[et_pb_section fb_built=”1″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_row _builder_version=”4.16″ global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_text _builder_version=”4.16″ global_colors_info=”{}”]<\/p>\n
[\/et_pb_text][et_pb_divider _builder_version=”4.27.4″ global_colors_info=”{}”][\/et_pb_divider][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.27.6″ _module_preset=”default” hover_enabled=”0″ sticky_enabled=”0″][et_pb_column _builder_version=”4.27.6″ _module_preset=”default” type=”4_4″][dsm_card_carousel _builder_version=”4.27.6″ _module_preset=”default” hover_enabled=”0″ sticky_enabled=”0″ slide_to_show=”4″ equal_height=”on”][dsm_card_carousel_child title=”Prof. Jianqing Fan” image=”https:\/\/easyconferences.eu\/isnps2026\/wp-content\/uploads\/2026\/03\/Jianqing-Fan-scaled.jpg” button_text=”View More” _builder_version=”4.27.6″ _module_preset=”default” parent_button=”off” parent_header_level=”h4″ subtitle=”Princeton University” button_url=”#jianqing-fan” title_text=”Jianqing Fan” image_background_height=”279px” hover_enabled=”0″ sticky_enabled=”0″][\/dsm_card_carousel_child][dsm_card_carousel_child title=”Prof. Xuming He” image=”https:\/\/easyconferences.eu\/isnps2026\/wp-content\/uploads\/2026\/03\/Xuming-He.png” button_text=”View More” _builder_version=”4.27.6″ _module_preset=”default” parent_button=”off” parent_header_level=”h4″ subtitle=”Washington University in St. Louis” title_text=”Xuming He” image_background_height=”279px” button_url=”#xuming-he” hover_enabled=”0″ sticky_enabled=”0″][\/dsm_card_carousel_child][dsm_card_carousel_child title=”Prof. Dimitris N. Politis” image=”https:\/\/easyconferences.eu\/isnps2026\/wp-content\/uploads\/2026\/06\/dimitris-n-politis.jpg” button_text=”View More” _builder_version=”4.27.6″ _module_preset=”default” parent_button=”off” parent_header_level=”h4″ subtitle=”University of California at San Diego” image_background_height=”279px” button_url=”#dimitris-politis” hover_enabled=”0″ sticky_enabled=”0″ title_text=”dimitris-n-politis”][\/dsm_card_carousel_child][dsm_card_carousel_child title=”Prof. Ingrid van Keilegom” image=”https:\/\/easyconferences.eu\/isnps2026\/wp-content\/uploads\/2026\/06\/Ingrid-van-Keilegom.jpg” button_text=”View More” _builder_version=”4.27.6″ _module_preset=”default” parent_button=”off” parent_header_level=”h4″ subtitle=”KU Leuven” title_text=”Ingrid-van-Keilegom” image_background_height=”279px” button_url=”#ingrid-van-keilegom” hover_enabled=”0″ sticky_enabled=”0″][\/dsm_card_carousel_child][dsm_card_carousel_child title=”Prof. Daniela Witten” image=”https:\/\/easyconferences.eu\/isnps2026\/wp-content\/uploads\/2026\/03\/Daniela-Witten.jpg” button_text=”View More” _builder_version=”4.27.6″ _module_preset=”default” parent_button=”off” parent_header_level=”h4″ subtitle=”University of Washington” image_background_height=”279px” title_text=”Daniela Witten” button_url=”#daniela-witten” hover_enabled=”0″ sticky_enabled=”0″][\/dsm_card_carousel_child][\/dsm_card_carousel][et_pb_divider _builder_version=”4.27.4″ global_colors_info=”{}”][\/et_pb_divider][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ _builder_version=”4.27.6″ _module_preset=”default” link_option_url_new_window=”on” hover_enabled=”0″ global_colors_info=”{}” module_id=”jianqing-fan” sticky_enabled=”0″][et_pb_column type=”1_4″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/easyconferences.eu\/isnps2026\/wp-content\/uploads\/2026\/03\/Jianqing-Fan-scaled.jpg” title_text=”Jianqing Fan” url=”http:\/\/fan.princeton.edu” url_new_window=”on” _builder_version=”4.27.6″ _module_preset=”default” border_radii=”on|26px|26px|26px|26px” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n
by Professor Jianqing Fan, Princeton University<\/strong>[\/et_pb_text][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n VIEW PROFILE<\/a><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n Classification and diffusion-induced neural density estimators and simulators for generative AI[\/et_pb_text][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n Neural network-based methods for conditional density estimation have recently gained substantial attention, as various neural density estimators have outperformed classical approaches in real-data experiments. Despite these empirical successes, implementation can be challenging due to the need to ensure non-negativity and unit-mass constraints, and theoretical understanding remains limited. In particular, it is unclear whether such estimators can adaptively achieve faster convergence rates when the underlying density exhibits a low-dimensional structure. This paper addresses these gaps by proposing a structure-agnostic neural density estimator, called the classification-induced neural density estimator and simulator (CINDES) that is straightforward to implement and provably adaptive, attaining faster rates when the true density admits a low-dimensional composition structure. Another key contribution of our work is to show that the proposed estimator integrates naturally into generative sampling pipelines, most notably score-based diffusion models, where it achieves provably faster convergence when the underlying density is structured. We validate its performance through extensive simulations and a real-data application. We also prove the optimality of score-based diffusion models for density estimation when the target density admits a factorizable, low-dimensional, nonparametric structure in a separate work. The main challenge is that the low-dimensional, factorizable structure no longer holds for most diffused timesteps, and it is very difficult to show that these diffused score functions can be well approximated without a significant increase in the number of network parameters. [\/et_pb_text][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n Jianqing Fan, Academician of Academia Sinica and member of Royal Academy of Belgium, is the Frederick L. Moore Professor at Princeton University. He was a professor at UNC-Chapel Hill, UCLA, and the Chinese University of Hong Kong, and the president of the Institute of Mathematical Statistics and the International Chinese Statistical Association. He is the joint editor of the Journal of the American Statistical Associationand was the co-editor of The Annals of Statistics, Probability Theory and Related Fields, Econometrics Journal, Journal of Econometrics, and Journal of Business and Economics Statistics. His research interests include high-dimensional statistics, data science, machine learning, mathematics of AI, financial economics, and computational biology. He coauthored 4 books and published over 300 papers. His published work has been recognized by The 2000 COPSS Presidents’ Award, Morningside Gold Medal of Applied Mathematics, Guggenheim Fellow, P.L. Hsu Prize, Guy medal in silver, Noether Distinguished Scholar Award, Le Cam Award and Lecture, Frontiers of Science Award, and Wald Memorial Award and Lecture, and follow of American Associations for Advancement of Science, Institute of Mathematical Statistics, American Statistical Association, and Society of Financial Econometrics<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ _builder_version=”4.27.6″ _module_preset=”default” link_option_url_new_window=”on” hover_enabled=”0″ global_colors_info=”{}” module_id=”xuming-he” sticky_enabled=”0″][et_pb_column type=”1_4″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/easyconferences.eu\/isnps2026\/wp-content\/uploads\/2026\/03\/Xuming-He.png” title_text=”Xuming He” url=”https:\/\/sds.wustl.edu\/people\/xuming-he” url_new_window=”on” _builder_version=”4.27.6″ _module_preset=”default” border_radii=”on|26px|26px|26px|26px” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n by Professor Xuming He, Washington University in St. Louis<\/strong><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n VIEW PROFILE<\/a><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n Distributed-oracle estimation for high-dimensional quantile regression<\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n Quantile regression (QR) is a valuable tool for analyzing heterogeneous covariate effects across the entire outcome distribution including lower and upper tails. However, implementing QR in high-dimensional settings where data are decentralized presents computational and communication hurdles. We propose a communication-efficient estimator for high-dimensional QR designed for data distributed across multiple machines. To use folded-concave penalties, we develop an iterative multi-step (IM) algorithm utilizing a surrogate smoothed quantile loss. This approach effectively balances statistical efficiency with communication constraints. To provide a theoretical foundation for our method, we introduce the concept of a distributed-oracle estimation and demonstrate that the IM estimator converges to this oracle with high probability. Furthermore, we extend our framework to enable distributed inference for specific low-dimensional components of interest. This talk is based on joint work with SongshanYang, Yifan Gu, and Hangfang Yang.<\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n Xuming He is the Kotzubei-Beckmann Distinguished Professor and Inaugural Chair of Statistics and Data Science at the Washington University in St. Louis. His research focuses on robust statistics, semiparametric regression, Bayesian inference, and post-selection inference. His interdisciplinary work promotes the application of statistics and data science across various fields, including biosciences, public health, and socioeconomic studies.<\/p>\n He is an elected Fellow of the American Association for the Advancement of Science (AAAS), the American Statistical Association (ASA), and the Institute of Mathematical Statistics (IMS). He is Past President of the International Statistical Institute (ISI), and currently serves as a Joint Editor of the Journal of the Royal Statistical Society \u2013 Series B. His recent honors and awards include the IMS Carver Medal (2022), ASA Founders Award (2021), and the Gottfried E. Noether Distinguished Scholar Award (2025) from the ASA.<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ _builder_version=”4.27.6″ _module_preset=”default” link_option_url_new_window=”on” hover_enabled=”0″ global_colors_info=”{}” module_id=”politis-dimitris” sticky_enabled=”0″][et_pb_column type=”1_4″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/easyconferences.eu\/isnps2026\/wp-content\/uploads\/2026\/06\/dimitris-n-politis.jpg” title_text=”dimitris-n-politis” url=”https:\/\/www.biostat.washington.edu\/people\/daniela-witten” url_new_window=”on” _builder_version=”4.27.6″ _module_preset=”default” hover_enabled=”0″ border_radii=”on|26px|26px|26px|26px” global_colors_info=”{}” sticky_enabled=”0″][\/et_pb_image][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n by Professor Dimitris N. Politis, of University of California at San Diego<\/strong><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n VIEW PROFILE<\/a><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n Predictive Inference in Nonparametric Regression: Model-free Bootstrap, Conformal Prediction, and Pertinent Prediction Intervals<\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n Predictive inference in a general regression setting is attracting progressively more attention in the big-data era. Given a nonparametric model driven by i.i.d. errors, model-based bootstrap procedures can be devised to yield prediction intervals for a future response associated with a regressor value of interest. Ideally, the bootstrap procedure will be designed to mimic\/incorporate the variability of all estimated components; in this case, the resulting prediction intervals are called `pertinent’. If a model equation is not available, there are three possible avenues: Model-free bootstrap, conformal prediction, and quantile estimation. The three approaches will be contrasted via theoretical analysis as well as numerical experiments with a focus on conditional coverage; in particular, three notions of conditionality will be described that are nested in terms of increasing strength. Under mild conditions, we can show that the Model-free bootstrap yields prediction intervals with guaranteed better conditional coverage compared to quantile estimation using any one of the three notions of conditionality. We also extend the concept of pertinence of prediction intervals to the nonparametric regression setting, and give concrete examples where its importance emerges under finite sample scenarios. Time-permitting, an application of Model-free and conformal prediction to Markov sequences will be given.<\/p>\n [Joint work with Yiren Wang, Dehao Dai, and Kejin Wu.]<\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n Dimitris N. Politis earned his Ph.D. in Statistics from Stanford University after a tour of various disciplines including Mathematics, Electrical Engineering, and Computer and Systems Engineering. He held tenured faculty positions at Purdue University and the University of Cyprus before moving to the University of California at San Diego\u00a0where he currently serves as Distinguished Professor of Mathematics and Data Science, and holds the Halicioglu Data Science Institute Chancellor’s Endowed Chair.<\/p>\n <\/p>\n His research in the last thirty years has been in the general area of Nonparametric Statistics, and in particular on Time Series Analysis, Resampling and Subsampling for Dependent Observations, Spatial Statistics, Random Fields and Point Processes, Information Theory and Signal Processing, Nonparametric Function Estimation, Spectral and Probability Density Estimation, and Model-free Prediction and Regression. \u00a0<\/p>\n <\/p>\n He has (co)authored three books and over 150 journal papers, and has given numerous lectures and seminars worldwide. He has organized several international workshops and conferences, and has served on the Editorial Board of a number of journals. He is a Fellow of the Institute of Mathematical Statistics and of the American Statistical Association, co-founder of the International Society for Nonparametric Statistics, and former Fellow of the Guggenheim Foundation.<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ _builder_version=”4.27.6″ _module_preset=”default” link_option_url_new_window=”on” hover_enabled=”0″ global_colors_info=”{}” module_id=”ingrid-van-keilegom” sticky_enabled=”0″][et_pb_column type=”1_4″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/easyconferences.eu\/isnps2026\/wp-content\/uploads\/2026\/06\/Ingrid-van-Keilegom.jpg” title_text=”Ingrid-van-Keilegom” url=”https:\/\/www.biostat.washington.edu\/people\/daniela-witten” url_new_window=”on” _builder_version=”4.27.6″ _module_preset=”default” border_radii=”on|26px|26px|26px|26px” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\nTitle<\/h4>\n
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(Join works with Yihong Gu, Dehao Dai, Mukherjee, and Ximing Li)<\/p>\nBiography<\/h4>\n
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