Tongliang Liu

Tongliang Liu's AcademicInfluence.com Rankings

Tongliang Liu

Computer Science

#8954

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#9410

Historical Rank

Machine Learning

#3798

World Rank

#3844

Historical Rank

Database

#5947

World Rank

#6166

Historical Rank

computer-science Degrees

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Computer Science

Tongliang Liu's Degrees

PhD Computer Science Stanford University
Masters Computer Science Stanford University
Bachelors Computer Science Tsinghua University

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Tongliang Liu's Published Works

Number of citations in a given year to any of this author's works

Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author

Published Works

Classification with Noisy Labels by Importance Reweighting (2014) (668)
Deep Domain Generalization via Conditional Invariant Adversarial Networks (2018) (416)
On Compressing Deep Models by Low Rank and Sparse Decomposition (2017) (317)
Domain Adaptation with Conditional Transferable Components (2016) (290)
Are Anchor Points Really Indispensable in Label-Noise Learning? (2019) (205)
Fast Supervised Discrete Hashing (2018) (196)
dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs (2017) (178)
Learning with Biased Complementary Labels (2017) (137)
Spectral Ensemble Clustering via Weighted K-Means: Theoretical and Practical Evidence (2017) (132)
Unsupervised Semantic-Preserving Adversarial Hashing for Image Search (2019) (121)
Parts-dependent Label Noise: Towards Instance-dependent Label Noise (2020) (121)
Domain Generalization via Entropy Regularization (2020) (118)
Control Batch Size and Learning Rate to Generalize Well: Theoretical and Empirical Evidence (2019) (114)
Domain Generalization via Conditional Invariant Representations (2018) (112)
Robust early-learning: Hindering the memorization of noisy labels (2021) (112)
Spectral Ensemble Clustering (2015) (112)
Decomposition-Based Transfer Distance Metric Learning for Image Classification (2014) (111)
Sub-center ArcFace: Boosting Face Recognition by Large-Scale Noisy Web Faces (2020) (109)
Deformed Graph Laplacian for Semisupervised Learning (2015) (107)
Semantic Structure-based Unsupervised Deep Hashing (2018) (105)
Learning with Bounded Instance- and Label-dependent Label Noise (2017) (102)
Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning (2020) (102)
Multiview Matrix Completion for Multilabel Image Classification (2015) (100)
A Regularization Approach for Instance-Based Superset Label Learning (2018) (94)
DistillHash: Unsupervised Deep Hashing by Distilling Data Pairs (2019) (91)
Algorithm-Dependent Generalization Bounds for Multi-Task Learning (2017) (88)
The Expressive Power of Parameterized Quantum Circuits (2018) (87)
Why ResNet Works? Residuals Generalize (2019) (85)
Joint Sparse Representation and Multitask Learning for Hyperspectral Target Detection (2017) (81)
On the Performance of Manhattan Nonnegative Matrix Factorization (2016) (79)
Local Rademacher Complexity for Multi-Label Learning (2014) (79)
A Survey of Label-noise Representation Learning: Past, Present and Future (2020) (78)
Experimental Quantum Generative Adversarial Networks for Image Generation (2020) (77)
Orthogonal Deep Neural Networks (2019) (75)
Algorithmic Stability and Hypothesis Complexity (2017) (67)
Expressive power of parametrized quantum circuits (2020) (67)
Large-Cone Nonnegative Matrix Factorization (2017) (66)
Adversarial Examples for Hamming Space Search (2020) (64)
Correcting the Triplet Selection Bias for Triplet Loss (2018) (64)
Multiclass Learning With Partially Corrupted Labels (2018) (64)
Transferring Knowledge Fragments for Learning Distance Metric from a Heterogeneous Domain (2019) (64)
Supervised Discrete Hashing With Relaxation (2018) (63)
Heterogeneous Graph Attention Network for Unsupervised Multiple-Target Domain Adaptation (2020) (62)
Representative Vector Machines: A Unified Framework for Classical Classifiers (2016) (59)
A Second-Order Approach to Learning with Instance-Dependent Label Noise (2020) (58)
Two-Stream Deep Hashing With Class-Specific Centers for Supervised Image Search (2020) (56)
Truncated Cauchy Non-Negative Matrix Factorization (2019) (56)
Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations (2021) (56)
CRIS: CLIP-Driven Referring Image Segmentation (2021) (54)
Dimensionality-Dependent Generalization Bounds for k-Dimensional Coding Schemes (2016) (51)
Confidence Scores Make Instance-dependent Label-noise Learning Possible (2019) (50)
Continuous Dropout (2018) (49)
Provably End-to-end Label-Noise Learning without Anchor Points (2021) (47)
Understanding and Improving Early Stopping for Learning with Noisy Labels (2021) (45)
Quantum noise protects quantum classifiers against adversaries (2020) (44)
Algorithm-Dependent Generalization Bounds for Multi-Task Learning. (2017) (43)
Elastic Net Hypergraph Learning for Image Clustering and Semi-Supervised Classification (2016) (42)
An Efficient and Provable Approach for Mixture Proportion Estimation Using Linear Independence Assumption (2018) (42)
On the learnability of quantum neural networks (2020) (42)
Adaptive Morphological Reconstruction for Seeded Image Segmentation (2019) (40)
Loss Decomposition and Centroid Estimation for Positive and Unlabeled Learning (2019) (36)
Understanding How Feature Structure Transfers in Transfer Learning (2017) (36)
Deep Blur Mapping: Exploiting High-Level Semantics by Deep Neural Networks (2016) (36)
Large-Margin Label-Calibrated Support Vector Machines for Positive and Unlabeled Learning (2019) (34)
Positive and Unlabeled Learning with Label Disambiguation (2019) (33)
On Better Exploring and Exploiting Task Relationships in Multitask Learning: Joint Model and Feature Learning (2018) (32)
Absent Multiple Kernel Learning Algorithms (2020) (32)
No Reference Quality Assessment for Multiply-Distorted Images Based on an Improved Bag-of-Words Model (2015) (31)
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels (2021) (31)
On the robustness and generalization of Cauchy regression (2014) (31)
Multi-Task Model and Feature Joint Learning (2015) (30)
Transfer Learning with Label Noise (2017) (30)
Reliable Multi-View Clustering (2018) (28)
Domain Generalization via Conditional Invariant Representation (2018) (27)
Instance-Dependent PU Learning by Bayesian Optimal Relabeling (2018) (26)
Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels (2020) (26)
Maximum Mean Discrepancy Test is Aware of Adversarial Attacks (2020) (25)
Improving Medical Images Classification With Label Noise Using Dual-Uncertainty Estimation (2021) (25)
Generative-Discriminative Complementary Learning (2019) (25)
Selective-Supervised Contrastive Learning with Noisy Labels (2022) (24)
Label-Noise Robust Domain Adaptation (2020) (23)
Confident Anchor-Induced Multi-Source Free Domain Adaptation (2021) (22)
Adversarial Robustness through the Lens of Causality (2022) (22)
Understanding and Improving Graph Injection Attack by Promoting Unnoticeability (2022) (21)
Estimating Instance-dependent Label-noise Transition Matrix using DNNs (2021) (21)
Instance-dependent Label-noise Learning under a Structural Causal Model (2021) (20)
HRSiam: High-Resolution Siamese Network, Towards Space-Borne Satellite Video Tracking (2021) (20)
Extended T: Learning with Mixed Closed-set and Open-set Noisy Labels (2020) (20)
A Grover-search based quantum learning scheme for classification (2018) (19)
Relational Subsets Knowledge Distillation for Long-tailed Retinal Diseases Recognition (2021) (19)
Implementable Quantum Classifier for Nonlinear Data (2018) (18)
Multi-View Matrix Completion for Multi-Label Image Classification (2019) (18)
Killing Two Birds with One Stone: Efficient and Robust Training of Face Recognition CNNs by Partial FC (2022) (17)
An Information-Theoretic View for Deep Learning (2018) (17)
Eigenfunction-Based Multitask Learning in a Reproducing Kernel Hilbert Space (2019) (17)
Class2Simi: A New Perspective on Learning with Label Noise (2020) (16)
LTF: A Label Transformation Framework for Correcting Label Shift (2020) (16)
Probabilistic Margins for Instance Reweighting in Adversarial Training (2021) (15)
Exploring Set Similarity for Dense Self-supervised Representation Learning (2021) (14)
General Heterogeneous Transfer Distance Metric Learning via Knowledge Fragments Transfer (2017) (14)
A Quantum-inspired Algorithm for General Minimum Conical Hull Problems (2019) (14)
Bridging the Gap Between Few-Shot and Many-Shot Learning via Distribution Calibration (2021) (14)
Understanding the Interaction of Adversarial Training with Noisy Labels (2021) (14)
Maximum Mean Discrepancy is Aware of Adversarial Attacks (2020) (14)
Reliable Adversarial Distillation with Unreliable Teachers (2021) (13)
Invariance Principle Meets Out-of-Distribution Generalization on Graphs (2022) (13)
A Shape Transformation-based Dataset Augmentation Framework for Pedestrian Detection (2019) (13)
Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation (2022) (12)
Dual-Path Distillation: A Unified Framework to Improve Black-Box Attacks (2020) (12)
Online Heterogeneous Transfer Metric Learning (2018) (12)
Understanding (Generalized) Label Smoothing when Learning with Noisy Labels (2021) (12)
Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities (2022) (11)
Harnessing Side Information for Classification Under Label Noise (2020) (11)
Learning Diverse-Structured Networks for Adversarial Robustness (2021) (10)
Quantum Differentially Private Sparse Regression Learning (2020) (10)
Towards Defending against Adversarial Examples via Attack-Invariant Features (2021) (10)
To Smooth or Not? When Label Smoothing Meets Noisy Labels (2021) (10)
Me-Momentum: Extracting Hard Confident Examples from Noisily Labeled Data (2021) (10)
Instance-Dependent Positive and Unlabeled Learning With Labeling Bias Estimation (2021) (10)
Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model (2021) (10)
Revisiting Knowledge Distillation: An Inheritance and Exploration Framework (2021) (10)
Laplacian Welsch Regularization for Robust Semisupervised Learning (2020) (10)
Exploiting Class Activation Value for Partial-Label Learning (2022) (10)
TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation (2021) (10)
Removing Adversarial Noise in Class Activation Feature Space (2021) (9)
Quantum Divide-and-Conquer Anchoring for Separable Non-negative Matrix Factorization (2018) (9)
Local Blur Mapping: Exploiting High-Level Semantics by Deep Neural Networks (2016) (9)
Meta Discovery: Learning to Discover Novel Classes given Very Limited Data (2021) (9)
Weakly Supervised Temporal Action Localization with Segment-Level Labels (2020) (9)
KFC: An Efficient Framework for Semi-Supervised Temporal Action Localization (2021) (9)
CausalAdv: Adversarial Robustness through the Lens of Causality (2021) (8)
Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs (2022) (8)
Understanding Robust Overfitting of Adversarial Training and Beyond (2022) (8)
Multi-Class Classification from Noisy-Similarity-Labeled Data (2020) (8)
Dual Diversified Dynamical Gaussian Process Latent Variable Model for Video Repairing (2016) (7)
Instance Correction for Learning with Open-set Noisy Labels (2021) (7)
An Optimal Transport Analysis on Generalization in Deep Learning. (2021) (7)
Towards Mixture Proportion Estimation without Irreducibility (2020) (7)
Recent Advances for Quantum Neural Networks in Generative Learning (2022) (7)
Learning with Group Noise (2021) (7)
Learning relative features through adaptive pooling for image classification (2014) (7)
Efficient Bipartite Entanglement Detection Scheme with a Quantum Adversarial Solver. (2022) (6)
Video Face Editing Using Temporal-Spatial-Smooth Warping (2014) (6)
An Optimal Transport View on Generalization (2018) (6)
Towards Digital Retina in Smart Cities: A Model Generation, Utilization and Communication Paradigm (2019) (6)
COVID-MTL: Multitask learning with Shift3D and random-weighted loss for COVID-19 diagnosis and severity assessment (2020) (6)
Robust Weight Perturbation for Adversarial Training (2022) (6)
Federated Causal Discovery (2021) (6)
Rethinking Class-Prior Estimation for Positive-Unlabeled Learning (2020) (5)
Where is the Bottleneck of Adversarial Learning with Unlabeled Data? (2019) (5)
Towards Efficient Front-End Visual Sensing for Digital Retina: A Model-Centric Paradigm (2020) (5)
On the Rates of Convergence From Surrogate Risk Minimizers to the Bayes Optimal Classifier (2018) (4)
Bayesian Quantum Circuit (2018) (4)
DeepSolo: Let Transformer Decoder with Explicit Points Solo for Text Spotting (2022) (4)
Robust Angular Local Descriptor Learning (2018) (4)
Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network (2021) (4)
Fair Classification with Instance-dependent Label Noise (2022) (4)
Label Propagated Nonnegative Matrix Factorization for Clustering (2022) (4)
Truncated Cauchy Non-negative Matrix Factorization for Robust Subspace Learning (2017) (4)
NoiLIn: Improving Adversarial Training and Correcting Stereotype of Noisy Labels (2021) (4)
Transferable Coupled Network for Zero-Shot Sketch-Based Image Retrieval (2021) (3)
On the Performance of Manhattan Non-negative Matrix Factorization (2016) (3)
Symmetric Pruning in Quantum Neural Networks (2022) (3)
Harnessing Out-Of-Distribution Examples via Augmenting Content and Style (2022) (3)
Relation-Aware Fine-Grained Reasoning Network for Textbook Question Answering (2021) (3)
Modelling Adversarial Noise for Adversarial Defense (2021) (3)
Deep Heterogeneous Multi-Task Metric Learning for Visual Recognition and Retrieval (2020) (3)
Watermarking for Out-of-distribution Detection (2022) (3)
Meta Clustering Learning for Large-scale Unsupervised Person Re-identification (2021) (3)
Group Feedback Capsule Network (2020) (3)
MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models (2022) (3)
Exploring Language Hierarchy for Video Grounding (2022) (3)
Out-of-distribution Detection with Implicit Outlier Transformation (2023) (3)
LTF: A Label Transformation Framework for Correcting Target Shift (2020) (2)
Improving White-box Robustness of Pre-processing Defenses via Joint Adversarial Training (2021) (2)
Towards Lightweight Black-Box Attacks against Deep Neural Networks (2022) (2)
Counterfactual Fairness with Partially Known Causal Graph (2022) (2)
A Machine Learning Approach for Predicting Human Preference for Graph Layouts* (2021) (2)
Improving Adversarial Robustness via Mutual Information Estimation (2022) (2)
Skipping Two Layers in ResNet Makes the Generalization Gap Smaller than Skipping One or No Layer (2019) (2)
Vecnet: A Spectral and Multi-Scale Spatial Fusion Deep Network for Pixel-Level Cloud Type Classification in Himawari-8 Imagery (2021) (2)
FedDAG: Federated DAG Structure Learning (2021) (2)
NoiLIn: Do Noisy Labels Always Hurt Adversarial Training? (2021) (2)
SimT: Handling Open-set Noise for Domain Adaptive Semantic Segmentation (2022) (2)
PADDLES: Phase-Amplitude Spectrum Disentangled Early Stopping for Learning with Noisy Labels (2022) (2)
Bilateral Dependency Optimization: Defending Against Model-inversion Attacks (2022) (1)
Understanding How Pretraining Regularizes Deep Learning Algorithms. (2021) (1)
Local Rademacher Complexity for Multi-label Learning Local Rademacher Complexity for Multi-label Learning (2014) (1)
Pluralistic Image Completion with Probabilistic Mixture-of-Experts (2022) (1)
Mutual Quantization for Cross-Modal Search with Noisy Labels (2022) (1)
Robust Dual Recurrent Neural Networks for Financial Time Series Prediction (2021) (1)
MSR: Making Self-supervised learning Robust to Aggressive Augmentations (2022) (1)
Trustable Co-Label Learning From Multiple Noisy Annotators (2022) (1)
KRADA: Known-region-aware Domain Alignment for Open World Semantic Segmentation (2021) (1)
Improving Supervised Learning in Conversational Analysis through Reusing Preprocessing Data as Auxiliary Supervisors (2022) (1)
Robust Generalization against Photon-Limited Corruptions via Worst-Case Sharpness Minimization (2023) (1)
Train Me to Fight: Machine-Learning Based On-Device Malware Detection for Mobile Devices (2022) (1)
MAPS: a dataset for semantic profiling and analysis of Android applications (2022) (1)
Modeling Adversarial Noise for Adversarial Training (2021) (1)
Diversified Bayesian Nonnegative Matrix Factorization (2020) (1)
Diversified Dynamical Gaussian Process Latent Variable Model for Video Repair (2016) (1)
LR-SVM+: Learning Using Privileged Information with Noisy Labels (2022) (1)
Fairness Improves Learning from Noisily Labeled Long-Tailed Data (2023) (1)
ProtoSimi: label correction for fine-grained visual categorization (2023) (0)
Understanding and Improving Early Stopping for Learning with Noisy Labels Supplementary (2021) (0)
The complexity of algorithmic hypothesis class (2016) (0)
S AMPLE S ELECTION WITH U NCERTAINTY OF L OSSES FOR L EARNING WITH N OISY L ABELS (2022) (0)
Strength-Adaptive Adversarial Training (2022) (0)
Repulsive Mixture Models of Exponential Family PCA for Clustering (2020) (0)
Do We Need to Penalize Variance of Losses for Learning with Label Noise? (2022) (0)
Boosting Fairness for Masked Face Recognition (2021) (0)
BiCro: Noisy Correspondence Rectification for Multi-modality Data via Bi-directional Cross-modal Similarity Consistency (2023) (0)
TWGAN: Twin Discriminator Generative Adversarial Networks (2021) (0)
Handling Open-set Noise and Novel Target Recognition in Domain Adaptive Semantic Segmentation (2023) (0)
U NDERSTANDING AND I MPROVING G RAPH I NJECTION A TTACK BY P ROMOTING U NNOTICEABILITY (2022) (0)
Dynamics-Aware Loss for Learning with Label Noise (2023) (0)
Advances in data representation and learning for pattern analysis (2019) (0)
Learning from Noisy Pairwise Similarity and Unlabeled Data (2022) (0)
Not “ monkey ” Not “ prairie dog ” Not “ meerkat ” ComplementaryLabel True Label Meerkat MonkeyPrairie Dog (2018) (0)
Architecture, Dataset and Model-Scale Agnostic Data-free Meta-Learning (2023) (0)
Multi-scale Cooperative Multimodal Transformers for Multimodal Sentiment Analysis in Videos (2022) (0)
Demystifying Assumptions in Learning to Discover Novel Classes (2021) (0)
Guest Editorial: Visual Domain Adaptation and Generalisation (2019) (0)
HumanMAC: Masked Motion Completion for Human Motion Prediction (2023) (0)
1 Generalization in Classical Statistical Learning Theory (2018) (0)
Improving robustness of softmax corss-entropy loss via inference information (2021) (0)
A machine learning approach for predicting human shortest path task performance (2022) (0)
PI-GNN: A Novel Perspective on Semi-Supervised Node Classification against Noisy Labels (2021) (0)
A Machine Learning Approach for Predicting Human Preference for Graph Drawings (2022) (0)
Combating Exacerbated Heterogeneity for Robust Models in Federated Learning (2023) (0)
KRADA: Known-region-aware Domain Alignment for Open-set Domain Adaptation in Semantic Segmentation (2021) (0)
Learning and Mining with Noisy Labels (2022) (0)
Sample-Efficient Kernel Mean Estimator with Marginalized Corrupted Data (2022) (0)
Identifiability and Asymptotics in Learning Homogeneous Linear ODE Systems from Discrete Observations (2022) (0)
Unicom: Universal and Compact Representation Learning for Image Retrieval (2023) (0)
Label-Noise Robust Domain Adaptation Supplementary Material (2020) (0)
Nonlinear Multi-Model Reuse (2022) (0)
C AUSAL A DV : A DVERSARIAL R OBUSTNESS THROUGH THE L ENS OF C AUSALITY (2022) (0)
Transfer Learning in Conversational Analysis through Reusing Preprocessing Data as Supervisors (2021) (0)
Multiscale Representation for Real-Time Anti-Aliasing Neural Rendering (2023) (0)
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks (2022) (0)
Exploit CAM by itself: Complementary Learning System for Weakly Supervised Semantic Segmentation (2023) (0)
Kernel Mean Estimation by Marginalized Corrupted Distributions (2021) (0)
Erratum: Expressive power of parametrized quantum circuits [Phys. Rev. Research 2, 033125 (2020)] (2022) (0)
Erratum: Learnability of Quantum Neural Networks [PRX QUANTUM 2 , 040337 (2021)] (2022) (0)
Type-I Generative Adversarial Attack (2022) (0)

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