Jiaxin Zhang

According to our database, Jiaxin Zhang authored at least 40 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Survival of the Safest: Towards Secure Prompt Optimization through Interleaved Multi-Objective Evolution.

Ankita Sinha, Wendi Cui, Kamalika Das, Jiaxin Zhang

CoRR, 2024

Lateralization LoRA: Interleaved Instruction Tuning with Modality-Specialized Adaptations.

Zhiyang Xu, Minqian Liu, Ying Shen, Joy Rimchala, Jiaxin Zhang, Qifan Wang, Yu Cheng, Lifu Huang

CoRR, 2024

PhaseEvo: Towards Unified In-Context Prompt Optimization for Large Language Models.

Wendi Cui, Jiaxin Zhang, Zhuohang Li, Hao Sun, Damien Lopez, Kamalika Das, Bradley A. Malin, Kumar Sricharan

CoRR, 2024

GLOCALFAIR: Jointly Improving Global and Local Group Fairness in Federated Learning.

Syed Irfan Ali Meerza, Luyang Liu, Jiaxin Zhang, Jian Liu

CoRR, 2024

DCR-Consistency: Divide-Conquer-Reasoning for Consistency Evaluation and Improvement of Large Language Models.

Wendi Cui, Jiaxin Zhang, Zhuohang Li, Damien Lopez, Kamalika Das, Bradley A. Malin, Kumar Sricharan

CoRR, 2024

DECDM: Document Enhancement using Cycle-Consistent Diffusion Models.

Jiaxin Zhang, Joy Rimchala, Lalla Mouatadid, Kamalika Das, Kumar Sricharan

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024

On the Quantification of Image Reconstruction Uncertainty without Training Data.

Jiaxin Zhang, Sirui Bi, Victor Fung

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024

Synthetic Knowledge Ingestion: Towards Knowledge Refinement and Injection for Enhancing Large Language Models.

Jiaxin Zhang, Wendi Cui, Yiran Huang, Kamalika Das, Kumar Sricharan

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

Holistic Evaluation for Interleaved Text-and-Image Generation.

Minqian Liu, Zhiyang Xu, Zihao Lin, Trevor Ashby, Joy Rimchala, Jiaxin Zhang, Lifu Huang

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

Do You Know What You Are Talking About? Characterizing Query-Knowledge Relevance For Reliable Retrieval Augmented Generation.

Zhuohang Li, Jiaxin Zhang, Chao Yan, Kamalika Das, Kumar Sricharan, Murat Kantarcioglu, Bradley A. Malin

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

Divide-Conquer-Reasoning for Consistency Evaluation and Automatic Improvement of Large Language Models.

Wendi Cui, Zhuohang Li, Damien Lopez, Kamalika Das, Bradley A. Malin, Kumar Sricharan, Jiaxin Zhang

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: EMNLP 2024, 2024

SPUQ: Perturbation-Based Uncertainty Quantification for Large Language Models.

Xiang Gao, Jiaxin Zhang, Lalla Mouatadid, Kamalika Das

Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics, 2024

Discriminant Distance-Aware Representation on Deterministic Uncertainty Quantification Methods.

Jiaxin Zhang, Kamalika Das, Kumar Sricharan

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

SAC³: Reliable Hallucination Detection in Black-Box Language Models via Semantic-aware Cross-check Consistency.

Jiaxin Zhang, Zhuohang Li, Kamalika Das, Bradley A. Malin, Kumar Sricharan

CoRR, 2023

Interactive Multi-fidelity Learning for Cost-effective Adaptation of Language Model with Sparse Human Supervision.

Jiaxin Zhang, Zhuohang Li, Kamalika Das, Kumar Sricharan

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Speech Privacy Leakage from Shared Gradients in Distributed Learning.

Zhuohang Li, Jiaxin Zhang, Jian Liu

Proceedings of the IEEE International Conference on Acoustics, 2023

SAC³: Reliable Hallucination Detection in Black-Box Language Models via Semantic-aware Cross-check Consistency.

Jiaxin Zhang, Zhuohang Li, Kamalika Das, Bradley A. Malin, Kumar Sricharan

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

RecUP-FL: Reconciling Utility and Privacy in Federated learning via User-configurable Privacy Defense.

Yue Cui, Syed Irfan Ali Meerza, Zhuohang Li, Luyang Liu, Jiaxin Zhang, Jian Liu

Proceedings of the 2023 ACM Asia Conference on Computer and Communications Security, 2023

AutoNF: Automated Architecture Optimization of Normalizing Flows with Unconstrained Continuous Relaxation Admitting Optimal Discrete Solution.

Yu Wang, Ján Drgona, Jiaxin Zhang, Karthik Somayaji Nanjangud Suryanarayana, Malachi Schram, Frank Liu, Peng Li

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

Accelerating Inverse Learning via Intelligent Localization with Exploratory Sampling.

Sirui Bi, Victor Fung, Jiaxin Zhang

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Atomic structure generation from reconstructing structural fingerprints.

Victor Fung, Shuyi Jia, Jiaxin Zhang, Sirui Bi, Junqi Yin, Panchapakesan Ganesh

Mach. Learn. Sci. Technol., December, 2022

Fair and Privacy-Preserving Alzheimer's Disease Diagnosis Based on Spontaneous Speech Analysis via Federated Learning.

Syed Irfan Ali Meerza, Zhuohang Li, Luyang Liu, Jiaxin Zhang, Jian Liu

Proceedings of the 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2022

Privacy-preserving Speech-based Depression Diagnosis via Federated Learning.

Yue Cui, Zhuohang Li, Luyang Liu, Jiaxin Zhang, Jian Liu

Proceedings of the 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2022

Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage.

Zhuohang Li, Jiaxin Zhang, Luyang Liu, Jian Liu

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Gradient-Based Novelty Detection Boosted by Self-Supervised Binary Classification.

Jingbo Sun, Li Yang, Jiaxin Zhang, Frank Liu, Mahantesh Halappanavar, Deliang Fan, Yu Cao

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Simulation Intelligence: Towards a New Generation of Scientific Methods.

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 H. Macke, Kyle Cranmer, Jiaxin Zhang, Haruko M. Wainwright, Adi Hanuka, Manuela Veloso, Samuel Assefa, Stephan Zheng, Avi Pfeffer

CoRR, 2021

Inverse design of two-dimensional materials with invertible neural networks.

Victor Fung, Jiaxin Zhang, Guoxiang Hu, Panchapakesan Ganesh, Bobby G. Sumpter

CoRR, 2021

A Hybrid Gradient Method to Designing Bayesian Experiments for Implicit Models.

Jiaxin Zhang, Sirui Bi, Guannan Zhang

CoRR, 2021

Self-supervised Novelty Detection for Continual Learning: A Gradient-Based Approach Boosted by Binary Classification.

Jingbo Sun, Li Yang, Jiaxin Zhang, Frank Liu, Mahantesh Halappanavar, Deliang Fan, Yu Cao

Proceedings of the Continual Semi-Supervised Learning - First International Workshop, 2021

Enabling long-range exploration in minimization of multimodal functions.

Jiaxin Zhang, Hoang Tran, Dan Lu, Guannan Zhang

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

On the Stochastic Stability of Deep Markov Models.

Ján Drgona, Sayak Mukherjee, Jiaxin Zhang, Frank Liu, Mahantesh Halappanavar

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Byzantine-robust Federated Learning through Spatial-temporal Analysis of Local Model Updates.

Zhuohang Li, Luyang Liu, Jiaxin Zhang, Jian Liu

Proceedings of the 27th IEEE International Conference on Parallel and Distributed Systems, 2021

A Scalable Gradient Free Method for Bayesian Experimental Design with Implicit Models.

Jiaxin Zhang, Sirui Bi, Guannan Zhang

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

On the quantification and efficient propagation of imprecise probabilities with copula dependence.

Jiaxin Zhang, Michael D. Shields

Int. J. Approx. Reason., 2020

Scalable Deep-Learning-Accelerated Topology Optimization for Additively Manufactured Materials.

Sirui Bi, Jiaxin Zhang, Guannan Zhang

CoRR, 2020

Thermodynamic Consistent Neural Networks for Learning Material Interfacial Mechanics.

Jiaxin Zhang, Congjie Wei, Chenglin Wu

CoRR, 2020

Data Driven Modeling of Interfacial Traction Separation Relations using Thermodynamic Consistent Neural Network (TCNN).

Congjie Wei, Jiaxin Zhang, Kenneth M. Liechti, Chenglin Wu

CoRR, 2020

A Scalable Evolution Strategy with Directional Gaussian Smoothing for Blackbox Optimization.

Jiaxin Zhang, Hoang Tran, Dan Lu, Guannan Zhang

CoRR, 2020

2019

Learning nonlinear level sets for dimensionality reduction in function approximation.

Guannan Zhang, Jiaxin Zhang, Jacob D. Hinkle

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2016

The generalization of Latin hypercube sampling.

Michael D. Shields, Jiaxin Zhang

Reliab. Eng. Syst. Saf., 2016