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Refereed Publications (Journals and Conferences)
Li, G., Wu, W., Chi, Y., Ma, C., Rinaldo, A. and Wei, Y. (2024). Sharp high-probability sample complexities for policy evaluation with linear function approximation, to appear in iEEE Transactions on Information Theory arXiv
Nguyen, H., Ho, N. and Rinaldo, A. (2024). Sigmoid Gating is More Sample Efficient than Softmax Gating in Mixture of Experts, NeurIPS 2024 arXiv
Wang, F., Li, W., Padilla, O. H., Yu, Y. and Rinaldo, A. (2024). Multilayer random dot product graphs: Estimation and online change point detection. To appear in JRSS. arXiv
Nguyen, H., Ho, N. and Rinaldo, A. (2024). On Least Squares Estimation in Softmax Gating Mixture of Experts, ICML 2024 arXiv
Yi, Y., Madrid Padilla, O.H., Wang, D. and Rinaldo, A. (2024+). Optimal network online change point localisation. To appear in SIMODS arXiv
Wu, W., Kim, J. and Rinaldo, A. (2024). On the estimation of persistence intensity functions and linear representations of persistence diagrams. AISTATS 2024. arXiv
Shin, J., Ramdas, A. and Rinaldo, A. (2023). E-detectors: a nonparametric framework for online changepoint detection. The New England Journal of Statistics in Data Science, 1-32. arXiv
Li, W., Wang, D. and Rinaldo, A. (2023). Divide and Conquer Dynamic Programming: An Almost Linear Time Change Point Detection Methodology in High Dimensions. ICML 2023. arXiv
Madrid Padilla, O.H., Yu, Y., Wang, D. And Rinaldo, A. (2023+). A Note on Online Change Point Detection. To appear in Sequential Analysis arXiv
Li, W., Wang, D. and Rinaldo, A. (2022). Detecting Abrupt Changes in Sequential Pairwise Comparison Data, NeurIPS 2022. arXiv
Shrotriya, S. And Li, W. and Rinaldo, A. (2022). The Performance of the MLE in the Bradley-Terry-Luce Model in \(\ell_\infty\)-Loss and under General Graph Topologies. UAI 2022. arXiv
Wang, F., Madrid Padilla, O.H., Yu, Y. and Rinaldo, A. (2022). Denoising and change point localisation in piecewise-constant high-dimensional regression coefficients, AISTATS 2022 (oral presentation). arXiv
Patil, P., Rinaldo, A. And Tibshirani, R. (2022). Estimating Functionals of the Out-of- Sample Error Distribution in High-Dimensional Ridge Regression. AISTATS 2022.
Madrid Padilla, O.H., Yu, Y. and Rinaldo, A. (2021). Optimal partition recovery in general graphs. AISTATS 2022. arXiv
Madrid Padilla, O.H., Yu, Y., Wang, D. And Rinaldo, A. (2022+). Optimal nonparametric multivariate change point detection and localization, IEEE Transactions on Information Theory, 68(3), 1922-1944. arXiv R code
Shin, J., Ramdas, A., Rinaldo, A. (2021). Nonparametric iterated-logarithm extensions of the sequential generalized likelihood ratio test, IEEE Journal on Selected Areas in Information Theory, 691-704 arXiv code
Shin, J., Ramdas, A., Rinaldo, A. (2021). On the bias, risk and consistency of sample means in multi-armed bandits, SIAM Journal on Mathematics of Data Science (SIMODS), 3(4), 1278–1300. arXiv
Madrid Padilla, O.H., Yu, Y. and Rinaldo, A. (2021). Lattice partition recovery with dyadic CART. NeurIPS 2021. arXiv
Madrid Padilla, O.H., Yu, Y., Wang, D. and Rinaldo, A. (2021). Optimal nonparametric change point detection and localization, Electronic Journal of Statistics, 15(1), 1154-1201. arXiv R code
Wen, Q., Wang, D., Yu, Y., Rinaldo, A. and Willett, R. (2021). Localizing Changes in High-Dimensional Regression Models. AISTASTS 2021. arXiv
Patil, P., Rinaldo, A., Tibshirani, R. and Wei, Y. (2021). Uniform consistency of cross validation estimators for high-dimensional ridge regression. AISTASTS 2021.
Wang, D., Yu. Y. and Rinaldo, A. (2021). Optimal Covariance Change Point Detection in High Dimensions, Bernoulli, 27(1): 554-575. arXiv
Wang, D., Yu. Y. and Rinaldo, A. (2020). Optimal Change Point Detection and Localization in Sparse Dynamic Networks, Annals of Statistics}, 49(1), 203-232. arXiv R code
Bong, H., Li, W., Shrotriya, S. and Rinaldo, A. (2020). Nonparametric Estimation in the Dynamic Bradley-Terry Model, AISTASTS 2020. arXiv
Kim, J. Shin, J., Chazal, F., Rinaldo, A. and Wasserman, L. (2020). Homotopy Reconstruction via the Cech Complex and the Rips Complex, to appear in SoCG 2020. arXiv
Sadeghi, K. and Rinaldo, A. (2020). Hierarchical Models for Independence Structures of Networks, Statistica Neerlandica, 74(3), 439-457. arXiv
Wang, D., Yu, Y. and Rinaldo, A. (2020). Univariate Mean Change Point Detection: Penalization, CUSUM and Optimality, Electronic Journal of Statistics, 14(1), 1917-1961. arXiv
Shin, J., Ramdas, A., Rinaldo, A. (2020). On conditional versus marginal bias in multi-armed bandits, ICML 2020. arXiv
Wang. D., Lu, X. and Rinaldo, A. (2019). DBSCAN: Optimal Rates For Density-Based Cluster Estimation, Journal of Machine Learning Research, 20(170), 1−50. arXiv
Shin, J., Ramdas, A., Rinaldo, A. (2019). Are sample means in multi-armed bandits positively or negatively biased? NeuriPS 2019. ariXv
Gu, X., Akoglu, L. and Rinaldo, A. (2019). Statistical Analysis of Nearest Neighbor Methods for Anomaly Detection, NeurIPS 2019. arXiv
Aamari, E., Kim, J., Chazal, F., Bertrand, M., Rinaldo, A., Wasserman, L. (2019). Estimating the Reach of a Manifold, Electronic Journal of Statistics, 13(1), 1359–1399. arXiv
Rinaldo, A., Wasserman, L. and G’Sell, M. (2019). Bootstrapping and sample splitting for high-dimensional, assumption-lean inference, the Annals of Statistics, 47(6), 3438-3469. arXiv
Kim, J., Shin, J., Rinaldo, A. and Wasserman, L. (2019). Uniform Convergence Rate of the Kernel Density Estimator Adaptive to Intrinsic Dimension, ICML 2019. arXiv
Kim, J. Rinaldo, A. and Wasserman, L. (2019). Minimax Rates for Estimating the Dimension of a Manifold, Journal of Computational Geometry, 10(1), 42–95. arXiv
Rinaldo, A., Lauritzen, S. and Sadeghi, K. (2019). On Exchangeability in Network Models. Journal of Algebraic Statistics, 10(1), 85–14. arXiv
Lauritzen, S., Rinaldo, A. and Sadeghi, K. (2018), Random Networks, Graphical Models, and Exchangeability (2019), the Journal of the Royal Statistical Society, Series B, 30(3), 481–508. arXiv
Tibshirani, R.J., Rinaldo, A., Tibshirani, R. and Wasserman, L. (2018). Uniform Asymptotic Inference and the Bootstrap After Model Selection, the Annals of Statistics, 46(3), 1255–1287. arXiv
Chazal, F., Fasy, B.T., Lecci, F., Michel, B., Rinaldo, A., Wasserman, L., (2018), Robust Topological Inference: Distance-to-a-Measure and Kernel Distance, the Journal of Machine Learning Research, 18, 1–40. arXiv
Lin, K., Sharpnack, J., Rinaldo, A. and Tibshirani, R.J. (2017). Approximate Recovery in Changepoint Problems, from ℓ2 Estimation Error Rates, NIPS 2017. arXiv
Balakrishnan, S., Kolar, M., Rinaldo, A. and Singh, A. (2017). Recovering Block-structured Activations Using Compressive Measurements, the Electronic Journal of Statistics, 11(1), 2647–2678. arXiv
Lei, J., G’Sell, M., Rinaldo, A., Tibshirani, R. and Wasserman, L. (2016). Distribution-Free Predictive Inference For Regression,the Journal of the American Statistical Association, 113(523), 1094-111. arXiv
Kim, J., Chen, Y.-C, Balakrishnan, S., Rinaldo, A. and Wasserman, L. (2016). Statistical Inference for Cluster Trees, NIPS 2016. arXiv
Kim, N., Wilburne, D., Petrović, S. and Rinaldo A. (2016). On the Geometry and Extremal Properties of the Edge-Degeneracy Model, SDM16 Workshop on Mining Networks and Graphs. arXiv
Yin, M., Rinaldo, A. and Fadnavis S. (2015). Asymptotic quantization of exponential random graphs, the Annals of Applied Probability, 26(6), 3251-3285. arXiv
Sharpnack, J., Rinaldo, A. and Singh, A. (2015). Detecting Anomalous Activity on Networks With the Graph Fourier Scan Statistic, IEEE Transaction on Signal Processing, 64(2), 364-379. arXiv
Chazal, F., Fasy, B.T., Lecci, F., Michel, B., Rinaldo, A. and Wasserman, L. (2015). Subsampling Methods for Persistent Homology, ICML 2015. arXiv
Lei, J. and Rinaldo, A. (2015). Consistency of Spectral Clustering in Sparse Stochastic Block Models, Annals of Statistics, 43(1), 215 – 237. arXiv
Chazal, F., Fasy, B.T., Lecci, F., Rinaldo, A. and Wasserman, L. (2015). Stochastic Convergence of Persistence Landscapes and Silhouettes, Journal of Computational Geometry, 6(2), 140-161.
The paper initially appeared in the Proceedings of the 30th Symposium of Computational Geometry SoCG 2014. arXiv
Sadeghi, K. and Rinaldo, A. (2014). Statistical Models for Degree Distributions of Networks, NIPS 2014 Workshop “From Graphs to Rich Data.” arXiv
Stasi, D., Sadeghi, K., Rinaldo, A., Petrović, S. and Fienberg, S.E. (2014). \(\beta\) models for random hypergraphs with a given degree sequence, COMPSTAT 2014. arXiv
Wasserman, L., Kolar, M. and Rinaldo, A. (2014). Berry-Essen Bounds for Estimating Undirected Graphs, Electronic Journal of Statistics, 8(1), 1188-1224. arXiv
Yang, X., Rinaldo, A. and Fienberg, S.E. (2014). Estimation for Dyadic-Dependent Exponential Random Graph Models, the Journal of Algebraic Statistics, 5(1).
Fasy, B.T., Lecci, F., Rinaldo, A., Wasserman, L., Balakrishnan, S. and Singh, A. (2014). Confidence Sets for Persistence Diagrams, The Annals of Statistics, 42(6), 2301–2339. arXiv
Lecci, F., Rinaldo, A. and Wasserman, L. (2014). Statistical Analysis of Metric Graph Reconstruction, Journal of Machine Learning Research, 15, 3425-3446. arXiv
Kent, B. P., Rinaldo, A., Yeh, F.-C. and Verstynen, T. (2014). Mapping Topographic Structure in White Matter Pathways with Level Set Trees, PLOS One. link
Chazal, F., Fasy, B. T., Lecci, F., Rinaldo, A., Singh, A., Wasserman L. (2013). On the Bootstrap for Persistence Diagrams and Landscapes, Modeling and Analysis of Information Systems, 20:6, 96–105. arXiv
Balakrishnan, S., Narayanan, S., Rinaldo, A., Singh, A. and Wasserman (2013). Cluster Trees on Manifolds, NIPS 2013. arXiv
Lei, J., Rinaldo, A. and Wasserman, L. (2015). A Conformal Prediction Approach to Explore Functional Data, Annals of Mathematics and Artificial Intelligence, 74, 29–43. arXiv
Poczos, B., Rinaldo, A., Singh, A. and Wasserman, L. (2013). Distribution-Free Distribution Regression, AISTATS 2013. arXiv
Sharpnak, J., Rinaldo, A. and Singh, A. (2013). Changepoint Detection over Graphs with the Spectral Scan Statistic, AISTATS 2013. arXiv
Rinaldo, A., Petrovíc, S. and Fienberg, S.E. (2013). Maximum Likelihood Estimation in Network Models, Annals of Statistics, 41(3), 1085-1110. arXiv R code
Hall, R., Rinaldo, A. and Wasserman, L. (2013). Differential Privacy for Functions and Functional Data, Journal of Machine Learning Research, 14, 703-727. arXiv
Shalizi, C. R. and Rinaldo, A. (2013). Consistency under Sampling of Exponential Random Graph Models, Annals of Statistics, 41(2), 508–535.
Rinaldo, A., Petrovíc, S. and Fienberg, S.E. (2012). How Does Maximum Likelihood Estimation for the \(p_1\) Model Scale for Large Sparse Networks?, NIPS 2012 workshop on “Algorithmic and Statistical Approaches for Large Social Network Data Sets” pdf
Hall, R., Rinaldo, A. and Wasserman, L. (2012). Random Differential Privacy, Journal of Privacy and Confidentiality, 4(2), 43–59. arXiv
Rinaldo, A., Singh, A., Nugent, R. and Wasserman, L. (2012). Stability of Density-Based Clustering, Journal of Machine Learning, 13, 905–948. link
Fienberg, S.E. and Rinaldo, A. (2012). Maximum Likelihood Estimation in Log-linear Models, Annals of Statistics, 40(2), 996–1023. The original version of the manuscript appeared on the arXiv under the title “Maximum Likelihood Estimation in Log-linear Models: Theory and Algorithms” and is different than the published one.
Balakrishnan, S., Rinaldo, A., Sheehy, D. R., Singh, A. and Wasserman, L. (2012). Minimax Rates for Homology Inference, AISTATS 2012. arXiv
Sharpnack, J., Rinaldo, A. and Singh, A. (2012). Sparsistency of the Edge Lasso over Graphs, AISTATS 2012. link
Nardi, Y. and Rinaldo, A. (2012). The Log-linear Group Lasso Estimator for Hierarchical Log-Linear Models and Its Asymptotic Properties, Bernoulli, 18(3), 945-974.
Yang, X., Fienberg, S.E. and Rinaldo, A. (2012). Differential Privacy for Protecting Multi-dimensional Contingency Table Data: Extensions and Applications, Journal of Privacy and Confidentiality, 4(1), 101-125. link
Balakrishnan, S., Kolar, M., Rinaldo, A., Singh, A. and Wasserman, L. (2011). Statistical and computational tradeoffs in biclustering, NIPS 2011 Workshop “Computational Trade-offs in Statistical Learning.” pdf
Kolar, M., Balakrishnan, S., Rinaldo, A. and Singh, A. (2011). Minimax Localization of Structural Information in Large Noisy Matrices, Neural Information Processing Systems, NIPS 2011. link
Nardi, Y. and Rinaldo, A. (2011). Autoregressive Process Modeling via the Lasso Procedure, Journal of Multivariate Analysis, 103(3), 528–549. arXiv
Fienberg, S.E., Rinaldo, A. and Yang, X. (2011). Differential Privacy and the Risk-Utility Tradeoff for Multi-dimensional Contingency Tables, Lecture Notes in Computer Science,2011, Volume 6344, 187–199, Springer. pdf
Rinaldo, A. and Wasserman, L. (2010). Generalized Density Clustering, The Annals of Statistics, 38(5), 2678–2722. arXiv
Petrovíc, S., Rinaldo, A. and Fienberg, S.E. (2009). Algebraic Statistics for a Directed Random Graph Model with Reciprocation, Algebraic Methods in Statistics and Probability II, Contemporary Mathematics series, published by the American Mathematical Society. pdf
Fienberg, S.E., Petrovíc, S. and Rinaldo, A. (2009). Algebraic Statistics for \(p_1\) Random Graphs Models: Markov Bases and their Uses, “Looking back: A festschrift to Honor Paul Holland,” published by the Educational Testing Services.
Rinaldo, A. (2009). Properties and Refinement of the Fused Lasso, The Annals of Statistics 37, 5B, 2922–2952. Correction
Rinaldo, A., Fienberg, S.E. and Zhou, Y. (2009). On the Geometry of Discrete Exponential Families with Application to Exponential Random Graph Models, Electronic Journal of Statistics, 3, 446–484.
Nardi, Y. and Rinaldo, A. (2008). On the Asymptotic Properties of The Group Lasso Estimator in Least Squares Problem, Electronic Journal of Statistics, 2, 605–633.
Dobra, A., Fienberg, S.E., Rinaldo, A., Slavkovic, A. and Zhou, Y. (2008). Algebraic Statistics and Contingency Table Problems: Estimation and Disclosure Limitation, in Emerging Applications of Algebraic Geometry, (M. Putinar, S. Sullivant, eds.), IMA Series in Applied Mathematics, Springer-Verlag. pdf
Fienberg, S.E., Hersh, P., Rinaldo, A. and Zhou, Y. (2007). Maximum Likelihood Estimation in Latent Class Models For Contingency Table Data, in Algebraic and Geometric Methods in Statistics, Cambridge University Press. pdf
Fienberg, S. E., Rinaldo, A. (2007). Three Centuries of Categorical Data Analysis: Log-linear Models and Maximum Likelihood Estimation, Journal of Statistical Planning and Inference, 137, 11, 3420-3445. Special Issue: In Celebration of the Centennial of The Birth of Samarendra Nath Roy (1906-1964). pdf
Eriksson, N., Fienberg, E. S., Rinaldo, A., Sullivant, S. (2006). Polyhedral Conditions for the Nonexistence of the MLE for Hierarchical Log-linear Models, Journal of Symbolic Computation, 41, 222–233. Special Issue on Algebraic Statistics. pdf
Rinaldo, A., Balcanu, S., Devlin, B., Sonpar, V., Wasserman, L., Roeder, K. (2005). Characterization of Multilocus Linkage Disequilibrium, Genetic Epidemiology, 28 (3), 193–206.
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