Search Results for author: Jiaye Teng

Found 11 papers, 1 papers with code

Fighting Fire with Fire: Avoiding DNN Shortcuts through Priming

no code implementations22 Jun 2022 Chuan Wen, Jianing Qian, Jierui Lin, Jiaye Teng, Dinesh Jayaraman, Yang Gao

Across applications spanning supervised classification and sequential control, deep learning has been reported to find "shortcut" solutions that fail catastrophically under minor changes in the data distribution.

Autonomous Driving Classification +4

Anomaly Detection with Test Time Augmentation and Consistency Evaluation

no code implementations6 Jun 2022 Haowei He, Jiaye Teng, Yang Yuan

Deep neural networks are known to be vulnerable to unseen data: they may wrongly assign high confidence stcores to out-distribuion samples.

Anomaly Detection Representation Learning

Realistic Deep Learning May Not Fit Benignly

no code implementations1 Jun 2022 Kaiyue Wen, Jiaye Teng, Jingzhao Zhang

Studies on benign overfitting provide insights for the success of overparameterized deep learning models.

When do Models Generalize? A Perspective from Data-Algorithm Compatibility

no code implementations12 Feb 2022 Jing Xu, Jiaye Teng, Yang Yuan, Andrew Chi-Chih Yao

By considering the entire training trajectory and focusing on early-stopping iterates, compatibility exploits the data and the algorithm information and is therefore a more suitable notion for generalization.

Generalization Bounds Learning Theory

T-SCI: A Two-Stage Conformal Inference Algorithm with Guaranteed Coverage for Cox-MLP

1 code implementation8 Mar 2021 Jiaye Teng, Zeren Tan, Yang Yuan

It is challenging to deal with censored data, where we only have access to the incomplete information of survival time instead of its exact value.

Can Pretext-Based Self-Supervised Learning Be Boosted by Downstream Data? A Theoretical Analysis

no code implementations5 Mar 2021 Jiaye Teng, Weiran Huang, Haowei He

Pretext-based self-supervised learning learns the semantic representation via a handcrafted pretext task over unlabeled data and then uses the learned representation for downstream tasks, which effectively reduces the sample complexity of downstream tasks under Conditional Independence (CI) condition.

Self-Supervised Learning

Inject Machine Learning into Significance Test for Misspecified Linear Models

no code implementations4 Jun 2020 Jiaye Teng, Yang Yuan

First, we apply a machine learning method to fit the ground truth function on the training set and calculate its linear approximation.

BIG-bench Machine Learning regression

$\ell_1$ Adversarial Robustness Certificates: a Randomized Smoothing Approach

no code implementations25 Sep 2019 Jiaye Teng, Guang-He Lee, Yang Yuan

Robustness is an important property to guarantee the security of machine learning models.

Adversarial Robustness

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