Instruct: Given the title of a paper, retrieve paper titles that are cited by the given paper
Query: Dataset shift in classification: terminology, benchmarks and methods
Dataset Shift in Machine Learning
New Transfer Learning Techniques for Disparate Label Sets
Imbalanced text classification: A term weighting approach
Transfer learning for class imbalance problems with inadequate data
PASCAL Boundaries: A Class-Agnostic Semantic Boundary Dataset
Evolutionary Undersampling for Classification with Imbalanced Datasets: Proposals and Taxonomy
Fine-Grained Categorization and Dataset Bootstrapping Using Deep Metric Learning with Humans in the Loop
Classifying Data Streams with Skewed Class Distributions and Concept Drifts
Covariate shift adaptation by importance weighted cross validation
an efficient mechanism for classification of imbalanced big data .
CLASSIFICATION OF IMBALANCED DATA: A REVIEW
LSHTC: A Benchmark for Large-Scale Text Classification
Semantic Redundancies in Image-Classification Datasets: The 10% You Don't Need
Determining the Training Window for Small Sample Size Classification with Concept Drift
Demographic Factors Improve Classification Performance
Just-in-Time Adaptive Classifiers—Part I: Detecting Nonstationary Changes
Unsupervised Adaptation Across Domain Shifts by Generating Intermediate Data Representations
Evolutionary Data Measures: Understanding the Difficulty of Text Classification Tasks
Zero-Shot Learning Posed as a Missing Data Problem
Survey of resampling techniques for improving classification performance in unbalanced datasets
Knowledge as a Teacher: Knowledge-Guided Structural Attention Networks
Partitioning graphs to speedup Dijkstra's algorithm
Sample Selection Bias as a Specification Error
A fully-differential capacitive touch controller with input common-mode feedback for symmetric display noise cancellation
Inductively-Coupled Miniaturized-Element Frequency Selective Surfaces With Narrowband, High-Order Bandpass Responses