Jiaxin Zhang

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 39

Top Topics

Gaussian Smoothing

Novelty Detection

Diffusion Models

Top Venues

Publications

Jiaxin Zhang, Kamalika Das, Kumar Sricharan
Discriminant Distance-Aware Representation on Deterministic Uncertainty Quantification Methods. AISTATS (2024)
Jiaxin Zhang, Sirui Bi, Victor Fung
On the Quantification of Image Reconstruction Uncertainty without Training Data. WACV (2024)
Zhiyang Xu, Minqian Liu, Ying Shen, Joy Rimchala, Jiaxin Zhang, Qifan Wang, Yu Cheng, Lifu Huang
Lateralization LoRA: Interleaved Instruction Tuning with Modality-Specialized Adaptations. CoRR (2024)
Xiang Gao, Jiaxin Zhang, Lalla Mouatadid, Kamalika Das
SPUQ: Perturbation-Based Uncertainty Quantification for Large Language Models. CoRR (2024)
Xiang Gao, Jiaxin Zhang, Lalla Mouatadid, Kamalika Das
SPUQ: Perturbation-Based Uncertainty Quantification for Large Language Models. EACL (1) (2024)
Jiaxin Zhang, Kamalika Das, Kumar Sricharan
Discriminant Distance-Aware Representation on Deterministic Uncertainty Quantification Methods. CoRR (2024)
Jiaxin Zhang, Joy Rimchala, Lalla Mouatadid, Kamalika Das, Kumar Sricharan
DECDM: Document Enhancement using Cycle-Consistent Diffusion Models. WACV (2024)
Minqian Liu, Zhiyang Xu, Zihao Lin, Trevor Ashby, Joy Rimchala, Jiaxin Zhang, Lifu Huang
Holistic Evaluation for Interleaved Text-and-Image Generation. CoRR (2024)
Sirui Bi, Victor Fung, Jiaxin Zhang
On the Quantification of Image Reconstruction Uncertainty without Training Data. CoRR (2023)
Yu Wang, Ján Drgona, Jiaxin Zhang, Karthik Somayaji Nanjangud Suryanarayana, Malachi Schram, Frank Liu, Peng Li
AutoNF: Automated Architecture Optimization of Normalizing Flows with Unconstrained Continuous Relaxation Admitting Optimal Discrete Solution. AAAI (2023)
Yue Cui, Syed Irfan Ali Meerza, Zhuohang Li, Luyang Liu, Jiaxin Zhang, Jian Liu
RecUP-FL: Reconciling Utility and Privacy in Federated learning via User-configurable Privacy Defense. AsiaCCS (2023)
Sirui Bi, Victor Fung, Jiaxin Zhang
Accelerating Inverse Learning via Intelligent Localization with Exploratory Sampling. AAAI (2023)
Zhuohang Li, Jiaxin Zhang, Luyang Liu, Jian Liu
Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage. CVPR (2022)
Jingbo Sun, Li Yang, Jiaxin Zhang, Frank Liu, Mahantesh Halappanavar, Deliang Fan, Yu Cao
Gradient-Based Novelty Detection Boosted by Self-Supervised Binary Classification. AAAI (2022)
Victor Fung, Shuyi Jia, Jiaxin Zhang, Sirui Bi, Junqi Yin, Panchapakesan Ganesh
Atomic structure generation from reconstructing structural fingerprints. CoRR (2022)
Victor Fung, Shuyi Jia, Jiaxin Zhang, Sirui Bi, Junqi Yin, Panchapakesan Ganesh
Atomic structure generation from reconstructing structural fingerprints. Mach. Learn. Sci. Technol. 3 (4) (2022)
Yue Cui, Zhuohang Li, Luyang Liu, Jiaxin Zhang, Jian Liu
Privacy-preserving Speech-based Depression Diagnosis via Federated Learning. EMBC (2022)
Syed Irfan Ali Meerza, Zhuohang Li, Luyang Liu, Jiaxin Zhang, Jian Liu
Fair and Privacy-Preserving Alzheimer's Disease Diagnosis Based on Spontaneous Speech Analysis via Federated Learning. EMBC (2022)
Zhuohang Li, Jiaxin Zhang, Luyang Liu, Jian Liu
Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage. CoRR (2022)
Jiaxin Zhang, Sirui Bi, Victor Fung
Accelerating Inverse Learning via Intelligent Localization with Exploratory Sampling. CoRR (2022)
Ján Drgona, Sayak Mukherjee, Jiaxin Zhang, Frank Liu, Mahantesh Halappanavar
On the Stochastic Stability of Deep Markov Models. CoRR (2021)
Jiaxin Zhang, Sirui Bi, Guannan Zhang
A Hybrid Gradient Method to Designing Bayesian Experiments for Implicit Models. CoRR (2021)
Jiaxin Zhang, Sirui Bi, Guannan Zhang
A Scalable Gradient-Free Method for Bayesian Experimental Design with Implicit Models. CoRR (2021)
Jiaxin Zhang, Sirui Bi, Guannan Zhang
A Scalable Gradient Free Method for Bayesian Experimental Design with Implicit Models. AISTATS (2021)
Victor Fung, Jiaxin Zhang, Guoxiang Hu, Panchapakesan Ganesh, Bobby G. Sumpter
Inverse design of two-dimensional materials with invertible neural networks. CoRR (2021)
Jingbo Sun, Li Yang, Jiaxin Zhang, Frank Liu, Mahantesh Halappanavar, Deliang Fan, Yu Cao
Self-supervised Novelty Detection for Continual Learning: A Gradient-Based Approach Boosted by Binary Classification. CSSL (2021)
Ján Drgona, Sayak Mukherjee, Jiaxin Zhang, Frank Liu, Mahantesh Halappanavar
On the Stochastic Stability of Deep Markov Models. NeurIPS (2021)
Jiaxin Zhang, Hoang Tran, Dan Lu, Guannan Zhang
Enabling long-range exploration in minimization of multimodal functions. UAI (2021)
Jingbo Sun, Li Yang, Jiaxin Zhang, Frank Liu, Mahantesh Halappanavar, Deliang Fan, Yu Cao
Gradient-based Novelty Detection Boosted by Self-supervised Binary Classification. CoRR (2021)
Alexander Lavin, Hector Zenil, Brooks Paige, David Krakauer, Justin Gottschlich, Tim Mattson, Anima Anandkumar, Sanjay Choudry, Kamil Rocki, Atilim Günes Baydin, Carina Prunkl, Olexandr Isayev, Erik Peterson, Peter L. McMahon, Jakob Macke, Kyle Cranmer, Jiaxin Zhang, Haruko M. Wainwright, Adi Hanuka, Manuela Veloso, Samuel Assefa, Stephan Zheng, Avi Pfeffer
Simulation Intelligence: Towards a New Generation of Scientific Methods. CoRR (2021)
Zhuohang Li, Luyang Liu, Jiaxin Zhang, Jian Liu
Byzantine-robust Federated Learning through Spatial-temporal Analysis of Local Model Updates. ICPADS (2021)
Zhuohang Li, Luyang Liu, Jiaxin Zhang, Jian Liu
Byzantine-robust Federated Learning through Spatial-temporal Analysis of Local Model Updates. CoRR (2021)
Jiaxin Zhang, Michael D. Shields
On the quantification and efficient propagation of imprecise probabilities with copula dependence. Int. J. Approx. Reason. 122 (2020)
Sirui Bi, Jiaxin Zhang, Guannan Zhang
Scalable Deep-Learning-Accelerated Topology Optimization for Additively Manufactured Materials. CoRR (2020)
Congjie Wei, Jiaxin Zhang, Kenneth M. Liechti, Chenglin Wu
Data Driven Modeling of Interfacial Traction Separation Relations using Thermodynamic Consistent Neural Network (TCNN). CoRR (2020)
Jiaxin Zhang, Congjie Wei, Chenglin Wu
Thermodynamic Consistent Neural Networks for Learning Material Interfacial Mechanics. CoRR (2020)
Jiaxin Zhang, Hoang Tran, Dan Lu, Guannan Zhang
A Scalable Evolution Strategy with Directional Gaussian Smoothing for Blackbox Optimization. CoRR (2020)
Guannan Zhang, Jiaxin Zhang, Jacob D. Hinkle
Learning nonlinear level sets for dimensionality reduction in function approximation. NeurIPS (2019)
Michael D. Shields, Jiaxin Zhang
The generalization of Latin hypercube sampling. Reliab. Eng. Syst. Saf. 148 (2016)