CS6604 Spring 2021 paper list

Active Embedding Search via Noisy Paired Comparisonsactive learning
Batch Decorrelation for Active Metric Learningactive learning
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learningactive learning
Active Ordinal Querying for Tuplewise Similarity Learningactive learning
The Sample Complexity of Best-k Items Selection from Pairwise Comparisonsactive learning
Fair Active Learningactive learning
bias and fairness
Crowd Teaching with Imperfect Labelsactive learning
weak supervision
Asking the Right Questions to the Right Users: Active Learning with Imperfect Oraclesactive learning
weak supervision
Generative Adversarial Active Learning for Unsupervised Outlier Detectionactive learning
outlier & OoD detection
Semi-Supervised Sequence Modeling with Cross-View Trainingsemi-supervised
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning resultssemi-supervised
Fixmatch: Simplifying semi-supervised learning with consistency and confidencesemi-supervised
Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learningsemi-supervised
Semi-Supervised Learning With Scarce Annotationssemi-supervised
Benchmarking Semi-supervised Federated Learningsemi-supervised
VT FeatMatch: Feature-Based Augmentationfor Semi-Supervised Learningsemi-supervised
data augmentation
Unsupervised Data Augmentation for Consistency Trainingsemi-supervised
data augmentation
Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learningsemi-supervised
adversarial training
Rethinking the Value of Labels for Improving Class-Imbalanced Learningsemi-supervised
class imbalance
VT Stochastic Generalized Adversarial Label Learningweak supervision
VT Self-Paced Robust Learning for Leveraging Clean Labels in Noisy Dataweak supervision
robustness & generalization
Snorkel: Rapid Training Data Creation with Weak Supervisionweak supervision
Data Programming Using Continuous and Quality-Guided Labeling Functionsweak supervision
Meta Label Correction for Noisy Label Learningweak supervision
Does label smoothing mitigate label noise?weak supervision
Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languagesweak supervision
Robust Distant Supervision Relation Extraction via Deep Reinforcement Learningweak supervision
Partial Label Learning with Batch Label Correctionweak supervision
data augmentation
VT Self-Supervised Hyperboloid Representations from Logical Queries over Knowledge Graphsself-supervision
Bootstrap Your Own LatentA New Approach to Self-Supervised Learningself-supervision
Contrastive Multi-View Representation Learning on Graphsself-supervision
Self-supervised Learning from a Multi-view Perspectiveself-supervision
Self-Supervised Learning of Pretext-Invariant Representationsself-supervision
Supervised Contrastive Learningself-supervision
Graph Contrastive Learning with Augmentationsself-supervision
data augmentation
Adversarial Self-Supervised Contrastive Learningself-supervision
adversarial training
SelfAugment: Automatic Augmentation Policies for Self-Supervised Learningdata augmentation
KeepAugment: A Simple Information-Preserving Data Augmentation Approachdata augmentation
AutoAugment: Learning Augmentation Policies from Datadata augmentation
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Featuresdata augmentation
Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasksdata augmentation
Implicit Semantic Data Augmentation for Deep Networksdata augmentation
Adversarial Training for Free!adversarial training
Smooth Adversarial Trainingadversarial training
Transferable Adversarial Training:A General Approach to Adapting Deep Classifiersadversarial training
Adversarial Training and Provable Defenses: Bridging the Gapadversarial training
Distributionally Adversarial Attackadversarial training
Adversarial Policies: Attacking Deep Reinforcement Learningadversarial training
Disentangling Adversarial Robustness and Generalizationadversarial training
robustness & generalization
On the Connection Between Adversarial Robustness and Saliency Map Interpretabilityadversarial training interpretability
robustness & generalization
Structured Adversarial Attack: Towards General Implementation and Better Interpretabilityadversarial training interpretability
VT Interpretable Event Detection and Extraction using Multi-Aspect Attentioninterpretability
A Benchmark for Interpretability Methods in DeepNeural Networksinterpretability
Causal Interpretability for Machine Learning - Problems, Methods and Evaluationinterpretability
Grad-CAM: Visual Explanations From Deep Networks via Gradient-Based Localizationinterpretability
ProtoAttend: Attention-Based Prototypical Learninginterpretability
Robustness in Machine Learning Explanations: Does It Matter?interpretability
robustness & generalization
Measuring Robustness to Natural Distribution Shifts in Image Classificationrobustness & generalization
Domain Generalization using Causal Matchingrobustness & generalization
Coping with Label Shift via Distributionally Robust Optimisationrobustness & generalization
Self-Challenging Improves Cross-Domain Generalizationrobustness & generalization
Multi-Object Representation Learning with Iterative Variational Inferencerobustness & generalization
Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalizationrobustness & generalization
Faking Fairness via Stealthily Biased Samplingbias and fairness
Robust Optimization for Fairnesswith Noisy Protected Groupsbias and fairness
Socially Responsible AI Algorithms:Issues, Purposes, and Challengesbias and fairness
Neutralizing Self-Selection Bias inSampling for Sortitionbias and fairness
Learning from Positive and Unlabeled Data with a Selection Biasbias and fairness
Counterfactual Fairnessbias and fairness
Equality of Opportunity in Supervised Learningbias and fairness
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingsbias and fairness
Language (Technology) is Power: A Critical Survey of “Bias” in NLPbias and fairness
Verifying Individual Fairness in Machine Learning Modelsbias and fairness
Biased Gamesbias and fairness
Class-Balanced Loss Based on Effective Number of Samplesclass imbalance
Dice Loss for Data-imbalanced NLP Tasksclass imbalance
ADASYN: Adaptive synthetic sampling approach for imbalanced learningclass imbalance
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Lossclass imbalance
Striking the Right Balance with Uncertaintyclass imbalance
Distribution-Balanced Loss for Multi-LabelClassification in Long-Tailed Datasetsclass imbalance
Learning to Segment the Tailclass imbalance
M2m: Imbalanced Classification via Major-to-minor Translationclass imbalance
VT Multidimensional Uncertainty-Aware Evidential Neural Networksoutlier & OoD detection
Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networksoutlier & OoD detection
SUOD: Toward Scalable Unsupervised Outlier Detectionoutlier & OoD detection
Deep Setsoutlier & OoD detection
Automating Outlier Detection via Meta-Learningoutlier & OoD detection
Deep anomaly detection with outlier exposureoutlier & OoD detection
Further Analysis of Outlier Detection with Deep Generative Modelsoutlier & OoD detection
Energy-based Out-of-distribution Detectionoutlier & OoD detection
Outlier Exposure with Confidence Control for Out-of-Distribution Detectionoutlier & OoD detection
Explainable Deep One-Class Classificationoutlier & OoD detection interpretability
Semi-Supervised Learning under Class Distribution Mismatchoutlier & OoD detection interpretability
Unsupervised Data Imputation via Variational Inference of Deep Subspacesmissing values/attributes
Missing Data Imputation using Optimal Transportmissing values/attributes
Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projectionsmissing values/attributes
Why Not to Use Zero Imputation? Correcting Sparsity Bias in Training Neural Networksmissing values/attributes
Multivariate Time Series Imputation with Generative Adversarial Networksmissing values/attributes
MCFlow: Monte Carlo Flow Models for Data Imputationmissing values/attributes
Learning on Attribute-Missing Graphsmissing values/attributes
Handling Missing Data with Graph Representation Learningmissing values/attributes
Inductive Matrix Completion Based on Graph Neural Networksmissing values/attributes