Favorite Papers

Feature Importance

• Catav, A., Fu, B., Zoabi, Y., Meilik, A. L. W., Shomron, N., Ernst, J., … & Gilad-Bachrach, R. (2021, July). Marginal contribution feature importance-an axiomatic approach for explaining data. In International Conference on Machine Learning (pp. 1324-1335). PMLR.
• Harel, N., Gilad-Bachrach, R., & Obolski, U. (2022). Inherent Inconsistencies of Feature Importance. arXiv preprint arXiv:2206.08204.
• Grömping, U. (2009). Variable importance assessment in regression: linear regression versus random forest. The American Statistician, 63(4), 308-319.

Hydrology

• Tang, G., Clark, M. P., Newman, A. J., Wood, A. W., Papalexiou, S. M., Vionnet, V., & Whitfield, P. H. (2020). SCDNA: A serially complete precipitation and temperature dataset for North America from 1979 to 2018. Earth System Science Data, 12(4), 2381-2409.
• Franzen, S. E., Farahani, M. A., & Goodwell, A. E. (2020). Information Flows: Characterizing Precipitation‐Streamflow Dependencies in the Colorado Headwaters With an Information Theory Approach. Water Resources Research, 56(10), e2019WR026133.
• Tennant, C., Larsen, L., Bellugi, D., Moges, E., Zhang, L., & Ma, H. (2020). The utility of information flow in formulating discharge forecast models: A case study from an arid snow‐dominated catchment. Water Resources Research, 56(8), e2019WR024908.
• Gong, W., Gupta, H. V., Yang, D., Sricharan, K., & Hero III, A. O. (2013). Estimating epistemic and aleatory uncertainties during hydrologic modeling: An information theoretic approach. Water resources research, 49(4), 2253-2273.

Information Theory

• Griffith, V., & Koch, C. (2014). Quantifying synergistic mutual information. Guided self-organization: inception, 159-190.
• Barrett, A. B. (2015). Exploration of synergistic and redundant information sharing in static and dynamical Gaussian systems. Physical Review E, 91(5), 052802.
• Quax, R., Har-Shemesh, O., & Sloot, P. M. (2017). Quantifying synergistic information using intermediate stochastic variables. Entropy, 19(2), 85.
• Gurushankar, K., Venkatesh, P., & Grover, P. (2022, September). Extracting Unique Information Through Markov Relations. In 2022 58th Annual Allerton Conference on Communication, Control, and Computing (Allerton) (pp. 1-6). IEEE.

Causal Inference and Graphical Models

• Marx, A., Gretton, A., & Mooij, J. M. (2021, December). A weaker faithfulness assumption based on triple interactions. In Uncertainty in Artificial Intelligence (pp. 451-460). PMLR.
• Sadeghi, K. (2017). Faithfulness of probability distributions and graphs. Journal of Machine Learning Research, 18(148), 1-29.
• van Breugel, B., Kyono, T., Berrevoets, J., & van der Schaar, M. (2021). Decaf: Generating fair synthetic data using causally-aware generative networks. Advances in Neural Information Processing Systems, 34, 22221-22233.

Feature Selection

• Yang, Y. (2006). Comparing learning methods for classification. Statistica Sinica, 635-657.
• Zou, H. (2006). The adaptive lasso and its oracle properties. Journal of the American statistical association, 101(476), 1418-1429.
• Leng, C., Lin, Y., & Wahba, G. (2006). A note on the lasso and related procedures in model selection. Statistica Sinica, 1273-1284.