Tongliang Liu

La La LiDAR: Large-Scale Layout Generation from LiDAR Data AAAI 2026 Select Before Use: On the Importance of Reference Model Selection in Preference Alignment ACL 2026 GUIC: Certified Graph Unlearning with Individual Fairness Guarantees AAAI 2026 Robust Learning from Noisily Labeled Long-Tailed Data via Fairness Regularizer 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 Surrogate Prompt Learning: Towards Efficient and Diverse Prompt Learning for Vision-Language Models ICML 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 LaVin-DiT: Large Vision Diffusion Transformer CVPR 2025 Jailbreaking the Non-Transferable Barrier via Test-Time Data Disguising CVPR 2025 Flow: Modularized Agentic Workflow Automation ICLR 2025 Provable Discriminative Hyperspherical Embedding for Out-of-Distribution Detection AAAI 2025 Toward Robust Non-Transferable Learning: A Survey and Benchmark IJCAI 2025 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 Layer-Aware Analysis of Catastrophic Overfitting: Revealing the Pseudo-Robust Shortcut Dependency ICML 2024 Machine Vision Therapy: Multimodal Large Language Models Can Enhance Visual Robustness via Denoising In-Context 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 Task-aware Orthogonal Sparse Network for Exploring Shared Knowledge in Continual Learning ICML 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 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 Training A Secure Model against Data-Free Model Extraction ECCV 2024 IDEAL: Influence-Driven Selective Annotations Empower In-Context Learners in Large Language Models ICLR 2024 Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting ICLR 2024 Refined Coreset Selection: Towards Minimal Coreset Size under Model Performance Constraints ICML 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 Out-of-Distribution Detection with Negative Prompts ICLR 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 Unveiling Causal Reasoning in Large Language Models: Reality or Mirage? NIPS 2024 Unicom: Universal and Compact Representation Learning for Image Retrieval 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 Moderate Coreset: A Universal Method of Data Selection for Real-world Data-efficient Deep Learning 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 Robust Weight Perturbation for Adversarial Training IJCAI 2022 Watermarking for Out-of-distribution Detection 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 CRIS: CLIP-Driven Referring Image Segmentation CVPR 2022 Towards Lightweight Black-Box Attack Against Deep Neural Networks NIPS 2022 RSA: Reducing Semantic Shift from Aggressive Augmentations for Self-supervised Learning NIPS 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 Fair Classification with Instance-dependent Label Noise CLEAR 2022 Sample Selection with Uncertainty of Losses for Learning with Noisy Labels ICLR 2022 Adversarial Robustness Through the Lens of Causality ICLR 2022 Pluralistic Image Completion with Gaussian Mixture Models NIPS 2022 Estimating Noise Transition Matrix with Label Correlations for Noisy Multi-Label Learning NIPS 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 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 Modeling Adversarial Noise for Adversarial Training 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 TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation NIPS 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 Robust early-learning: Hindering the memorization of noisy labels ICLR 2021 Part-dependent Label Noise: Towards Instance-dependent Label Noise NIPS 2020 Domain Generalization via Entropy Regularization NIPS 2020 Dual-Path Distillation: A Unified Framework to Improve Black-Box Attacks ICML 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 Sub-center ArcFace: Boosting Face Recognition by Large-scale Noisy Web Faces ECCV 2020 Generative-Discriminative Complementary Learning AAAI 2020 Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning NIPS 2020 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 Control Batch Size and Learning Rate to Generalize Well: Theoretical and Empirical Evidence NIPS 2019 An Efficient and Provable Approach for Mixture Proportion Estimation Using Linear Independence Assumption CVPR 2018 Online Heterogeneous Transfer Metric Learning IJCAI 2018 Deep Domain Generalization via Conditional Invariant Adversarial Networks ECCV 2018 Quantum Divide-and-Conquer Anchoring for Separable Non-negative Matrix Factorization IJCAI 2018 Correcting the Triplet Selection Bias for Triplet Loss ECCV 2018 Learning with Biased Complementary Labels ECCV 2018 Semantic Structure-based Unsupervised Deep Hashing IJCAI 2018 On Compressing Deep Models by Low Rank and Sparse Decomposition CVPR 2017 Understanding How Feature Structure Transfers in Transfer Learning IJCAI 2017 General Heterogeneous Transfer Distance Metric Learning via Knowledge Fragments Transfer IJCAI 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