Jing Liu

According to our database, Jing Liu authored at least 26 papers between 2015 and 2025.

Collaborative distances:

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

2025

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

Jing Liu, Toshiaki Koike-Akino, Ye Wang, Hassan Mansour, Matthew Brand

CoRR, June, 2025

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

Young Jin Park, François G. Germain, Jing Liu, Ye Wang, Toshiaki Koike-Akino, Gordon Wichern, Navid Azizan, Christopher R. Laughman, Ankush Chakrabarty

CoRR, June, 2025

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

Xiangyu Chen, Jing Liu, Ye Wang, Matthew Brand, Pu Wang, Toshiaki Koike-Akino

CoRR, May, 2025

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

Toshiaki Koike-Akino, Jing Liu, Ye Wang

CoRR, May, 2025

LatentLLM: Attention-Aware Joint Tensor Compression.

Toshiaki Koike-Akino, Xiangyu Chen, Jing Liu, Ye Wang, Pu Wang, Matthew Brand

CoRR, May, 2025

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

Ashley Lewis, Michael White, Jing Liu, Toshiaki Koike-Akino, Kieran Parsons, Ye Wang

CoRR, February, 2025

Smoothed Embeddings for Robust Language Models.

Ryo Hase, Md. Rafi Ur Rashid, Ashley Lewis, Jing Liu, Toshiaki Koike-Akino, Kieran Parsons, Ye Wang

CoRR, January, 2025

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

Md. Rafi Ur Rashid, Jing Liu, Toshiaki Koike-Akino, Ye Wang, Shagufta Mehnaz

Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 2025

2024

Quantum Diffusion Models for Few-Shot Learning.

Ruhan Wang, Ye Wang, Jing Liu, Toshiaki Koike-Akino

CoRR, 2024

Exploring User-level Gradient Inversion with a Diffusion Prior.

Zhuohang Li, Andrew Lowy, Jing Liu, Toshiaki Koike-Akino, Bradley A. Malin, Kieran Parsons, Ye Wang

CoRR, 2024

Variational Randomized Smoothing for Sample-Wise Adversarial Robustness.

Ryo Hase, Ye Wang, Toshiaki Koike-Akino, Jing Liu, Kieran Parsons

CoRR, 2024

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

Keshav Bimbraw, Ye Wang, Jing Liu, Toshiaki Koike-Akino

CoRR, 2024

Efficient Differentially Private Fine-Tuning of Diffusion Models.

Jing Liu, Andrew Lowy, Toshiaki Koike-Akino, Kieran Parsons, Ye Wang

CoRR, 2024

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

Xiangyu Chen, Jing Liu, Ye Wang, Pu (Perry) Wang, Matthew Brand, Guanghui Wang, Toshiaki Koike-Akino

CoRR, 2024

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

Andrew Lowy, Zhuohang Li, Jing Liu, Toshiaki Koike-Akino, Kieran Parsons, Ye Wang

CoRR, 2024

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

Keshav Bimbraw, Jing Liu, Ye Wang, Toshiaki Koike-Akino

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.

Xiangyu Chen, Jing Liu, Ye Wang, Pu (Perry) Wang, Matthew Brand, Guanghui Wang, Toshiaki Koike-Akino

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

Analyzing Inference Privacy Risks Through Gradients In Machine Learning.

Zhuohang Li, Andrew Lowy, Jing Liu, Toshiaki Koike-Akino, Kieran Parsons, Bradley A. Malin, Ye Wang

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

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

Xiangyu Chen, Jing Liu, Ye Wang, Pu (Perry) Wang, Matthew Brand, Guanghui Wang, Toshiaki Koike-Akino

Proceedings of the 35th British Machine Vision Conference, 2024

2023

Stabilizing Subject Transfer in EEG Classification with Divergence Estimation.

Niklas Smedemark-Margulies, Ye Wang, Toshiaki Koike-Akino, Jing Liu, Kieran Parsons, Yunus Bicer, Deniz Erdogmus

CoRR, 2023

2019

Sparse Bayesian Learning for Robust PCA: Algorithms and Analyses.

Jing Liu, Bhaskar D. Rao

IEEE Trans. Signal Process., 2019

Robust PCA via ℓ₀-ℓ₁ Regularization.

Jing Liu, Bhaskar D. Rao

IEEE Trans. Signal Process., 2019

Sparse Bayesian Learning for Robust PCA.

Jing Liu, Yacong Ding, Bhaskar Rao

Proceedings of the IEEE International Conference on Acoustics, 2019

2018

Robust Linear Regression via ℓ₀ Regularization.

Jing Liu, Pamela C. Cosman, Bhaskar D. Rao

IEEE Trans. Signal Process., 2018

2017

Sparsity regularized Principal Component Pursuit.

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.

Jing Liu, Marian Chuang, Andrew D. Chisholm, Pamela C. Cosman

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