Cover Image

Machine unlearning through fine-grained model parameters perturbation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zuo, Zhiwei, Tang, Zhuo, Li, Kenli, Datta, Anwitaman
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

eCIL-MU: Embedding based Class Incremental Learning and Machine Unlearning
by: Zuo, Zhiwei, et al.
Published: (2024)

Provable unlearning in topic modeling and downstream tasks
by: Wei, Stanley, et al.
Published: (2024)

Applying sparse autoencoders to unlearn knowledge in language models
by: Farrell, Eoin, et al.
Published: (2024)

Training data attribution in diffusion models via mirrored unlearning and noise-consistent skew
by: Serrà, Joan, et al.
Published: (2026)

On the limitation of evaluating machine unlearning using only a single training seed
by: Lanyon, Jamie, et al.
Published: (2025)

Zero-knowledge LLM hallucination detection and mitigation through fine-grained cross-model consistency
by: Goel, Aman, et al.
Published: (2025)

fMRI2GES: Co-speech Gesture Reconstruction from fMRI Signal with Dual Brain Decoding Alignment
by: Zhu, Chunzheng, et al.
Published: (2025)

Learning from models beyond fine-tuning
by: Zheng, Hongling, et al.
Published: (2023)

Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models
by: Ma, Olivia, et al.
Published: (2024)

The power of fine-grained experts: Granularity boosts expressivity in Mixture of Experts
by: Boix-Adsera, Enric, et al.
Published: (2025)

The devil is in the fine-grained details: Evaluating open-vocabulary object detectors for fine-grained understanding
by: Bianchi, Lorenzo, et al.
Published: (2023)

GCAM: Gaussian and causal-attention model of food fine-grained recognition
by: Zhuang, Guohang, et al.
Published: (2024)

Scores as Actions: a framework of fine-tuning diffusion models by continuous-time reinforcement learning
by: Zhao, Hanyang, et al.
Published: (2024)

Dual-Prototype Disentanglement: A Context-Aware Enhancement Framework for Time Series Forecasting
by: Yang, Haonan, et al.
Published: (2026)

FedLion: Faster Adaptive Federated Optimization with Fewer Communication
by: Tang, Zhiwei, et al.
Published: (2024)

DMSC: Dynamic Multi-Scale Coordination Framework for Time Series Forecasting
by: Yang, Haonan, et al.
Published: (2025)

Zeroth-Order Optimization Meets Human Feedback: Provable Learning via Ranking Oracles
by: Tang, Zhiwei, et al.
Published: (2023)

FLoRA: Fused forward-backward adapters for parameter efficient fine-tuning and reducing inference-time latencies of LLMs
by: Gowda, Dhananjaya, et al.
Published: (2025)

GeoLoRA: Geometric integration for parameter efficient fine-tuning
by: Schotthöfer, Steffen, et al.
Published: (2024)

DeepSeek vs. ChatGPT vs. Claude: A Comparative Study for Scientific Computing and Scientific Machine Learning Tasks
by: Jiang, Qile, et al.
Published: (2025)

Contextualizing biological perturbation experiments through language
by: Wu, Menghua, et al.
Published: (2025)

Power Plays: Unleashing Machine Learning Magic in Smart Grids
by: Rashid, Abdur, et al.
Published: (2024)

UDiTQC: U-Net-Style Diffusion Transformer for Quantum Circuit Synthesis
by: Chen, Zhiwei, et al.
Published: (2025)

Improving fine-grained understanding in image-text pre-training
by: Bica, Ioana, et al.
Published: (2024)

AMIR: Automated MisInformation Rebuttal -- A COVID-19 Vaccination Datasets based Recommendation System
by: Sharma, Shakshi, et al.
Published: (2023)

LightMoE: Reducing Mixture-of-Experts Redundancy through Expert Replacing
by: Hao, Jiawei, et al.
Published: (2026)

An Efficient NAS-based Approach for Handling Imbalanced Datasets
by: Yao, Zhiwei
Published: (2024)

Demographic-aware fine-grained visual recognition of pediatric wrist pathologies
by: Ahmed, Ammar, et al.
Published: (2025)

Identifying biological perturbation targets through causal differential networks
by: Wu, Menghua, et al.
Published: (2024)

Open-weight genome language model safeguards: Assessing robustness via adversarial fine-tuning
by: Black, James R. M., et al.
Published: (2025)

Driving Intelligent IoT Monitoring and Control through Cloud Computing and Machine Learning
by: Li, Hanzhe, et al.
Published: (2024)

Inference-Time Alignment of Diffusion Models with Direct Noise Optimization
by: Tang, Zhiwei, et al.
Published: (2024)

LIFT: Interpretable truck driving risk prediction with literature-informed fine-tuned LLMs
by: Hu, Xiao, et al.
Published: (2025)

Golden Layers and Where to Find Them: Improved Knowledge Editing for Large Language Models Via Layer Gradient Analysis
by: Datta, Shrestha, et al.
Published: (2026)

Informed along the road: roadway capacity driven graph convolution network for network-wide traffic prediction
by: Bian, Zilin, et al.
Published: (2024)

Self-Indexing KVCache: Predicting Sparse Attention from Compressed Keys
by: Yang, Xu, et al.
Published: (2026)

Batch-in-Batch: a new adversarial training framework for initial perturbation and sample selection
by: Wu, Yinting, et al.
Published: (2024)

Rethinking harmless refusals when fine-tuning foundation models
by: Pop, Florin, et al.
Published: (2024)

Improving Noise Robustness through Abstractions and its Impact on Machine Learning
by: Ibias, Alfredo, et al.
Published: (2024)

Evaluating Explainability in Machine Learning Predictions through Explainer-Agnostic Metrics
by: Munoz, Cristian, et al.
Published: (2023)