Jing Liu

Orcid: 0000-0002-1712-2966

Affiliations:

University of California, San Diego, Department of Electrical and Computer Engineering, CA, USA

According to our database¹, Jing Liu authored at least 33 papers between 2015 and 2026.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

Legend:

Book In proceedings Article PhD thesis Dataset Other

Bibliography

2026

TTQ: Activation-Aware Test-Time Quantization to Accelerate LLM Inference On The Fly.

[BibT_eX]

[DOI]

Toshiaki Koike-Akino

Jing Liu

Ye Wang

CoRR, March, 2026

Amplification Effects in Test-Time Reinforcement Learning: Safety and Reasoning Vulnerabilities.

[BibT_eX]

[DOI]

CoRR, March, 2026

Directional Embedding Smoothing for Robust Vision Language Models.

[BibT_eX]

[DOI]

Ye Wang

Jing Liu

Toshiaki Koike-Akino

CoRR, March, 2026

Embedding Morphology into Transformers for Cross-Robot Policy Learning.

[BibT_eX]

[DOI]

CoRR, March, 2026

GPT Sonography: Hand Gesture Decoding From Forearm Ultrasound Images via a Large Vision-Language Model.

[BibT_eX]

[DOI]

IEEE Access, 2026

Evaluating Security Policy Compliance in Infrastructure as Code Generated by Large Language Models.

[BibT_eX]

[DOI]

Proceedings of the 2026 14th International Symposium on Digital Forensics and Security (ISDFS), 2026

LatentLLM: Activation-Aware Transform to Multi-Head Latent Attention.

[BibT_eX]

[DOI]

Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

2025

AWP: Activation-Aware Weight Pruning and Quantization with Projected Gradient Descent.

[BibT_eX]

[DOI]

CoRR, June, 2025

Probabilistic Forecasting for Building Energy Systems using Time-Series Foundation Models.

[BibT_eX]

[DOI]

Christopher R. Laughman

Ankush Chakrabarty

CoRR, June, 2025

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models.

[BibT_eX]

[DOI]

CoRR, May, 2025

μ-MoE: Test-Time Pruning as Micro-Grained Mixture-of-Experts.

[BibT_eX]

[DOI]

Toshiaki Koike-Akino

Jing Liu

Ye Wang

CoRR, May, 2025

LatentLLM: Attention-Aware Joint Tensor Compression.

[BibT_eX]

[DOI]

CoRR, May, 2025

Winning Big with Small Models: Knowledge Distillation vs. Self-Training for Reducing Hallucination in QA Agents.

[BibT_eX]

[DOI]

CoRR, February, 2025

Smoothed Embeddings for Robust Language Models.

[BibT_eX]

[DOI]

CoRR, January, 2025

Quantum Diffusion Models for Few-Shot Learning.

[BibT_eX]

[DOI]

Proceedings of the Quantum Computing and Artificial Intelligence, 2025

Forget to Flourish: Leveraging Machine-Unlearning on Pretrained Language Models for Privacy Leakage.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence, 2025

2024

Exploring User-level Gradient Inversion with a Diffusion Prior.

[BibT_eX]

[DOI]

CoRR, 2024

Variational Randomized Smoothing for Sample-Wise Adversarial Robustness.

[BibT_eX]

[DOI]

CoRR, 2024

GPT Sonograpy: Hand Gesture Decoding from Forearm Ultrasound Images via VLM.

[BibT_eX]

[DOI]

CoRR, 2024

Efficient Differentially Private Fine-Tuning of Diffusion Models.

[BibT_eX]

[DOI]

CoRR, 2024

SuperLoRA: Parameter-Efficient Unified Adaptation of Multi-Layer Attention Modules.

[BibT_eX]

[DOI]

CoRR, 2024

Why Does Differential Privacy with Large Epsilon Defend Against Practical Membership Inference Attacks?

[BibT_eX]

[DOI]

CoRR, 2024

Random Channel Ablation for Robust Hand Gesture Classification with Multimodal Biosignals.

[BibT_eX]

[DOI]

Proceedings of the 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2024

SuperLoRA: Parameter-Efficient Unified Adaptation for Large Vision Models.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

Analyzing Inference Privacy Risks Through Gradients In Machine Learning.

[BibT_eX]

[DOI]

Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security, 2024

SuperLoRA: Parameter-Efficient Unified Adaptation of Large Foundation Models.

[BibT_eX]

[DOI]

Proceedings of the 35th British Machine Vision Conference, 2024

2023

Stabilizing Subject Transfer in EEG Classification with Divergence Estimation.

[BibT_eX]

[DOI]

Niklas Smedemark-Margulies

CoRR, 2023

2019

Sparse Bayesian Learning for Robust PCA: Algorithms and Analyses.

[BibT_eX]

[DOI]

Jing Liu

Bhaskar D. Rao

IEEE Trans. Signal Process., 2019

Robust PCA via ℓ0-ℓ1 Regularization.

[BibT_eX]

[DOI]

Jing Liu

Bhaskar D. Rao

IEEE Trans. Signal Process., 2019

Sparse Bayesian Learning for Robust PCA.

[BibT_eX]

[DOI]

Jing Liu

Yacong Ding

Bhaskar Rao

Proceedings of the IEEE International Conference on Acoustics, 2019

2018

Robust Linear Regression via ℓ0 Regularization.

[BibT_eX]

[DOI]

Jing Liu

Pamela C. Cosman

Bhaskar D. Rao

IEEE Trans. Signal Process., 2018

2017

Sparsity regularized Principal Component Pursuit.

[BibT_eX]

[DOI]

Jing Liu

Pamela C. Cosman

Bhaskar D. Rao

Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

2015

Image registration robust to sparse large errors.

[BibT_eX]

[DOI]

Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2015

Jing Liu

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Bibliography

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