Maximilian Schleich

Artificial Intelligence · 2020

A researcher in the Science corpus: 1 paper; trusts 10 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Maximilian Schleich trusts overall

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

  1. Scott M. Lundberg
  2. Steffen Rendle
  3. Andrew Y. Ng
  4. Erik Strumbelj
  5. Mark Chavira

Papers from Maximilian Schleich's perspective

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

  1. A Unified Approach to Interpreting Model Predictions
  2. The many Shapley values for model explanation
  3. Consistent Individualized Feature Attribution for Tree Ensembles
  4. Explaining individual predictions when features are dependent: More accurate approximations to Shapley values
  5. Algorithmic Transparency via Quantitative Input Influence: Theory and Experiments with Learning Systems
  6. From Local Explanations to Global Understanding with Explainable AI for Trees
  7. Problems with Shapley-value-based explanations as feature importance measures
  8. Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods
  9. On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes
  10. Factorization Machines

Their papers