{"id":4459,"date":"2024-01-23T16:17:26","date_gmt":"2024-01-23T16:17:26","guid":{"rendered":"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/?page_id=4459"},"modified":"2025-01-23T17:08:29","modified_gmt":"2025-01-23T17:08:29","slug":"louis-chen","status":"publish","type":"page","link":"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/speakers\/louis-chen\/","title":{"rendered":"Louis Chen"},"content":{"rendered":"<!--themify_builder_content-->\n<div id=\"themify_builder_content-4459\" data-postid=\"4459\" class=\"themify_builder_content themify_builder_content-4459 themify_builder tf_clear\">\n                    <div  data-lazy=\"1\" class=\"module_row themify_builder_row dropPadding-bottom tb_0l52602 tb_first tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_2 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col4-1 tb_0ygc603 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_esex603 image-top   tf_mw\" data-lazy=\"1\">\n        <div class=\"image-wrap tf_rel tf_mw\">\n            <img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"300\" src=\"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/files\/2024\/01\/Louis_Chen.jpg\" class=\"wp-post-image wp-image-4405\" title=\"Louis Chen\" alt=\"Louis Chen headshot\" srcset=\"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/files\/2024\/01\/Louis_Chen.jpg 300w, https:\/\/meetings.informs.org\/wordpress\/analytics2024\/files\/2024\/01\/Louis_Chen-150x150.jpg 150w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/>    \n        <\/div>\n    <!-- \/image-wrap -->\n    \n        <div class=\"image-content\">\n                    <h3 class=\"image-title\">\n                                    Louis Chen                            <\/h3>\n                    <\/div>\n    <!-- \/image-content -->\n        <\/div>\n<!-- \/module image -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column tb-column col4-3 tb_g2w3604 last\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_t7c7604   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h1>Louis Chen<\/h1>\n<h5>Asst. Professor, Operations Research at Naval Postgraduate School<\/h5>\n    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_mq1h163 \" data-behavior=\"toggle\" data-lazy=\"1\">\n    \n    <ul class=\"ui module-accordion  glossy tb_default_color\">\n            <li>\n            <div class=\"accordion-title tf_rel\">\n                <a href=\"#acc-mq1h163-0\" class=\"tb_title_accordion\" aria-controls=\"acc-mq1h163-0-content\" aria-expanded=\"false\">\n                    <i class=\"accordion-icon\"><svg  class=\"tf_fa tf-ti-plus\" aria-hidden=\"true\"><use href=\"#tf-ti-plus\"><\/use><\/svg><\/i>                    <i class=\"accordion-active-icon tf_hide\"><svg  class=\"tf_fa tf-ti-minus\" aria-hidden=\"true\"><use href=\"#tf-ti-minus\"><\/use><\/svg><\/i>                    <span class=\"accordion-title-wrap\">Bio<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-mq1h163-0-content\" data-id=\"acc-mq1h163-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                                    <div class=\"tb_text_wrap\">\n                        <p>Louis Chen is an Assistant Professor of Operations Research at the Naval Postgraduate School. He obtained his PhD in Operations Research at MIT (2019). His research work and interests lie in (Distributionally) Robust Optimization, decision-making under uncertainty, and adversarial robust machine learning. He also studies operations areas like inventory, scheduling, and networks.<\/p>                    <\/div>\n                            <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-css_id=\"u6fe602\" data-lazy=\"1\" class=\"module_row themify_builder_row fullwidth_row_container tb_u6fe602 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_1 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col-full tb_84q1605 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_k572608 track  \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h6>Track: Machine Learning &amp; AI<\/h6>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module text -->\n<div  class=\"module module-text tb_xwmz608   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h2>On the Adversarial Robustness of Benjamini-Hochberg<\/h2>\n<p>The Benjamini-Hochberg (BH) procedure is widely used to control the false detection rate (FDR) in large-scale hypothesis testing. We investigate the effect that adversarial perturbations may have on this control; in particular, we demonstrate that the FDR guarantee can be badly broken with minimal perturbation to a single p-value, highlighting the fragility of the BH procedure. We focus on identifying the perturbation of a single p-value that generates the largest adversarially-adjusted FDR, and provide: (1) an efficient (linear-time) optimal algorithm; (2) non-asymptotic guarantees on the expected adversarial-adjustment to FDR; and (3) numerical experiments on data sets to illustrate this effect. The takeaway is that the probabilistic dependence among the reject\/accept decisions generated by the BH procedure can be hacked. Our technical analysis involves reframing the BH procedure as a \u201cballs into bins\u201d process, and drawing a connection to generalized ballot problems that facilitates an information-theoretic approach for deriving non-asymptotic lower bounds.<\/p>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n        <\/div>\n<!--\/themify_builder_content-->","protected":false},"excerpt":{"rendered":"<p>Louis Chen Asst. 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Professor, Operations Research at Naval Postgraduate School<\/h5>\n<ul><li><h4>Bio<\/h4><p>Louis Chen is an Assistant Professor of Operations Research at the Naval Postgraduate School. He obtained his PhD in Operations Research at MIT (2019). His research work and interests lie in (Distributionally) Robust Optimization, decision-making under uncertainty, and adversarial robust machine learning. He also studies operations areas like inventory, scheduling, and networks.<\/p><\/li><\/ul>\n<h6>Track: Machine Learning &amp; AI<\/h6>\n<h2>On the Adversarial Robustness of Benjamini-Hochberg<\/h2> <p>The Benjamini-Hochberg (BH) procedure is widely used to control the false detection rate (FDR) in large-scale hypothesis testing. We investigate the effect that adversarial perturbations may have on this control; in particular, we demonstrate that the FDR guarantee can be badly broken with minimal perturbation to a single p-value, highlighting the fragility of the BH procedure. We focus on identifying the perturbation of a single p-value that generates the largest adversarially-adjusted FDR, and provide: (1) an efficient (linear-time) optimal algorithm; (2) non-asymptotic guarantees on the expected adversarial-adjustment to FDR; and (3) numerical experiments on data sets to illustrate this effect. The takeaway is that the probabilistic dependence among the reject\/accept decisions generated by the BH procedure can be hacked. Our technical analysis involves reframing the BH procedure as a \u201cballs into bins\u201d process, and drawing a connection to generalized ballot problems that facilitates an information-theoretic approach for deriving non-asymptotic lower bounds.<\/p>","_links":{"self":[{"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/wp-json\/wp\/v2\/pages\/4459","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/wp-json\/wp\/v2\/users\/1001140"}],"replies":[{"embeddable":true,"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/wp-json\/wp\/v2\/comments?post=4459"}],"version-history":[{"count":3,"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/wp-json\/wp\/v2\/pages\/4459\/revisions"}],"predecessor-version":[{"id":7050,"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/wp-json\/wp\/v2\/pages\/4459\/revisions\/7050"}],"up":[{"embeddable":true,"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/wp-json\/wp\/v2\/pages\/712"}],"wp:attachment":[{"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2024\/wp-json\/wp\/v2\/media?parent=4459"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}