Qianxin Liang

Artificial Intelligence · 2024

A researcher in the Science corpus: 1 paper; trusts 5 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Qianxin Liang trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Lloyd S. Shapley
  2. Scott M. Lundberg
  3. Wilson Estécio Marcílio
  4. Leo Breiman
  5. Pierre Geurts
  6. Trevor J. Hastie
  7. Liudmila Ostroumova

Papers from Qianxin Liang's perspective

Ordered by how strongly the researchers Qianxin Liang trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Bagging Predictors
  2. A Unified Approach to Interpreting Model Predictions
  3. LightGBM: A Highly Efficient Gradient Boosting Decision Tree
  4. Random Forests
  5. A Value for n-person Games
  6. Extremely randomized trees
  7. CatBoost: unbiased boosting with categorical features (Neural Information Processing Systems)
  8. From explanations to feature selection: assessing SHAP values as feature selection mechanism
  9. Data Mining Practical Machine Learning Tools and Techniques
  10. A practical tutorial on bagging and boosting based ensembles for machine learning: Algorithms, software tools, performance study, practical perspectives and opportunities

Their papers