9 citations · 25 across the 12 of their papers we have counts for
13 papers
Tight Transition Time Bounds for Separable Logistic Regression at the Edge of Stability
Haodong Wen, Kaiyue Wen, Jiaye Teng
We study logistic regression on linearly separable data under gradient descent with a large constant stepsize . Such dynamics may exhibit a characteristic Edge of Stability phen…
Predictive Inference With Fast Feature Conformal Prediction
Zihao Tang, Boyuan Wang, Chuan Wen +1
Conformal prediction is widely adopted in uncertainty quantification, due to its post-hoc, distribution-free, and model-agnostic properties. In the realm of modern deep learning, r…
On Uni-Modal Feature Learning in Supervised Multi-Modal Learning
Chenzhuang Du, Jiaye Teng, Tingle Li +5
We abstract the features (i.e. learned representations) of multi-modal data into 1) uni-modal features, which can be learned from uni-modal training, and 2) paired features, which…
Lower Generalization Bounds for GD and SGD in Smooth Stochastic Convex Optimization
Peiyuan Zhang, Jiaye Teng, Jingzhao Zhang
This work studies the generalization error of gradient methods. More specifically, we focus on how training steps and step-size might affect generalization in smooth stocha…
Predictive Inference with Feature Conformal Prediction
Jiaye Teng, Chuan Wen, Dinghuai Zhang +3
Conformal prediction is a distribution-free technique for establishing valid prediction intervals. Although conventionally people conduct conformal prediction in the output space,…
Fighting Fire with Fire: Avoiding DNN Shortcuts through Priming
Chuan Wen, Jianing Qian, Jierui Lin +3
Across applications spanning supervised classification and sequential control, deep learning has been reported to find "shortcut" solutions that fail catastrophically under minor c…
Anomaly Detection with Test Time Augmentation and Consistency Evaluation
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. Recent works try to solve the problem us…
Benign Overfitting in Classification: Provably Counter Label Noise with Larger Models
Kaiyue Wen, Jiaye Teng, Jingzhao Zhang
Studies on benign overfitting provide insights for the success of overparameterized deep learning models. In this work, we examine whether overfitting is truly benign in real-world…
Towards Data-Algorithm Dependent Generalization: a Case Study on Overparameterized Linear Regression
Jing Xu, Jiaye Teng, Yang Yuan +1
One of the major open problems in machine learning is to characterize generalization in the overparameterized regime, where most traditional generalization bounds become inconsiste…
Towards Understanding Generalization via Decomposing Excess Risk Dynamics
Jiaye Teng, Jianhao Ma, Yang Yuan
Generalization is one of the fundamental issues in machine learning. However, traditional techniques like uniform convergence may be unable to explain generalization under overpara…
T-SCI: A Two-Stage Conformal Inference Algorithm with Guaranteed Coverage for Cox-MLP
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. Fortunately, under linear predict…
Can Pretext-Based Self-Supervised Learning Be Boosted by Downstream Data? A Theoretical Analysis
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…
Inject Machine Learning into Significance Test for Misspecified Linear Models
Jiaye Teng, Yang Yuan
Due to its strong interpretability, linear regression is widely used in social science, from which significance test provides the significance level of models or coefficients in th…