July 18, 2026
This note is for Spector, Asher, and William Fithian. “Asymptotically Optimal Knockoff Statistics via the Masked Likelihood Ratio.” arXiv:2212.08766. Preprint, arXiv, October 1, 2024.
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July 18, 2026
This is the note for Sur, Pragya, and Emmanuel J. Candès. “A Modern Maximum-Likelihood Theory for High-Dimensional Logistic Regression.” Proceedings of the National Academy of Sciences 116, no. 29 (2019): 14516–25.
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June 10, 2026
This note is for Polson, N. G., Scott, J. G., & Windle, J. (2013). Bayesian Inference for Logistic Models Using Pólya–Gamma Latent Variables. Journal of the American Statistical Association, 108(504), 1339–1349.
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May 14, 2026
This note is for Lacroix, P., & Martin, M.-L. (2024). Trade-off between predictive performance and FDR control for high-dimensional Gaussian model selection. Electronic Journal of Statistics, 18(2).
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May 01, 2026 (Update: )
This note is for Luo, Yixiang, William Fithian, and Lihua Lei. “Estimating the FDR of Variable Selection.” arXiv:2408.07231. Preprint, arXiv, August 17, 2024.
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April 29, 2026 (Update: )
This note is for Ren, Z., & Candès, E. (2023). Knockoffs with side information. The Annals of Applied Statistics, 17(2), 1152–1174.
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April 05, 2026
This note is for Chakraborty, Abhinav, Junu Lee, and Eugene Katsevich. “Power of Masking Methods for Adaptive Testing in a Multivariate Normal Means Problem.” arXiv:2601.07764. Version 2. Preprint, arXiv, March 31, 2026..
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April 05, 2026
This note is for Yang, Chiao-Yu, Lihua Lei, Nhat Ho, and Will Fithian. “BONuS: Multiple Multivariate Testing with a Data-Adaptive test Statistic.” arXiv:2106.15743. Preprint, arXiv, July 1, 2021.
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April 01, 2026
This note is for Candès, E., Lei, L., & Ren, Z. (2023). Conformalized survival analysis. Journal of the Royal Statistical Society Series B: Statistical Methodology, 85(1), 24–45.
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February 28, 2026
This note is for Ke, Zheng Tracy, Jun S. Liu, and Yucong Ma. “Power of Knockoff: The Impact of Ranking Algorithm, Augmented Design, and Symmetric Statistic.” arXiv:2010.08132. Preprint, arXiv, February 13, 2024.
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January 19, 2026
This note is for Duan, T., Anand, A., Ding, D. Y., Thai, K. K., Basu, S., Ng, A., & Schuler, A. (2020). NGBoost: Natural Gradient Boosting for Probabilistic Prediction. Proceedings of the 37th International Conference on Machine Learning, 2690–2700.
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January 07, 2026
This note is for McInnes, L., Healy, J., & Melville, J. (2020). UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction (No. arXiv:1802.03426). arXiv.
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December 29, 2025
This note is for Lei, J. (2020). Cross-Validation With Confidence. Journal of the American Statistical Association, 115(532), 1978–1997.
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November 12, 2025
This note is for Barnett, I., Mukherjee, R., & Lin, X. (2017). The Generalized Higher Criticism for Testing SNP-Set Effects in Genetic Association Studies. Journal of the American Statistical Association, 112(517), 64–76.
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October 25, 2025
This note is for Liu, K., Long, Q., Shi, Z., Su, W. J., & Xiao, J. (2025). Statistical Impossibility and Possibility of Aligning LLMs with Human Preferences: From Condorcet Paradox to Nash Equilibrium (No. arXiv:2503.10990). arXiv.
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October 22, 2025
This note is for Zrnic, T., & Candès, E. J. (2024). Cross-prediction-powered inference. Proceedings of the National Academy of Sciences, 121(15), e2322083121.
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October 16, 2025
This note is for Wu, R., Zhou, S., Lu, J., Shen, Z., Xu, Z., Shu, J., Yang, K., Lin, F., & Zhang, Y. (2024). Removing obstacles before breaking through the memory wall: A close look at HBM errors in the field. Proceedings of the 2024 USENIX Conference on Usenix Annual Technical Conference, 851–867.
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October 16, 2025
This note is for Yu, Q., Zhang, W., Cardoso, J., & Kao, O. (2023). Exploring Error Bits for Memory Failure Prediction: An In-Depth Correlative Study. 2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD), 01–09.
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October 15, 2025
This note is for Donoho, D., & Jin, J. (2004). Higher criticism for detecting sparse heterogeneous mixtures. The Annals of Statistics, 32(3), 962–994.
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October 11, 2025
This note is for Kim, M. P., Ghorbani, A., & Zou, J. (2018). Multiaccuracy: Black-Box Post-Processing for Fairness in Classification (No. arXiv:1805.12317). arXiv.
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October 10, 2025
This note is for Wright, I. W., & Wegman, E. J. (1980). Isotonic, Convex and Related Splines. The Annals of Statistics, 8(5), 1023–1035.
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October 08, 2025
This note is for Deng, Z., Zhang, J., Zhang, L., Ye, T., Coley, Y., Su, W. J., & Zou, J. (2022). FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data (No. arXiv:2206.02792). arXiv.
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October 06, 2025
This note is for Zhang, L., Roth, A., & Zhang, L. (2024). Fair Risk Control: A Generalized Framework for Calibrating Multi-group Fairness Risks. Proceedings of the 41st International Conference on Machine Learning, 59783–59805.
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October 05, 2025
This note is for Li, X., Ruan, F., Wang, H., Long, Q., & Su, W. J. (2025). Robust detection of watermarks for large language models under human edits. Journal of the Royal Statistical Society Series B.
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October 04, 2025
This note is for Viladomat, J., Mazumder, R., McInturff, A., McCauley, D. J., & Hastie, T. (2014). Assessing the significance of global and local correlations under spatial autocorrelation: A nonparametric approach. Biometrics, 70(2), 409–418.
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October 03, 2025
This note is for Wang, Y., Zang, C., Li, Z., Guo, C. C., Lai, D., & Wei, P. (2025). A comparative study of statistical methods for identifying differentially expressed genes in spatial transcriptomics (p. 2025.02.17.638726). bioRxiv.
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October 03, 2025
This note is for Xie, Y., Li, X., Mallick, T., Su, W., & Zhang, R. (2025). Debiasing Watermarks for Large Language Models via Maximal Coupling. Journal of the American Statistical Association, 0(0), 1–11.
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October 02, 2025
This note is for the paper Zrnic, T., & Jordan, M. I. (2023). Post-selection inference via algorithmic stability. The Annals of Statistics, 51(4), 1666–1691..
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September 04, 2025 (Update: )
This note is for Jain, A., Montanari, A., & Sasoglu, E. (2024, November 6). Scaling laws for learning with real and surrogate data. The Thirty-eighth Annual Conference on Neural Information Processing Systems.
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July 22, 2025
this note is for Tian, X., & Shen, X. (2025). Enhancing Accuracy in Generative Models via Knowledge Transfer (No. arXiv:2405.16837). arXiv.
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June 12, 2025
This note is for Tang, D., Kong, D., & Wang, L. (2024). The synthetic instrument: From sparse association to sparse causation (No. arXiv:2304.01098). arXiv.
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June 02, 2025
This note is for Miao, W., Hu ,Wenjie, Ogburn ,Elizabeth L., & and Zhou, X.-H. (2023). Identifying Effects of Multiple Treatments in the Presence of Unmeasured Confounding. Journal of the American Statistical Association, 118(543), 1953–1967.
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May 01, 2025
This note is for Laves, M.-H., Ihler, S., Fast, J. F., Kahrs, L. A., & Ortmaier, T. (2020). Well-Calibrated Regression Uncertainty in Medical Imaging with Deep Learning. Proceedings of the Third Conference on Medical Imaging with Deep Learning, 393–412.
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April 25, 2025
This is the note for Lahlou, S., Jain, M., Nekoei, H., Butoi, V. I., Bertin, P., Rector-Brooks, J., Korablyov, M., & Bengio, Y. (2023). DEUP: Direct Epistemic Uncertainty Prediction (No. arXiv:2102.08501). arXiv. https://doi.org/10.48550/arXiv.2102.08501
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April 23, 2025
This note is for Upadhyay, U., Kim, J. M., Schmidt, C., Schölkopf, B., & Akata, Z. (2023). Likelihood Annealing: Fast Calibrated Uncertainty for Regression (No. arXiv:2302.11012). arXiv. https://doi.org/10.48550/arXiv.2302.11012
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April 23, 2025
This note is for Cherian, J., Gibbs, I., & Candes, E. (2024, November 6). Large language model validity via enhanced conformal prediction methods. The Thirty-eighth Annual Conference on Neural Information Processing Systems.
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April 21, 2025
This note is for Shumailov, I., Shumaylov, Z., Zhao, Y., Papernot, N., Anderson, R., & Gal, Y. (2024). AI models collapse when trained on recursively generated data. Nature, 631(8022), 755–759. https://doi.org/10.1038/s41586-024-07566-y
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April 09, 2025
This note is for Liu, Y., & and Xie, J. (2020). Cauchy Combination Test: A Powerful Test With Analytic p-Value Calculation Under Arbitrary Dependency Structures. Journal of the American Statistical Association, 115(529), 393–402.
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April 05, 2025
This note is for Kotelnikov, A., Baranchuk, D., Rubachev, I., & Babenko, A. (2023). TabDDPM: Modelling Tabular Data with Diffusion Models. Proceedings of the 40th International Conference on Machine Learning, 17564–17579.
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February 23, 2025
This note is for Leng, Z., Tan, M., Liu, C., Cubuk, E. D., Shi, X., Cheng, S., & Anguelov, D. (2022). PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions (No. arXiv:2204.12511). arXiv. https://doi.org/10.48550/arXiv.2204.12511
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February 23, 2025
This note is for Pereyra, G., Tucker, G., Chorowski, J., Kaiser, Ł., & Hinton, G. (2017). Regularizing Neural Networks by Penalizing Confident Output Distributions (No. arXiv:1701.06548). arXiv. https://doi.org/10.48550/arXiv.1701.06548
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February 22, 2025
This note is for Müller, R., Kornblith, S., & Hinton, G. E. (2019). When does label smoothing help? Advances in Neural Information Processing Systems, 32.
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February 22, 2025
This post is for Mukhoti, J., Kulharia, V., Sanyal, A., Golodetz, S., Torr, P., & Dokania, P. (2020). Calibrating Deep Neural Networks using Focal Loss. Advances in Neural Information Processing Systems, 33, 15288–15299.
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February 11, 2025
This note is for Wang, H., Ibrahim, S., & Mazumder, R. (2023). Nonparametric Finite Mixture Models with Possible Shape Constraints: A Cubic Newton Approach (No. arXiv:2107.08535). arXiv. https://doi.org/10.48550/arXiv.2107.08535
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February 07, 2025
This note is for Muennighoff, N., Yang, Z., Shi, W., Li, X. L., Fei-Fei, L., Hajishirzi, H., Zettlemoyer, L., Liang, P., Candès, E., & Hashimoto, T. (2025). s1: Simple test-time scaling (No. arXiv:2501.19393). arXiv. https://doi.org/10.48550/arXiv.2501.19393
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February 07, 2025
This note is based on Hershey, J. R., & Olsen, P. A. (2007). Approximating the Kullback Leibler Divergence Between Gaussian Mixture Models. 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP ’07, 4, IV-317-IV–320. https://doi.org/10.1109/ICASSP.2007.366913
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February 06, 2025
This post is for Cai, T. T., Ma, J., & Zhang, L. (2019). CHIME: Clustering of high-dimensional Gaussian mixtures with EM algorithm and its optimality. The Annals of Statistics, 47(3), 1234–1267. https://doi.org/10.1214/18-AOS1711
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February 06, 2025
This note is for Blei, D. M., Kucukelbir, A., & McAuliffe, J. D. (2017). Variational Inference: A Review for Statisticians. Journal of the American Statistical Association, 112(518), 859–877. https://doi.org/10.1080/01621459.2017.1285773
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February 04, 2025
This post is for Dwivedi, R., Ho, N., Khamaru, K., Wainwright, M. J., Jordan, M. I., & Yu, B. (2020). Singularity, Misspecification and the Convergence Rate of Em. The Annals of Statistics, 48(6), 3161–3182.
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January 28, 2025
This note is for Arthur, D., & Vassilvitskii, S. (2006). k-means++: The Advantages of Careful Seeding. Stanford, 11.
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