32 citations · 60 across the 17 of their papers we have counts for
17 papers
Flow-based Conformal Prediction for Multi-dimensional Time Series
Junghwan Lee, Chen Xu, Yao Xie
Time series prediction underpins a broad range of downstream tasks across many scientific domains. Recent advances and increasing adoption of black-box machine learning models for…
Local Flow Matching Generative Models
Chen Xu, Xiuyuan Cheng, Yao Xie
Flow Matching (FM) is a simulation-free method for learning a continuous, invertible flow that interpolates between two distributions, and in particular generates data from noise.…
Transformer Conformal Prediction for Time Series
Junghwan Lee, Chen Xu, Yao Xie
We present a conformal prediction method for time series using the Transformer architecture to capture long-memory and long-range dependencies. Specifically, we use the Transformer…
Kernel-based Optimally Weighted Conformal Time-Series Prediction
Jonghyeok Lee, Chen Xu, Yao Xie
In this work, we present a novel conformal prediction method for time-series, which we call Kernel-based Optimally Weighted Conformal Prediction Intervals (KOWCPI). Specifically, K…
Conformal prediction for multi-dimensional time series by ellipsoidal sets
Chen Xu, Hanyang Jiang, Yao Xie
Conformal prediction (CP) has been a popular method for uncertainty quantification because it is distribution-free, model-agnostic, and theoretically sound. For forecasting problem…
Flow-based Distributionally Robust Optimization
Chen Xu, Jonghyeok Lee, Xiuyuan Cheng +1
We present a computationally efficient framework, called , for solving flow-based distributionally robust optimization (DRO) problems with Wasserstein uncertainty…
Computing high-dimensional optimal transport by flow neural networks
Chen Xu, Xiuyuan Cheng, Yao Xie
Computing optimal transport (OT) for general high-dimensional data has been a long-standing challenge. Despite much progress, most of the efforts including neural network methods h…
Generalized generalized linear models: Convex estimation and online bounds
Anatoli Juditsky, Arkadi Nemirovski, Yao Xie +1
We introduce a new computational framework for estimating parameters in generalized generalized linear models (GGLM), a class of models that extends the popular generalized linear…
Normalizing flow neural networks by JKO scheme
Chen Xu, Xiuyuan Cheng, Yao Xie
Normalizing flow is a class of deep generative models for efficient sampling and likelihood estimation, which achieves attractive performance, particularly in high dimensions. The…
Sequential Predictive Conformal Inference for Time Series
Chen Xu, Yao Xie
We present a new distribution-free conformal prediction algorithm for sequential data (e.g., time series), called the \textit{sequential predictive conformal inference} (\texttt{SP…
Spatio-Temporal Wildfire Prediction using Multi-Modal Data
Chen Xu, Yao Xie, Daniel A. Zuniga Vazquez +2
Due to severe societal and environmental impacts, wildfire prediction using multi-modal sensing data has become a highly sought-after data-analytical tool by various stakeholders (…
Conformal prediction set for time-series
Chen Xu, Yao Xie
When building either prediction intervals for regression (with real-valued response) or prediction sets for classification (with categorical responses), uncertainty quantification…
Invertible Neural Networks for Graph Prediction
Chen Xu, Xiuyuan Cheng, Yao Xie
Graph prediction problems prevail in data analysis and machine learning. The inverse prediction problem, namely to infer input data from given output labels, is of emerging interes…
An alternative approach to train neural networks using monotone variational inequality
Chen Xu, Xiuyuan Cheng, Yao Xie
We propose an alternative approach to neural network training using the monotone vector field, an idea inspired by the seminal work of Juditsky and Nemirovski [Juditsky & Nemirovsk…
Conformal Anomaly Detection on Spatio-Temporal Observations with Missing Data
Chen Xu, Yao Xie
We develop a distribution-free, unsupervised anomaly detection method called ECAD, which wraps around any regression algorithm and sequentially detects anomalies. Rooted in conform…
Solar Radiation Ramping Events Modeling Using Spatio-temporal Point Processes
Minghe Zhang, Chen Xu, Andy Sun +2
Modeling and predicting solar events, particularly the solar ramping event, is critical for improving situational awareness for solar power generation systems. It has been acknowle…
Predictive Inference Is Free with the Jackknife+-after-Bootstrap
Byol Kim, Chen Xu, Rina Foygel Barber
Ensemble learning is widely used in applications to make predictions in complex decision problems---for example, averaging models fitted to a sequence of samples bootstrapped from…