Search Results for author: Shixiang Zhu

Found 21 papers, 2 papers with code

Counterfactual Fairness through Transforming Data Orthogonal to Bias

no code implementations26 Mar 2024 Shuyi Chen, Shixiang Zhu

We introduce a novel data pre-processing algorithm, Orthogonal to Bias (OB), designed to remove the influence of a group of continuous sensitive variables, thereby facilitating counterfactual fairness in machine learning applications.

counterfactual Decision Making +1

Conditional Generative Representation for Black-Box Optimization with Implicit Constraints

no code implementations27 Oct 2023 Wenqian Xing, Jungho Lee, Chong Liu, Shixiang Zhu

This approach leverages a conditional variational autoencoder to learn the distribution of feasible decisions, enabling a two-way mapping between the original decision space and a simplified, constraint-free latent space.

Bayesian Optimization Decision Making

Uncertainty-Aware Robust Learning on Noisy Graphs

no code implementations14 Jun 2023 Shuyi Chen, Kaize Ding, Shixiang Zhu

Graph neural networks have shown impressive capabilities in solving various graph learning tasks, particularly excelling in node classification.

Graph Learning Node Classification

Counterfactual Generative Models for Time-Varying Treatments

no code implementations25 May 2023 Shenghao Wu, Wenbin Zhou, Minshuo Chen, Shixiang Zhu

Estimating the counterfactual outcome of treatment is essential for decision-making in public health and clinical science, among others.

counterfactual Decision Making +2

Conditional Generative Modeling for High-dimensional Marked Temporal Point Processes

no code implementations21 May 2023 Zheng Dong, Zekai Fan, Shixiang Zhu

To address this challenge, this study proposes a novel event-generation framework for modeling point processes with high-dimensional marks.

Point Processes

Neural Spectral Marked Point Processes

1 code implementation ICLR 2022 Shixiang Zhu, Haoyun Wang, Zheng Dong, Xiuyuan Cheng, Yao Xie

In this paper, we introduce a novel and general neural network-based non-stationary influence kernel with high expressiveness for handling complex discrete events data while providing theoretical performance guarantees.

Point Processes

Early Detection of COVID-19 Hotspots Using Spatio-Temporal Data

no code implementations31 May 2021 Shixiang Zhu, Alexander Bukharin, Liyan Xie, Khurram Yamin, Shihao Yang, Pinar Keskinocak, Yao Xie

Recently, the Centers for Disease Control and Prevention (CDC) has worked with other federal agencies to identify counties with increasing coronavirus disease 2019 (COVID-19) incidence (hotspots) and offers support to local health departments to limit the spread of the disease.

Signal Processing Challenges and Examples for {\it in-situ} Transmission Electron Microscopy

no code implementations18 Apr 2021 Josh Kacher, Yao Xie, Sven P. Voigt, Shixiang Zhu, Henry Yuchi, Jordan Key, Surya R. Kalidindi

Transmission Electron Microscopy (TEM) is a powerful tool for imaging material structure and characterizing material chemistry.

Data-Driven Optimization for Atlanta Police Zone Design

no code implementations30 Mar 2021 Shixiang Zhu, He Wang, Yao Xie

By analyzing data before and after the zone redesign, we show that the new design has reduced the response time to high priority 911 calls by 5. 8\% and the imbalance of police workload among different zones by 43\%.

Balanced Districting on Grid Graphs with Provable Compactness and Contiguity

no code implementations9 Feb 2021 Cyrus Hettle, Shixiang Zhu, Swati Gupta, Yao Xie

Given a graph $G = (V, E)$ with vertex weights $w(v)$ and a desired number of parts $k$, the goal in graph partitioning problems is to partition the vertex set V into parts $V_1,\ldots, V_k$.

graph partitioning Data Structures and Algorithms Combinatorics Optimization and Control

Goodness-of-Fit Test for Mismatched Self-Exciting Processes

no code implementations16 Jun 2020 Song Wei, Shixiang Zhu, Minghe Zhang, Yao Xie

Recently there have been many research efforts in developing generative models for self-exciting point processes, partly due to their broad applicability for real-world applications.

Point Processes

Distributionally Robust Weighted $k$-Nearest Neighbors

no code implementations7 Jun 2020 Shixiang Zhu, Liyan Xie, Minghe Zhang, Rui Gao, Yao Xie

When the samples are limited, robustness is especially crucial to ensure the generalization capability of the classifier.

Few-Shot Learning General Classification +1

Deep Fourier Kernel for Self-Attentive Point Processes

no code implementations17 Feb 2020 Shixiang Zhu, Minghe Zhang, Ruyi Ding, Yao Xie

We present a novel attention-based model for discrete event data to capture complex non-linear temporal dependence structures.

Deep Attention Point Processes

Sequential Adversarial Anomaly Detection for One-Class Event Data

no code implementations21 Oct 2019 Shixiang Zhu, Henry Shaowu Yuchi, Minghe Zhang, Yao Xie

We consider the sequential anomaly detection problem in the one-class setting when only the anomalous sequences are available and propose an adversarial sequential detector by solving a minimax problem to find an optimal detector against the worst-case sequences from a generator.

Anomaly Detection Point Processes

Imitation Learning of Neural Spatio-Temporal Point Processes

1 code implementation13 Jun 2019 Shixiang Zhu, Shuang Li, Zhigang Peng, Yao Xie

We present a novel Neural Embedding Spatio-Temporal (NEST) point process model for spatio-temporal discrete event data and develop an efficient imitation learning (a type of reinforcement learning) based approach for model fitting.

Computational Efficiency Imitation Learning +1

Spatial-Temporal-Textual Point Processes for Crime Linkage Detection

no code implementations1 Feb 2019 Shixiang Zhu, Yao Xie

We propose a new statistical modeling framework for {\it spatio-temporal-textual} data and demonstrate its usage on crime linkage detection.

Point Processes

Learning Temporal Point Processes via Reinforcement Learning

no code implementations NeurIPS 2018 Shuang Li, Shuai Xiao, Shixiang Zhu, Nan Du, Yao Xie, Le Song

Social goods, such as healthcare, smart city, and information networks, often produce ordered event data in continuous time.

Point Processes reinforcement-learning +1

Crime Event Embedding with Unsupervised Feature Selection

no code implementations15 Jun 2018 Shixiang Zhu, Yao Xie

Using numerical experiments on a large-scale crime dataset, we show that our regularized RBMs can achieve better event embedding and the selected features are highly interpretable from human understanding.

feature selection

Crime incidents embedding using restricted Boltzmann machines

no code implementations28 Oct 2017 Shixiang Zhu, Yao Xie

We present a new approach for detecting related crime series, by unsupervised learning of the latent feature embeddings from narratives of crime record via the Gaussian-Bernoulli Restricted Boltzmann Machines (RBM).

Clustering

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