Jiaxin Zhang

Orcid: 0000-0002-7576-6110

Affiliations:
  • Intuit AI Research, Mountain View, CA, USA
  • Oak Ridge National Laboratory, TN, USA
  • Johns Hopkins University, Department of Civil Engineering, Baltimore, MD, USA (former)


According to our database1, Jiaxin Zhang authored at least 38 papers between 2016 and 2024.

Collaborative distances:

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2024
Do You Know What You Are Talking About? Characterizing Query-Knowledge Relevance For Reliable Retrieval Augmented Generation.
CoRR, 2024

Lateralization LoRA: Interleaved Instruction Tuning with Modality-Specialized Adaptations.
CoRR, 2024

PhaseEvo: Towards Unified In-Context Prompt Optimization for Large Language Models.
CoRR, 2024

GLOCALFAIR: Jointly Improving Global and Local Group Fairness in Federated Learning.
CoRR, 2024

DCR-Consistency: Divide-Conquer-Reasoning for Consistency Evaluation and Improvement of Large Language Models.
CoRR, 2024

DECDM: Document Enhancement using Cycle-Consistent Diffusion Models.
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024

On the Quantification of Image Reconstruction Uncertainty without Training Data.
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024

Holistic Evaluation for Interleaved Text-and-Image Generation.
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.
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

SPUQ: Perturbation-Based Uncertainty Quantification for Large Language Models.
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.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023
SAC<sup>3</sup>: Reliable Hallucination Detection in Black-Box Language Models via Semantic-aware Cross-check Consistency.
CoRR, 2023

Interactive Multi-fidelity Learning for Cost-effective Adaptation of Language Model with Sparse Human Supervision.
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.
Proceedings of the IEEE International Conference on Acoustics, 2023

SAC³: Reliable Hallucination Detection in Black-Box Language Models via Semantic-aware Cross-check Consistency.
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.
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.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

Accelerating Inverse Learning via Intelligent Localization with Exploratory Sampling.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
Atomic structure generation from reconstructing structural fingerprints.
Mach. Learn. Sci. Technol., December, 2022

Fair and Privacy-Preserving Alzheimer's Disease Diagnosis Based on Spontaneous Speech Analysis via Federated Learning.
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.
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.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Gradient-Based Novelty Detection Boosted by Self-Supervised Binary Classification.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
Simulation Intelligence: Towards a New Generation of Scientific Methods.
CoRR, 2021

Inverse design of two-dimensional materials with invertible neural networks.
CoRR, 2021

A Hybrid Gradient Method to Designing Bayesian Experiments for Implicit Models.
CoRR, 2021

Self-supervised Novelty Detection for Continual Learning: A Gradient-Based Approach Boosted by Binary Classification.
Proceedings of the Continual Semi-Supervised Learning - First International Workshop, 2021

Enabling long-range exploration in minimization of multimodal functions.
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

On the Stochastic Stability of Deep Markov Models.
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.
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.
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.
Int. J. Approx. Reason., 2020

Scalable Deep-Learning-Accelerated Topology Optimization for Additively Manufactured Materials.
CoRR, 2020

Thermodynamic Consistent Neural Networks for Learning Material Interfacial Mechanics.
CoRR, 2020

Data Driven Modeling of Interfacial Traction Separation Relations using Thermodynamic Consistent Neural Network (TCNN).
CoRR, 2020

A Scalable Evolution Strategy with Directional Gaussian Smoothing for Blackbox Optimization.
CoRR, 2020

2019
Learning nonlinear level sets for dimensionality reduction in function approximation.
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.
Reliab. Eng. Syst. Saf., 2016


  Loading...