Publications (15)
The FM Agent
Annan Li, Chufan Wu, Zengle Ge +19
Large language models (LLMs) are catalyzing the development of autonomous AI research agents for scientific and engineering discovery. We present FM Agent, a novel and general-purp…
HONES: A Fast and Tuning-free Homotopy Method For Online Newton Step
Yuting Ye, Lihua Lei, Cheng Ju
In this article, we develop and analyze a homotopy continuation method, referred to as HONES , for solving the sequential generalized projections in Online Newton Step, as well as…
Modernizing Facebook Scoped Search: Keyword and Embedding Hybrid Retrieval with LLM Evaluation
Yongye Su, Zeya Zhang, Jane Kou +5
Beyond general web-scale search, social network search uniquely enables users to retrieve information and discover potential connections within their social context. We introduce a…
Collaborative targeted inference from continuously indexed nuisance parameter estimators
Cheng Ju, Antoine Chambaz, Mark J. van der Laan
We wish to infer the value of a parameter at a law from which we sample independent observations. The parameter is smooth and we can define two variation-independent features of th…
Propensity score prediction for electronic healthcare databases using Super Learner and High-dimensional Propensity Score Methods
Cheng Ju, Mary Combs, Samuel D Lendle +4
The optimal learner for prediction modeling varies depending on the underlying data-generating distribution. Super Learner (SL) is a generic ensemble learning algorithm that uses c…
Non-convex Finite-Sum Optimization Via SCSG Methods
Lihua Lei, Cheng Ju, Jianbo Chen +1
We develop a class of algorithms, as variants of the stochastically controlled stochastic gradient (SCSG) methods (Lei and Jordan, 2016), for the smooth non-convex finite-sum optim…
Scalable Collaborative Targeted Learning for High-Dimensional Data
Cheng Ju, Susan Gruber, Samuel D. Lendle +5
Robust inference of a low-dimensional parameter in a large semi-parametric model relies on external estimators of infinite-dimensional features of the distribution of the data. Typ…
On Adaptive Propensity Score Truncation in Causal Inference
Cheng Ju, Joshua Schwab, Mark J. van der Laan
The positivity assumption, or the experimental treatment assignment (ETA) assumption, is important for identifiability in causal inference. Even if the positivity assumption holds,…
Collaborative-controlled LASSO for Constructing Propensity Score-based Estimators in High-Dimensional Data
Cheng Ju, Richard Wyss, Jessica M. Franklin +3
Propensity score (PS) based estimators are increasingly used for causal inference in observational studies. However, model selection for PS estimation in high-dimensional data has…
Semisupervised Learning on Heterogeneous Graphs and its Applications to Facebook News Feed
Cheng Ju, James Li, Bram Wasti +1
Graph-based semi-supervised learning is a fundamental machine learning problem, and has been well studied. Most studies focus on homogeneous networks (e.g. citation network, friend…
Robust inference on the average treatment effect using the outcome highly adaptive lasso
Cheng Ju, David Benkeser, Mark J. van der Laan
Many estimators of the average effect of a treatment on an outcome require estimation of the propensity score, the outcome regression, or both. It is often beneficial to utilize fl…
FC-Track: Overlap-Aware Post-Association Correction for Online Multi-Object Tracking
Cheng Ju, Zejing Zhao, Akio Namiki
Reliable multi-object tracking (MOT) is essential for robotic systems operating in complex and dynamic environments. Despite recent advances in detection and association, online MO…
Extending iLQR method with control delay
Cheng Ju, Yan Qin, Chunjiang Fu
Iterative linear quadradic regulator(iLQR) has become a benchmark method to deal with nonlinear stochastic optimal control problem. However, it does not apply to delay system. In t…
The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image Classification
Cheng Ju, Aurélien Bibaut, Mark J. van der Laan
Artificial neural networks have been successfully applied to a variety of machine learning tasks, including image recognition, semantic segmentation, and machine translation. Howev…
DENOISER: Rethinking the Robustness for Open-Vocabulary Action Recognition
Haozhe Cheng, Cheng Ju, Haicheng Wang +5
As one of the fundamental video tasks in computer vision, Open-Vocabulary Action Recognition (OVAR) recently gains increasing attention, with the development of vision-language pre…