effects, making it a powerful tool for predicting missing values under minimum assumptions on the missingness mechanism. Statistical inference with neural network imputation, including variance estimation, is challenging because the basis for function estimation is estimated rather than known. In this paper, we tackle the problem of statistical inference with neural network imputation by treating the hidden nodes in a neural network as data-driven basis functions. We prove that the uncertainty in estimating the basis functions can be safely ignored and hence the linearization method for neural network imputation can be greatly simplified. A simulation study confirms that the proposed approach results in efficient and well-calibrated confidence intervals even when classic approaches fail due to severe nonlinearity and complicated interactions.
2021-07-05 / 14:00 ~ 15:00
학과 세미나/콜로퀴엄 - 대수기하학: Holomorphic anomaly equation in Gromov-Witten theory 인쇄
by 노현호(충남대학교)
Gromov-Witten invariants are some rational numbers roughly counting curves inside a Calabi-Yau manifold. These numbers have some recurvsive structure on the genus of the curve. I will explain how to study this recursive structure throughholomorphic anomaly equation. For a semi-simple Gromov-Witten theory, I will explain the method of proof of holomorphic anomaly equation for several examples using Givental-Teleman's classification thoerem.If I have more time, I will discuss how to generalize this method to a non-semi simple Gromov-Witten theory.
Events for the 취소된 행사 포함 모두인쇄
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