← Learning Types
Machine Learning › Learning Types ›

Weakly Supervised Learning

3895 directly classified papers

Papers per year

Papers

Auxiliary Losses for Learning Generalizable Concept-based Models NIPS 2023
Referring Image Segmentation Using Text Supervision ICCV 2023
SYMPTOMIFY: Transforming Symptom Annotations with Language Model Knowledge Harvesting EMNLP 2023
Error Detection for Text-to-SQL Semantic Parsing EMNLP 2023
Revisiting Foreground and Background Separation in Weakly-supervised Temporal Action Localization: A Clustering-based Approach ICCV 2023
Estimating Conditional Average Treatment Effects with Missing Treatment Information AISTATS 2023
Which is Better for Learning with Noisy Labels: The Semi-supervised Method or Modeling Label Noise? ICML 2023
The Devil Is in the Points: Weakly Semi-Supervised Instance Segmentation via Point-Guided Mask Representation CVPR 2023
Label-efficient Segmentation via Affinity Propagation NIPS 2023
B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding ICML 2023
Deep Clustering with Incomplete Noisy Pairwise Annotations: A Geometric Regularization Approach ICML 2023
Bias Mimicking: A Simple Sampling Approach for Bias Mitigation CVPR 2023
CrossSplit: Mitigating Label Noise Memorization through Data Splitting ICML 2023
GRAFENNE: Learning on Graphs with Heterogeneous and Dynamic Feature Sets ICML 2023
Weakly Supervised Referring Image Segmentation with Intra-Chunk and Inter-Chunk Consistency ICCV 2023
ALWOD: Active Learning for Weakly-Supervised Object Detection ICCV 2023
Weak Proxies are Sufficient and Preferable for Fairness with Missing Sensitive Attributes ICML 2023
A Universal Unbiased Method for Classification from Aggregate Observations ICML 2023
On Heterogeneous Treatment Effects in Heterogeneous Causal Graphs ICML 2023
PCA-based Multi-Task Learning: a Random Matrix Approach ICML 2023
Spurious Features Everywhere - Large-Scale Detection of Harmful Spurious Features in ImageNet ICCV 2023
Active Negative Loss Functions for Learning with Noisy Labels NIPS 2023
Difference-in-Differences Meets Tree-based Methods: Heterogeneous Treatment Effects Estimation with Unmeasured Confounding ICML 2023
Statistical Inference on Multi-armed Bandits with Delayed Feedback ICML 2023
Learning to Correct Noisy Labels for Fine-Grained Entity Typing via Co-Prediction Prompt Tuning EMNLP 2023