### Machine Learning

Human Decisions and Machine Predictions. NBER WP, 2017.

- PW Koh, P Liang. Understanding Black-box Predictions via Influence Functions. arXiv:1703.04730.

#### Cross-validation

- S Saeb, L Lonini, A Jayaraman, DC Mohr, KP Kording. Voodoo Machine Learning for Clinical Predictions.

M Wainberg, B Alipanahi, BJ Frey. Are Random Forests Truly the Best Classifiers?. JMLR, 2016.

LAC Millard, PA Flach, JPT Higgins. Machine learning to assist risk-of-bias assessments in systematic reviews. IJE, 2016.

- Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning. JMLR, 2017.

#### Network Representation Learning

- Graph Embedding Techniques, Applications, and Performance: A Survey. arXiv:1705.02801.
LG Moyano. Learning network representations. EPJ Special Topics, 2017.

Neural Embeddings of Graphs in Hyperbolic Space. arXiv:1705.10359.

- M Nickel, D Kiela. PoincarĂ© Embeddings for Learning Hierarchical Representations. arXiv:1705.08039.
- struc2vec: Learning Node Representations from Structural Identity. arXiv:1704.03165.
- From Node Embedding To Community Embedding. arXiv:1610.09950.
- Asymmetric Transitivity Preserving Graph Embedding. KDD 2016.
- node2vec: Scalable Feature Learning for Networks. KDD 2016.
- J Tang, M Qu, Q Mei. PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks. KDD 2015.
- LINE: Large-scale Information Network Embedding. WWW 2015.
- B Perozzi, R Al-Rfou, S Skiena. DeepWalk: online learning of social representations. KDD 2014.