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.
• Maier, H. R., Taghikhah, F. R., Nabavi, E., Razavi, S., Gupta, H., Wu, W., … & Huang, J. (2024). How much X is in XAI: Responsible use of Explainable Artificial Intelligence in Hydrology and Water Resources.

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.
• Allgeier, J., González-Nicolás, A., Erdal, D., Nowak, W., & Cirpka, O. A. (2020). A stochastic framework to optimize monitoring strategies for delineating groundwater divides. Frontiers in Earth Science, 8, 554845.
• Rinderer, M., Ali, G., & Larsen, L. G. (2018). Assessing structural, functional and effective hydrologic connectivity with brain neuroscience methods: State-of-the-art and research directions. Earth-Science Reviews, 178, 29-47.
• Kirchner, J. W. (2006). Getting the right answers for the right reasons: Linking measurements, analyses, and models to advance the science of hydrology. Water resources research, 42(3).
• Lees, T., Buechel, M., Anderson, B., Slater, L., Reece, S., Coxon, G., & Dadson, S. J. (2021). Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models. Hydrology and Earth System Sciences, 25(10), 5517-5534.
• Li, X., Khandelwal, A., Jia, X., Cutler, K., Ghosh, R., Renganathan, A., … & Kumar, V. (2022). Regionalization in a global hydrologic deep learning model: from physical descriptors to random vectors. Water Resources Research, 58(8), e2021WR031794.
• Wagener, T., Dadson, S. J., Hannah, D. M., Coxon, G., Beven, K., Bloomfield, J. P., … & Old, G. (2021). Knowledge gaps in our perceptual model of Great Britain’s hydrology. Hydrological Processes, 35(7), e14288.
• Liu, Y., Wagener, T., Beck, H. E., & Hartmann, A. (2020). What is the hydrologically effective area of a catchment?. Environmental Research Letters, 15(10), 104024.
• Acuña Espinoza, E., Loritz, R., Álvarez Chaves, M., Bäuerle, N., & Ehret, U. (2024). To bucket or not to bucket? Analyzing the performance and interpretability of hybrid hydrological models with dynamic parameterization. Hydrology and Earth System Sciences, 28(12), 2705-2719.
• Baste, S., Klotz, D., Acuña Espinoza, E., Bardossy, A., & Loritz, R. (2025). Unveiling the limits of deep learning models in hydrological extrapolation tasks. Hydrology and Earth System Sciences, 29(21), 5871-5891.

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.
• Nearing, G. S., & Gupta, H. V. (2015). The quantity and quality of information in hydrologic models. Water Resources Research, 51(1), 524-538.
• Weijs, S. V. (2014). The data processing inequality and environmental model prediction.
• Goodwell, A. E., & Kumar, P. (2017). Temporal information partitioning: Characterizing synergy, uniqueness, and redundancy in interacting environmental variables. Water Resources Research, 53(7), 5920-5942.

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.
• Chicharro, D., & Ledberg, A. (2012). When two become one: the limits of causality analysis of brain dynamics. PloS one, 7(3), e32466.
• Runge, J. (2021). Necessary and sufficient graphical conditions for optimal adjustment sets in causal graphical models with hidden variables. Advances in Neural Information Processing Systems, 34, 15762-15773.
• Gerhardus, A., & Runge, J. (2020). High-recall causal discovery for autocorrelated time series with latent confounders. Advances in Neural Information Processing Systems, 33, 12615-12625.

Stochastic Differential Equations

• Weinreb, C., Wolock, S., Tusi, B. K., Socolovsky, M., & Klein, A. M. (2018). Fundamental limits on dynamic inference from single-cell snapshots. Proceedings of the National Academy of Sciences, 115(10), E2467-E2476.
• Coomer, M. A., Ham, L., & Stumpf, M. P. (2022). Noise distorts the epigenetic landscape and shapes cell-fate decisions. Cell Systems, 13(1), 83-102.
• Zhang, S. Y. (2024, November). Joint trajectory and network inference via reference fitting. In Machine Learning in Computational Biology (pp. 72-85). PMLR.

Statistical Learning

• Yang, Y. (2006). Comparing learning methods for classification. Statistica Sinica, 635-657.
• Basu, S., Kumbier, K., Brown, J. B., & Yu, B. (2018). Iterative random forests to discover predictive and stable high-order interactions. Proceedings of the National Academy of Sciences, 115(8), 1943-1948.
• 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.
• Kirchner, J. W. (2022). Impulse response functions for nonlinear, nonstationary, and heterogeneous systems, estimated by deconvolution and demixing of noisy time series. Sensors, 22(9), 3291.