Li Shen

Orcid: 0000-0001-5659-3464

Affiliations:
  • Tencent, Shenzhen, China
  • JD Explore Academy, Beijing, China


According to our database1, Li Shen authored at least 120 papers between 2018 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Bibliography

2024
Messages are Never Propagated Alone: Collaborative Hypergraph Neural Network for Time-Series Forecasting.
IEEE Trans. Pattern Anal. Mach. Intell., April, 2024

AdaSAM: Boosting sharpness-aware minimization with adaptive learning rate and momentum for training deep neural networks.
Neural Networks, January, 2024

Revisiting Knowledge Distillation for Autoregressive Language Models.
CoRR, 2024

2023
OMG: Towards Effective Graph Classification Against Label Noise.
IEEE Trans. Knowl. Data Eng., December, 2023

Distributionally Robust Memory Evolution With Generalized Divergence for Continual Learning.
IEEE Trans. Pattern Anal. Mach. Intell., December, 2023

Efficient Federated Learning Via Local Adaptive Amended Optimizer With Linear Speedup.
IEEE Trans. Pattern Anal. Mach. Intell., December, 2023

Prescribed Safety Performance Imitation Learning From a Single Expert Dataset.
IEEE Trans. Pattern Anal. Mach. Intell., October, 2023

Don't Be So Dense: Sparse-to-Sparse GAN Training Without Sacrificing Performance.
Int. J. Comput. Vis., October, 2023

Task-Adaptive Feature Disentanglement and Hallucination for Few-Shot Classification.
IEEE Trans. Circuits Syst. Video Technol., August, 2023

Differentiable Neural Architecture Search for Extremely Lightweight Image Super-Resolution.
IEEE Trans. Circuits Syst. Video Technol., June, 2023

Curriculum-Based Asymmetric Multi-Task Reinforcement Learning.
IEEE Trans. Pattern Anal. Mach. Intell., June, 2023

Efficient-Adam: Communication-Efficient Distributed Adam.
IEEE Trans. Signal Process., 2023

Dynamic Contrastive Distillation for Image-Text Retrieval.
IEEE Trans. Multim., 2023

Fusion of Global and Local Knowledge for Personalized Federated Learning.
Trans. Mach. Learn. Res., 2023

FedDAG: Federated DAG Structure Learning.
Trans. Mach. Learn. Res., 2023

Dynamic PDGAN: discriminator-boosted knowledge distillation for StyleGANs.
J. Electronic Imaging, 2023

Concrete Subspace Learning based Interference Elimination for Multi-task Model Fusion.
CoRR, 2023

Task-Distributionally Robust Data-Free Meta-Learning.
CoRR, 2023

Stability and Generalization of the Decentralized Stochastic Gradient Descent Ascent Algorithm.
CoRR, 2023

Winning Prize Comes from Losing Tickets: Improve Invariant Learning by Exploring Variant Parameters for Out-of-Distribution Generalization.
CoRR, 2023

Diversifying the Mixture-of-Experts Representation for Language Models with Orthogonal Optimizer.
CoRR, 2023

Learn From Model Beyond Fine-Tuning: A Survey.
CoRR, 2023

Revisiting Plasticity in Visual Reinforcement Learning: Data, Modules and Training Stages.
CoRR, 2023

Parameter Efficient Multi-task Model Fusion with Partial Linearization.
CoRR, 2023

Which mode is better for federated learning? Centralized or Decentralized.
CoRR, 2023

Efficient Federated Prompt Tuning for Black-box Large Pre-trained Models.
CoRR, 2023

AdaMerging: Adaptive Model Merging for Multi-Task Learning.
CoRR, 2023

Unlikelihood Tuning on Negative Samples Amazingly Improves Zero-Shot Translation.
CoRR, 2023

Deep Model Fusion: A Survey.
CoRR, 2023

Are Large Language Models Really Robust to Word-Level Perturbations?
CoRR, 2023

FedLALR: Client-Specific Adaptive Learning Rates Achieve Linear Speedup for Non-IID Data.
CoRR, 2023

Continual Learning From a Stream of APIs.
CoRR, 2023

MerA: Merging Pretrained Adapters For Few-Shot Learning.
CoRR, 2023

Can Linguistic Knowledge Improve Multimodal Alignment in Vision-Language Pretraining?
CoRR, 2023

Master-slave Deep Architecture for Top-K Multi-armed Bandits with Non-linear Bandit Feedback and Diversity Constraints.
CoRR, 2023

Towards Understanding the Generalizability of Delayed Stochastic Gradient Descent.
CoRR, 2023

DFedADMM: Dual Constraints Controlled Model Inconsistency for Decentralized Federated Learning.
CoRR, 2023

A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning.
CoRR, 2023

Boosting Backdoor Attack with A Learnable Poisoning Sample Selection Strategy.
CoRR, 2023

Systematic Investigation of Sparse Perturbed Sharpness-Aware Minimization Optimizer.
CoRR, 2023

Understanding How Consistency Works in Federated Learning via Stage-wise Relaxed Initialization.
CoRR, 2023

Instructed Diffuser with Temporal Condition Guidance for Offline Reinforcement Learning.
CoRR, 2023

Dynamic Sparsity Is Channel-Level Sparsity Learner.
CoRR, 2023

Are Large Kernels Better Teachers than Transformers for ConvNets?
CoRR, 2023

Learning Better with Less: Effective Augmentation for Sample-Efficient Visual Reinforcement Learning.
CoRR, 2023

Towards More Suitable Personalization in Federated Learning via Decentralized Partial Model Training.
CoRR, 2023

Prompt-Tuning Decision Transformer with Preference Ranking.
CoRR, 2023

Towards the Flatter Landscape and Better Generalization in Federated Learning under Client-level Differential Privacy.
CoRR, 2023

On Efficient Training of Large-Scale Deep Learning Models: A Literature Review.
CoRR, 2023

Quantum Imitation Learning.
CoRR, 2023

Visual Prompt Based Personalized Federated Learning.
CoRR, 2023

Graph Decision Transformer.
CoRR, 2023

AdaSAM: Boosting Sharpness-Aware Minimization with Adaptive Learning Rate and Momentum for Training Deep Neural Networks.
CoRR, 2023

OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System.
CoRR, 2023

Subspace based Federated Unlearning.
CoRR, 2023

Bag of Tricks for Effective Language Model Pretraining and Downstream Adaptation: A Case Study on GLUE.
CoRR, 2023

Enhance Local Consistency in Federated Learning: A Multi-Step Inertial Momentum Approach.
CoRR, 2023

SaFormer: A Conditional Sequence Modeling Approach to Offline Safe Reinforcement Learning.
CoRR, 2023

Enhancing Adversarial Training via Reweighting Optimization Trajectory.
Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

Towards Stable Backdoor Purification through Feature Shift Tuning.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

FlatMatch: Bridging Labeled Data and Unlabeled Data with Cross-Sharpness for Semi-Supervised Learning.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

LGViT: Dynamic Early Exiting for Accelerating Vision Transformer.
Proceedings of the 31st ACM International Conference on Multimedia, 2023

Off-policy Imitation Learning from Visual Inputs.
Proceedings of the IEEE International Conference on Robotics and Automation, 2023

Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape.
Proceedings of the International Conference on Machine Learning, 2023

Improving the Model Consistency of Decentralized Federated Learning.
Proceedings of the International Conference on Machine Learning, 2023

Learning to Learn from APIs: Black-Box Data-Free Meta-Learning.
Proceedings of the International Conference on Machine Learning, 2023

Towards One-shot Neural Combinatorial Solvers: Theoretical and Empirical Notes on the Cardinality-Constrained Case.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Harnessing Out-Of-Distribution Examples via Augmenting Content and Style.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Enhancing Fine-Tuning based Backdoor Defense with Sharpness-Aware Minimization.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Zero-shot Sharpness-Aware Quantization for Pre-trained Language Models.
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

Towards Making the Most of ChatGPT for Machine Translation.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

Merging Experts into One: Improving Computational Efficiency of Mixture of Experts.
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

MetaMix: Towards Corruption-Robust Continual Learning with Temporally Self-Adaptive Data Transformation.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Make Landscape Flatter in Differentially Private Federated Learning.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Robust Generalization Against Photon-Limited Corruptions via Worst-Case Sharpness Minimization.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Architecture, Dataset and Model-Scale Agnostic Data-free Meta-Learning.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Learning Meta Representations for Agents in Multi-Agent Reinforcement Learning.
Proceedings of the Conference on Lifelong Learning Agents, 2023

Evaluating Model-Free Reinforcement Learning toward Safety-Critical Tasks.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

FedABC: Targeting Fair Competition in Personalized Federated Learning.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

Offline Quantum Reinforcement Learning in a Conservative Manner.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
AlphaGAN: Fully Differentiable Architecture Search for Generative Adversarial Networks.
IEEE Trans. Pattern Anal. Mach. Intell., 2022

Stochastic Client Selection for Federated Learning With Volatile Clients.
IEEE Internet Things J., 2022

On Transforming Reinforcement Learning by Transformer: The Development Trajectory.
CoRR, 2022

Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUE.
CoRR, 2022

Strength-Adaptive Adversarial Training.
CoRR, 2022

Dynamic Contrastive Distillation for Image-Text Retrieval.
CoRR, 2022

SafeRL-Kit: Evaluating Efficient Reinforcement Learning Methods for Safe Autonomous Driving.
CoRR, 2022

Robust Weight Perturbation for Adversarial Training.
CoRR, 2022

Bridging Cross-Lingual Gaps During Leveraging the Multilingual Sequence-to-Sequence Pretraining for Text Generation.
CoRR, 2022

Robust Unlearnable Examples: Protecting Data Against Adversarial Learning.
CoRR, 2022

Achieving Personalized Federated Learning with Sparse Local Models.
CoRR, 2022

Meta-learning without data via Wasserstein distributionally-robust model fusion.
Proceedings of the Uncertainty in Artificial Intelligence, 2022

Boosting the Transferability of Adversarial Attacks with Reverse Adversarial Perturbation.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Make Sharpness-Aware Minimization Stronger: A Sparsified Perturbation Approach.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Safety Correction from Baseline: Towards the Risk-aware Policy in Robotics via Dual-agent Reinforcement Learning.
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022

Penalized Proximal Policy Optimization for Safe Reinforcement Learning.
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

Robust Weight Perturbation for Adversarial Training.
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

Understanding Robust Overfitting of Adversarial Training and Beyond.
Proceedings of the International Conference on Machine Learning, 2022

Improving Task-free Continual Learning by Distributionally Robust Memory Evolution.
Proceedings of the International Conference on Machine Learning, 2022

DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training.
Proceedings of the International Conference on Machine Learning, 2022

The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Robust Unlearnable Examples: Protecting Data Privacy Against Adversarial Learning.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Improving Sharpness-Aware Minimization with Fisher Mask for Better Generalization on Language Models.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2022, 2022

Meta-Learning with Less Forgetting on Large-Scale Non-Stationary Task Distributions.
Proceedings of the Computer Vision - ECCV 2022, 2022

Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Learning to Learn and Remember Super Long Multi-Domain Task Sequence.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

On the Complementarity between Pre-Training and Random-Initialization for Resource-Rich Machine Translation.
Proceedings of the 29th International Conference on Computational Linguistics, 2022

2021
UniFaceGAN: A Unified Framework for Temporally Consistent Facial Video Editing.
IEEE Trans. Image Process., 2021

Knowledge Distillation With Multi-Objective Divergence Learning.
IEEE Signal Process. Lett., 2021

DGL-GAN: Discriminator Guided Learning for GAN Compression.
CoRR, 2021

Spatial-Temporal-Fusion BNN: Variational Bayesian Feature Layer.
CoRR, 2021

Federated Causal Discovery.
CoRR, 2021

Local AdaGrad-Type Algorithm for Stochastic Convex-Concave Minimax Problems.
CoRR, 2021

Sparse Training via Boosting Pruning Plasticity with Neuroregeneration.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020
MAP Inference Via ℓ <sub>2</sub>-Sphere Linear Program Reformulation.
Int. J. Comput. Vis., 2020

Task-agnostic Temporally Consistent Facial Video Editing.
CoRR, 2020

2019
MAP Inference via L2-Sphere Linear Program Reformulation.
CoRR, 2019

2018
A Generalized Matrix Splitting Algorithm.
CoRR, 2018


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