← Authors

Tongliang Liu

189 papers · 2015–2026 · 11 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (12) πŸƒ Academic Marathon (10) 🌍 Conference Polyglot (11) 🌈 Renaissance Researcher (5) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (25) 🏠 Conference Loyalist (42) πŸ† Keyword Champion (10) πŸ‘‘ Triple Crown πŸ† Grand Slam πŸ”¬ Deep Specialist (35) 🀝 Dynamic Duo (90) πŸš€ Conference Pioneer ⚑ Prolific Year (18) πŸ”₯ Unstoppable (11) ❓ The Questioner (5) πŸ’Ž Century Club (186) πŸ—ƒοΈ Keyword Collector (90) πŸ“ˆ Trend Setter

Conferences

ICLR (44) ICML (44) NIPS (42) CVPR (20) IJCAI (11) ICCV (10) AAAI (9) ECCV (5) JMLR (2) ACL (1) CLEAR (1)

Top co-authors

Bo Han (90) Mingming Gong (39) Gang Niu (29) Dacheng Tao (27) Xiaobo Xia (25) Masashi Sugiyama (22) Kun Zhang (21) Feng Liu (17) Nannan Wang (16) Jun Yu (15)

Research topics

Privacy (1)

Keywords

label noise (23) noisy label (14) adversarial robustness (12) domain adaptation (11) noisy label learning (10) out-of-distribution detection (9) adversarial training (9) representation learning (9) deep neural network (8) transition matrix (8) transfer learning (7) contrastive learning (7) distribution shift (7) domain generalization (6) instance-dependent noise (6) causal inference (5) generative model (5) semi-supervised learning (5) few-shot learning (5) neural network (5)

Papers

La La LiDAR: Large-Scale Layout Generation from LiDAR Data AAAI 2026
Robust Learning from Noisily Labeled Long-Tailed Data via Fairness Regularizer AAAI 2026
GUIC: Certified Graph Unlearning with Individual Fairness Guarantees AAAI 2026
Label Distribution Learning with Biased Annotations Assisted by Multi-Label Learning IJCAI 2025
From Debate to Equilibrium: Belief-Driven Multi-Agent LLM Reasoning via Bayesian Nash Equilibrium ICML 2025
Instance-dependent Early Stopping ICLR 2025
Understanding and Enhancing the Transferability of Jailbreaking Attacks ICLR 2025
Efficient and Trustworthy Causal Discovery with Latent Variables and Complex Relations ICLR 2025
Recovery of Causal Graph Involving Latent Variables via Homologous Surrogates ICLR 2025
Chain-of-Focus Prompting: Leveraging Sequential Visual Cues to Prompt Large Autoregressive Vision Models ICLR 2025
Towards Effective Evaluations and Comparisons for LLM Unlearning Methods ICLR 2025
Towards Out-of-Modal Generalization without Instance-level Modal Correspondence ICLR 2025
DEEM: Diffusion models serve as the eyes of large language models for image perception ICLR 2025
Noisy Test-Time Adaptation in Vision-Language Models ICLR 2025
Learning Graph Invariance by Harnessing Spuriosity ICLR 2025
A Robust Method to Discover Causal or Anticausal Relation ICLR 2025
Flow: Modularized Agentic Workflow Automation ICLR 2025
When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need ICML 2025
A Lens into Interpretable Transformer Mistakes via Semantic Dependency ICML 2025
Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning ICML 2025
Ranked from Within: Ranking Large Multimodal Models Without Labels ICML 2025
A Sample Efficient Conditional Independence Test in the Presence of Discretization ICML 2025
Surrogate Prompt Learning: Towards Efficient and Diverse Prompt Learning for Vision-Language Models ICML 2025
Jailbreaking the Non-Transferable Barrier via Test-Time Data Disguising CVPR 2025
LaVin-DiT: Large Vision Diffusion Transformer CVPR 2025
Toward Robust Non-Transferable Learning: A Survey and Benchmark IJCAI 2025
Provable Discriminative Hyperspherical Embedding for Out-of-Distribution Detection AAAI 2025
Unveiling Causal Reasoning in Large Language Models: Reality or Mirage? NIPS 2024
Discovery of the Hidden World with Large Language Models NIPS 2024
NoiseGPT: Label Noise Detection and Rectification through Probability Curvature NIPS 2024
Learning the Latent Causal Structure for Modeling Label Noise NIPS 2024
Decomposed Prompt Decision Transformer for Efficient Unseen Task Generalization NIPS 2024
Machine Vision Therapy: Multimodal Large Language Models Can Enhance Visual Robustness via Denoising In-Context Learning ICML 2024
Task-aware Orthogonal Sparse Network for Exploring Shared Knowledge in Continual Learning ICML 2024
E2HQV: High-Quality Video Generation from Event Camera via Theory-Inspired Model-Aided Deep Learning AAAI 2024
Exploring Channel-Aware Typical Features for Out-of-Distribution Detection AAAI 2024
One-Shot Learning as Instruction Data Prospector for Large Language Models ACL 2024
Envisioning Outlier Exposure by Large Language Models for Out-of-Distribution Detection ICML 2024
Improving Accuracy-robustness Trade-off via Pixel Reweighted Adversarial Training ICML 2024
Refined Coreset Selection: Towards Minimal Coreset Size under Model Performance Constraints ICML 2024
Enhanced Motion-Text Alignment for Image-to-Video Transfer Learning CVPR 2024
Your Transferability Barrier is Fragile: Free-Lunch for Transferring the Non-Transferable Learning CVPR 2024
Towards Realistic Model Selection for Semi-supervised Learning ICML 2024
Early Stopping Against Label Noise Without Validation Data ICLR 2024
Enhancing Contrastive Learning for Ordinal Regression via Ordinal Content Preserved Data Augmentation ICLR 2024
Causal Structure Recovery with Latent Variables under Milder Distributional and Graphical Assumptions ICLR 2024
IDEAL: Influence-Driven Selective Annotations Empower In-Context Learners in Large Language Models ICLR 2024
Training A Secure Model against Data-Free Model Extraction ECCV 2024
Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting ICLR 2024
Out-of-Distribution Detection with Negative Prompts ICLR 2024
Mitigating Label Noise on Graphs via Topological Sample Selection ICML 2024
Unraveling the Impact of Heterophilic Structures on Graph Positive-Unlabeled Learning ICML 2024
Optimal Kernel Choice for Score Function-based Causal Discovery ICML 2024
MOKD: Cross-domain Finetuning for Few-shot Classification via Maximizing Optimized Kernel Dependence ICML 2024
Layer-Aware Analysis of Catastrophic Overfitting: Revealing the Pseudo-Robust Shortcut Dependency ICML 2024
On the Over-Memorization During Natural, Robust and Catastrophic Overfitting ICLR 2024
NoiseDiffusion: Correcting Noise for Image Interpolation with Diffusion Models beyond Spherical Linear Interpolation ICLR 2024
Federated Causal Discovery from Heterogeneous Data ICLR 2024
Neural Auto-designer for Enhanced Quantum Kernels ICLR 2024
FedImpro: Measuring and Improving Client Update in Federated Learning ICLR 2024
Robust Training of Federated Models with Extremely Label Deficiency ICLR 2024
Negative Label Guided OOD Detection with Pretrained Vision-Language Models ICLR 2024
Improving Non-Transferable Representation Learning by Harnessing Content and Style ICLR 2024
Identifiability and Asymptotics in Learning Homogeneous Linear ODE Systems from Discrete Observations JMLR 2024
Few-Shot Adversarial Prompt Learning on Vision-Language Models NIPS 2024
Mind the Gap Between Prototypes and Images in Cross-domain Finetuning NIPS 2024
What If the Input is Expanded in OOD Detection? NIPS 2024
Pseudo-Private Data Guided Model Inversion Attacks NIPS 2024
In-N-Out: Lifting 2D Diffusion Prior for 3D Object Removal via Tuning-Free Latents Alignment NIPS 2024
Moderate Coreset: A Universal Method of Data Selection for Real-world Data-efficient Deep Learning ICLR 2023
Defending against Data-Free Model Extraction by Distributionally Robust Defensive Training NIPS 2023
InstanT: Semi-supervised Learning with Instance-dependent Thresholds NIPS 2023
FlatMatch: Bridging Labeled Data and Unlabeled Data with Cross-Sharpness for Semi-Supervised Learning NIPS 2023
Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation NIPS 2023
CS-Isolate: Extracting Hard Confident Examples by Content and Style Isolation NIPS 2023
FedFed: Feature Distillation against Data Heterogeneity in Federated Learning NIPS 2023
An Efficient Dataset Condensation Plugin and Its Application to Continual Learning NIPS 2023
Eliminating Catastrophic Overfitting Via Abnormal Adversarial Examples Regularization NIPS 2023
Subclass-Dominant Label Noise: A Counterexample for the Success of Early Stopping NIPS 2023
Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources NIPS 2023
Towards Label-free Scene Understanding by Vision Foundation Models NIPS 2023
BiCro: Noisy Correspondence Rectification for Multi-Modality Data via Bi-Directional Cross-Modal Similarity Consistency CVPR 2023
Robust Generalization Against Photon-Limited Corruptions via Worst-Case Sharpness Minimization CVPR 2023
Architecture, Dataset and Model-Scale Agnostic Data-Free Meta-Learning CVPR 2023
DeepSolo: Let Transformer Decoder With Explicit Points Solo for Text Spotting CVPR 2023
Late Stopping: Avoiding Confidently Learning from Mislabeled Examples ICCV 2023
Point-Query Quadtree for Crowd Counting, Localization, and More ICCV 2023
ALIP: Adaptive Language-Image Pre-Training with Synthetic Caption ICCV 2023
HumanMAC: Masked Motion Completion for Human Motion Prediction ICCV 2023
PADDLES: Phase-Amplitude Spectrum Disentangled Early Stopping for Learning with Noisy Labels ICCV 2023
Multiscale Representation for Real-Time Anti-Aliasing Neural Rendering ICCV 2023
Combating Noisy Labels with Sample Selection by Mining High-Discrepancy Examples ICCV 2023
Holistic Label Correction for Noisy Multi-Label Classification ICCV 2023
Unicom: Universal and Compact Representation Learning for Image Retrieval ICLR 2023
Combating Exacerbated Heterogeneity for Robust Models in Federated Learning ICLR 2023
Contextual Convolutional Networks ICLR 2023
Out-of-distribution Detection with Implicit Outlier Transformation ICLR 2023
Harnessing Out-Of-Distribution Examples via Augmenting Content and Style ICLR 2023
Symmetric Pruning in Quantum Neural Networks ICLR 2023
Mosaic Representation Learning for Self-supervised Visual Pre-training ICLR 2023
A Holistic View of Label Noise Transition Matrix in Deep Learning and Beyond ICLR 2023
Evolving Semantic Prototype Improves Generative Zero-Shot Learning ICML 2023
Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation ICML 2023
Detecting Out-of-distribution Data through In-distribution Class Prior ICML 2023
A Universal Unbiased Method for Classification from Aggregate Observations ICML 2023
Which is Better for Learning with Noisy Labels: The Semi-supervised Method or Modeling Label Noise? ICML 2023
Eliminating Adversarial Noise via Information Discard and Robust Representation Restoration ICML 2023
Phase-aware Adversarial Defense for Improving Adversarial Robustness ICML 2023
Exploring Model Dynamics for Accumulative Poisoning Discovery ICML 2023
Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability ICML 2023
Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities IJCAI 2023
Modeling Adversarial Noise for Adversarial Training ICML 2022
Towards Lightweight Black-Box Attack Against Deep Neural Networks NIPS 2022
Robust Weight Perturbation for Adversarial Training IJCAI 2022
RSA: Reducing Semantic Shift from Aggressive Augmentations for Self-supervised Learning NIPS 2022
Instance-Dependent Label-Noise Learning With Manifold-Regularized Transition Matrix Estimation CVPR 2022
Killing Two Birds With One Stone: Efficient and Robust Training of Face Recognition CNNs by Partial FC CVPR 2022
Learning from Noisy Pairwise Similarity and Unlabeled Data JMLR 2022
Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs NIPS 2022
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks NIPS 2022
Pluralistic Image Completion with Gaussian Mixture Models NIPS 2022
Fair Classification with Instance-dependent Label Noise CLEAR 2022
CRIS: CLIP-Driven Referring Image Segmentation CVPR 2022
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels ICLR 2022
Estimating Noise Transition Matrix with Label Correlations for Noisy Multi-Label Learning NIPS 2022
Adversarial Robustness Through the Lens of Causality ICLR 2022
Exploiting Class Activation Value for Partial-Label Learning ICLR 2022
Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations ICLR 2022
Rethinking Class-Prior Estimation for Positive-Unlabeled Learning ICLR 2022
Meta Discovery: Learning to Discover Novel Classes given Very Limited Data ICLR 2022
Counterfactual Fairness with Partially Known Causal Graph NIPS 2022
MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models NIPS 2022
Out-of-Distribution Detection with An Adaptive Likelihood Ratio on Informative Hierarchical VAE NIPS 2022
Class-Dependent Label-Noise Learning with Cycle-Consistency Regularization NIPS 2022
Watermarking for Out-of-distribution Detection NIPS 2022
Understanding and Improving Graph Injection Attack by Promoting Unnoticeability ICLR 2022
Reliable Adversarial Distillation with Unreliable Teachers ICLR 2022
Exploring Set Similarity for Dense Self-Supervised Representation Learning CVPR 2022
SimT: Handling Open-Set Noise for Domain Adaptive Semantic Segmentation CVPR 2022
Selective-Supervised Contrastive Learning With Noisy Labels CVPR 2022
Mutual Quantization for Cross-Modal Search With Noisy Labels CVPR 2022
To Smooth or Not? When Label Smoothing Meets Noisy Labels ICML 2022
Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network ICML 2022
Understanding Robust Overfitting of Adversarial Training and Beyond ICML 2022
Improving Adversarial Robustness via Mutual Information Estimation ICML 2022
Understanding and Improving Early Stopping for Learning with Noisy Labels NIPS 2021
Removing Adversarial Noise in Class Activation Feature Space ICCV 2021
A Second-Order Approach to Learning With Instance-Dependent Label Noise CVPR 2021
Revisiting Knowledge Distillation: An Inheritance and Exploration Framework CVPR 2021
Confidence Scores Make Instance-dependent Label-noise Learning Possible ICML 2021
Learning Diverse-Structured Networks for Adversarial Robustness ICML 2021
Maximum Mean Discrepancy Test is Aware of Adversarial Attacks ICML 2021
Provably End-to-end Label-noise Learning without Anchor Points ICML 2021
Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels ICML 2021
Towards Defending against Adversarial Examples via Attack-Invariant Features ICML 2021
Instance-dependent Label-noise Learning under a Structural Causal Model NIPS 2021
Confident Anchor-Induced Multi-Source Free Domain Adaptation NIPS 2021
Learning with Group Noise AAAI 2021
Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model AAAI 2021
Me-Momentum: Extracting Hard Confident Examples From Noisily Labeled Data ICCV 2021
Probabilistic Margins for Instance Reweighting in Adversarial Training NIPS 2021
TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation NIPS 2021
Robust early-learning: Hindering the memorization of noisy labels ICLR 2021
Dual-Path Distillation: A Unified Framework to Improve Black-Box Attacks ICML 2020
Generative-Discriminative Complementary Learning AAAI 2020
Sub-center ArcFace: Boosting Face Recognition by Large-scale Noisy Web Faces ECCV 2020
Label-Noise Robust Domain Adaptation ICML 2020
LTF: A Label Transformation Framework for Correcting Label Shift ICML 2020
Learning with Bounded Instance and Label-dependent Label Noise ICML 2020
Domain Generalization via Entropy Regularization NIPS 2020
Part-dependent Label Noise: Towards Instance-dependent Label Noise NIPS 2020
Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning NIPS 2020
Control Batch Size and Learning Rate to Generalize Well: Theoretical and Empirical Evidence NIPS 2019
Positive and Unlabeled Learning with Label Disambiguation IJCAI 2019
Are Anchor Points Really Indispensable in Label-Noise Learning? NIPS 2019
DistillHash: Unsupervised Deep Hashing by Distilling Data Pairs CVPR 2019
Quantum Divide-and-Conquer Anchoring for Separable Non-negative Matrix Factorization IJCAI 2018
Correcting the Triplet Selection Bias for Triplet Loss ECCV 2018
Deep Domain Generalization via Conditional Invariant Adversarial Networks ECCV 2018
Learning with Biased Complementary Labels ECCV 2018
Online Heterogeneous Transfer Metric Learning IJCAI 2018
Semantic Structure-based Unsupervised Deep Hashing IJCAI 2018
An Efficient and Provable Approach for Mixture Proportion Estimation Using Linear Independence Assumption CVPR 2018
Understanding How Feature Structure Transfers in Transfer Learning IJCAI 2017
General Heterogeneous Transfer Distance Metric Learning via Knowledge Fragments Transfer IJCAI 2017
On Compressing Deep Models by Low Rank and Sparse Decomposition CVPR 2017
Algorithmic Stability and Hypothesis Complexity ICML 2017
Domain Adaptation with Conditional Transferable Components ICML 2016
Multi-Task Model and Feature Joint Learning IJCAI 2015