Search Results for author: Tianyang Li

Found 8 papers, 1 papers with code

Learning Deep Implicit Functions for 3D Shapes with Dynamic Code Clouds

1 code implementation26 Mar 2022 Tianyang Li, Xin Wen, Yu-Shen Liu, Hua Su, Zhizhong Han

However, the local codes are constrained at discrete and regular positions like grid points, which makes the code positions difficult to be optimized and limits their representation ability.

3D Shape Representation

Point Cloud Completion by Skip-attention Network with Hierarchical Folding

no code implementations CVPR 2020 Xin Wen, Tianyang Li, Zhizhong Han, Yu-Shen Liu

Point cloud completion aims to infer the complete geometries for missing regions of 3D objects from incomplete ones.

Point Cloud Completion

Robust Screening of COVID-19 from Chest X-ray via Discriminative Cost-Sensitive Learning

no code implementations27 Apr 2020 Tianyang Li, Zhongyi Han, Benzheng Wei, Yuanjie Zheng, Yanfei Hong, Jinyu Cong

However, robust and accurate screening of COVID-19 from chest X-rays is still a globally recognized challenge because of two bottlenecks: 1) imaging features of COVID-19 share some similarities with other pneumonia on chest X-rays, and 2) the misdiagnosis rate of COVID-19 is very high, and the misdiagnosis cost is expensive.

High Dimensional Robust $M$-Estimation: Arbitrary Corruption and Heavy Tails

no code implementations24 Jan 2019 Liu Liu, Tianyang Li, Constantine Caramanis

We define a natural condition we call the Robust Descent Condition (RDC), and show that if a gradient estimator satisfies the RDC, then Robust Hard Thresholding (IHT using this gradient estimator), is guaranteed to obtain good statistical rates.

High Dimensional Robust Sparse Regression

no code implementations29 May 2018 Liu Liu, Yanyao Shen, Tianyang Li, Constantine Caramanis

Our algorithm recovers the true sparse parameters with sub-linear sample complexity, in the presence of a constant fraction of arbitrary corruptions.

Approximate Newton-based statistical inference using only stochastic gradients

no code implementations23 May 2018 Tianyang Li, Anastasios Kyrillidis, Liu Liu, Constantine Caramanis

We present a novel statistical inference framework for convex empirical risk minimization, using approximate stochastic Newton steps.

Time Series Time Series Analysis

Statistical inference using SGD

no code implementations21 May 2017 Tianyang Li, Liu Liu, Anastasios Kyrillidis, Constantine Caramanis

We present a novel method for frequentist statistical inference in $M$-estimation problems, based on stochastic gradient descent (SGD) with a fixed step size: we demonstrate that the average of such SGD sequences can be used for statistical inference, after proper scaling.

Fast Classification Rates for High-dimensional Gaussian Generative Models

no code implementations NeurIPS 2015 Tianyang Li, Adarsh Prasad, Pradeep K. Ravikumar

We consider the problem of binary classification when the covariates conditioned on the each of the response values follow multivariate Gaussian distributions.

Classification General Classification

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