74 citations · 195 across the 15 of their papers we have counts for
15 papers
ABOPD: Antibody CDR Design via On-Policy Distillation
Zhuo Yang, Jiaying He, Jiaqing Xie +5
Antibodies are essential therapeutic molecules, and their complementarity-determining regions (CDRs) form the primary antigen-recognition interface. Recent protein generative model…
Structured Episodic Event Memory
Zhengxuan Lu, Dongfang Li, Yukun Shi +3
Current approaches to memory in Large Language Models (LLMs) predominantly rely on static Retrieval-Augmented Generation (RAG), which often results in scattered retrieval and fails…
MolAct: An Agentic RL Framework for Molecular Editing and Property Optimization
Zhuo Yang, Yeyun Chen, Jiaqing Xie +7
Molecular editing and optimization are multi-step problems that require iteratively improving properties while keeping molecules chemically valid and structurally similar. We frame…
Label Information Enhanced Fraud Detection against Low Homophily in Graphs
Yuchen Wang, Jinghui Zhang, Zhengjie Huang +9
Node classification is a substantial problem in graph-based fraud detection. Many existing works adopt Graph Neural Networks (GNNs) to enhance fraud detectors. While promising, cur…
Fast and Scalable Estimator for Sparse and Unit-Rank Higher-Order Regression Models
Jiaqi Zhang, Beilun Wang
Because tensor data appear more and more frequently in various scientific researches and real-world applications, analyzing the relationship between tensor features and the univari…
Sparse and Low-Rank High-Order Tensor Regression via Parallel Proximal Method
Jiaqi Zhang, Yinghao Cai, Zhaoyang Wang +1
Recently, tensor data (or multidimensional array) have been generated in many modern applications, such as functional magnetic resonance imaging (fMRI) in neuroscience and videos i…
A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models
Beilun Wang, Arshdeep Sekhon, Yanjun Qi
We consider the problem of including additional knowledge in estimating sparse Gaussian graphical models (sGGMs) from aggregated samples, arising often in bioinformatics and neuroi…
Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure
Beilun Wang, Arshdeep Sekhon, Yanjun Qi
We focus on the problem of estimating the change in the dependency structures of two -dimensional Gaussian Graphical models (GGMs). Previous studies for sparse change estimation…
A Constrained, Weighted-L1 Minimization Approach for Joint Discovery of Heterogeneous Neural Connectivity Graphs
Chandan Singh, Beilun Wang, Yanjun Qi
Determining functional brain connectivity is crucial to understanding the brain and neural differences underlying disorders such as autism. Recent studies have used Gaussian graphi…
GaKCo: a Fast GApped k-mer string Kernel using COunting
Ritambhara Singh, Arshdeep Sekhon, Kamran Kowsari +3
String Kernel (SK) techniques, especially those using gapped -mers as features (gk), have obtained great success in classifying sequences like DNA, protein, and text. However, t…
DeepCloak: Masking Deep Neural Network Models for Robustness Against Adversarial Samples
Ji Gao, Beilun Wang, Zeming Lin +2
Recent studies have shown that deep neural networks (DNN) are vulnerable to adversarial samples: maliciously-perturbed samples crafted to yield incorrect model outputs. Such attack…
A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models
Beilun Wang, Ji Gao, Yanjun Qi
Estimating multiple sparse Gaussian Graphical Models (sGGMs) jointly for many related tasks (large ) under a high-dimensional (large ) situation is an important task. Most pr…
A Theoretical Framework for Robustness of (Deep) Classifiers against Adversarial Examples
Beilun Wang, Ji Gao, Yanjun Qi
Most machine learning classifiers, including deep neural networks, are vulnerable to adversarial examples. Such inputs are typically generated by adding small but purposeful modifi…
Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks
Jack Lanchantin, Ritambhara Singh, Beilun Wang +1
Deep neural network (DNN) models have recently obtained state-of-the-art prediction accuracy for the transcription factor binding (TFBS) site classification task. However, it remai…
A constrained L1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models
Beilun Wang, Ritambhara Singh, Yanjun Qi
Identifying context-specific entity networks from aggregated data is an important task, arising often in bioinformatics and neuroimaging. Computationally, this task can be formulat…