Jing Liu - RankMe

35 papers A* 3A 1Misc 2Journal 25Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Amplification Effects in Test-Time Reinforcement Learning: Safety and Reasoning Vulnerabilities. CoRR Vanshaj Khattar, Md Rafi ur Rashid, Moumita Choudhury, Jing Liu, Toshiaki Koike-Akino, Ming Jin, Ye Wang
2026	J	`jnl`	Directional Embedding Smoothing for Robust Vision Language Models. CoRR Ye Wang, Jing Liu, Toshiaki Koike-Akino
2026	J	`jnl`	Embedding Morphology into Transformers for Cross-Robot Policy Learning. CoRR Kei Suzuki, Jing Liu, Ye Wang, Chiori Hori, Matthew Brand, Diego Romeres, Toshiaki Koike-Akino
2026	—	`conf`	Evaluating Security Policy Compliance in Infrastructure as Code Generated by Large Language Models. ISDFS Ryo Hase, Ye Wang, Toshiaki Koike-Akino, Jing Liu, Kieran Parsons, Jumpei Hato
2026	A*	`conf`	LatentLLM: Activation-Aware Transform to Multi-Head Latent Attention. AAAI Toshiaki Koike-Akino, Xiangyu Chen, Jing Liu, Ye Wang, Pu Perry Wang, Matthew Brand
2026	J	`jnl`	TTQ: Activation-Aware Test-Time Quantization to Accelerate LLM Inference On The Fly. CoRR Toshiaki Koike-Akino, Jing Liu, Ye Wang
2025	J	`jnl`	AWP: Activation-Aware Weight Pruning and Quantization with Projected Gradient Descent. CoRR Jing Liu, Toshiaki Koike-Akino, Ye Wang, Hassan Mansour, Matthew Brand
2025	A*	`conf`	Forget to Flourish: Leveraging Machine-Unlearning on Pretrained Language Models for Privacy Leakage. AAAI Md. Rafi Ur Rashid, Jing Liu, Toshiaki Koike-Akino, Ye Wang, Shagufta Mehnaz
2025	J	`jnl`	LatentLLM: Attention-Aware Joint Tensor Compression. CoRR Toshiaki Koike-Akino, Xiangyu Chen, Jing Liu, Ye Wang, Pu Wang, Matthew Brand
2025	J	`jnl`	Probabilistic Forecasting for Building Energy Systems using Time-Series Foundation Models. CoRR Young Jin Park, François G. Germain, Jing Liu, Ye Wang, Toshiaki Koike-Akino, Gordon Wichern, Navid Azizan, Christopher R. Laughman, Ankush Chakrabarty
2025	J	`jnl`	Smoothed Embeddings for Robust Language Models. CoRR Ryo Hase, Md. Rafi Ur Rashid, Ashley Lewis, Jing Liu, Toshiaki Koike-Akino, Kieran Parsons, Ye Wang
2025	J	`jnl`	TuneComp: Joint Fine-tuning and Compression for Large Foundation Models. CoRR Xiangyu Chen, Jing Liu, Ye Wang, Matthew Brand, Pu Wang, Toshiaki Koike-Akino
2025	J	`jnl`	Winning Big with Small Models: Knowledge Distillation vs. Self-Training for Reducing Hallucination in QA Agents. CoRR Ashley Lewis, Michael White, Jing Liu, Toshiaki Koike-Akino, Kieran Parsons, Ye Wang
2025	J	`jnl`	μ-MoE: Test-Time Pruning as Micro-Grained Mixture-of-Experts. CoRR Toshiaki Koike-Akino, Jing Liu, Ye Wang
2024	A*	`conf`	Analyzing Inference Privacy Risks Through Gradients In Machine Learning. CCS Zhuohang Li, Andrew Lowy, Jing Liu, Toshiaki Koike-Akino, Kieran Parsons, Bradley A. Malin, Ye Wang
2024	J	`jnl`	Analyzing Inference Privacy Risks Through Gradients in Machine Learning. CoRR Zhuohang Li, Andrew Lowy, Jing Liu, Toshiaki Koike-Akino, Kieran Parsons, Bradley A. Malin, Ye Wang
2024	J	`jnl`	Efficient Differentially Private Fine-Tuning of Diffusion Models. CoRR Jing Liu, Andrew Lowy, Toshiaki Koike-Akino, Kieran Parsons, Ye Wang
2024	J	`jnl`	Exploring User-level Gradient Inversion with a Diffusion Prior. CoRR Zhuohang Li, Andrew Lowy, Jing Liu, Toshiaki Koike-Akino, Bradley A. Malin, Kieran Parsons, Ye Wang
2024	J	`jnl`	Forget to Flourish: Leveraging Machine-Unlearning on Pretrained Language Models for Privacy Leakage. CoRR Md. Rafi Ur Rashid, Jing Liu, Toshiaki Koike-Akino, Shagufta Mehnaz, Ye Wang
2024	J	`jnl`	GPT Sonograpy: Hand Gesture Decoding from Forearm Ultrasound Images via VLM. CoRR Keshav Bimbraw, Ye Wang, Jing Liu, Toshiaki Koike-Akino
2024	J	`jnl`	Quantum Diffusion Models for Few-Shot Learning. CoRR Ruhan Wang, Ye Wang, Jing Liu, Toshiaki Koike-Akino
2024	—	`conf`	Random Channel Ablation for Robust Hand Gesture Classification with Multimodal Biosignals. EMBC Keshav Bimbraw, Jing Liu, Ye Wang, Toshiaki Koike-Akino
2024	J	`jnl`	Random Channel Ablation for Robust Hand Gesture Classification with Multimodal Biosignals. CoRR Keshav Bimbraw, Jing Liu, Ye Wang, Toshiaki Koike-Akino
2024	—	`conf`	SuperLoRA: Parameter-Efficient Unified Adaptation for Large Vision Models. CVPR Workshops Xiangyu Chen, Jing Liu, Ye Wang, Pu Perry Wang, Matthew Brand, Guanghui Wang, Toshiaki Koike-Akino
2024	A	`conf`	SuperLoRA: Parameter-Efficient Unified Adaptation of Large Foundation Models. BMVC Xiangyu Chen, Jing Liu, Ye Wang, Pu Perry Wang, Matthew Brand, Guanghui Wang, Toshiaki Koike-Akino
2024	J	`jnl`	SuperLoRA: Parameter-Efficient Unified Adaptation of Multi-Layer Attention Modules. CoRR Xiangyu Chen, Jing Liu, Ye Wang, Pu Perry Wang, Matthew Brand, Guanghui Wang, Toshiaki Koike-Akino
2024	J	`jnl`	Variational Randomized Smoothing for Sample-Wise Adversarial Robustness. CoRR Ryo Hase, Ye Wang, Toshiaki Koike-Akino, Jing Liu, Kieran Parsons
2024	J	`jnl`	Why Does Differential Privacy with Large Epsilon Defend Against Practical Membership Inference Attacks? CoRR Andrew Lowy, Zhuohang Li, Jing Liu, Toshiaki Koike-Akino, Kieran Parsons, Ye Wang
2023	J	`jnl`	Stabilizing Subject Transfer in EEG Classification with Divergence Estimation. CoRR Niklas Smedemark-Margulies, Ye Wang, Toshiaki Koike-Akino, Jing Liu, Kieran Parsons, Yunus Bicer, Deniz Erdogmus
2019	J	`jnl`	Robust PCA via ℓ IEEE Trans. Signal Process. Jing Liu, Bhaskar D. Rao
2019	Misc	`conf`	Sparse Bayesian Learning for Robust PCA. ICASSP Jing Liu, Yacong Ding, Bhaskar Rao
2019	J	`jnl`	Sparse Bayesian Learning for Robust PCA: Algorithms and Analyses. IEEE Trans. Signal Process. Jing Liu, Bhaskar D. Rao
2018	J	`jnl`	Robust Linear Regression via ℓ IEEE Trans. Signal Process. Jing Liu, Pamela C. Cosman, Bhaskar D. Rao
2017	Misc	`conf`	Sparsity regularized Principal Component Pursuit. ICASSP Jing Liu, Pamela C. Cosman, Bhaskar D. Rao
2015	—	`conf`	Image registration robust to sparse large errors. EMBC Jing Liu, Marian Chuang, Andrew D. Chisholm, Pamela C. Cosman