{"610220":{"#nid":"610220","#data":{"type":"event","title":"ARC-TRIAD Colloquium: Michael Mitzenmacher (Harvard)","body":[{"value":"\u003Cp align = \u0022center\u0022\u003E\u003Cstrong\u003EARC-TRIAD Colloquium\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp align = \u0022center\u0022\u003E\u003Cstrong\u003EMichael Mitzenmacher\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp align = \u0022center\u0022\u003E\u003Cstrong\u003EMonday, November 26, 2018\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp align = \u0022center\u0022\u003E\u003Cstrong\u003EKlaus 1116 East \u0026ndash; 11:00 am\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ETitle:\u0026nbsp; \u003C\/strong\u003EBloom Filters, Cuckoo Hashing, Cuckoo Filters, Adaptive Cuckoo Filters, and Learned Bloom Filters\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u0026nbsp; I will go over some of my past and present work on hashing-based data structures.\u0026nbsp; After presenting some background on Bloom filters and cuckoo hashing, we will describe cuckoo filters, an efficient data structure for approximate set membership that improves on the well-known Bloom filter. We then discuss recent work on how to make cuckoo filters adaptive in response to false positives, which can be important for many practical problems.\u0026nbsp; Finally, I will present some very recent work on how to possibly improve Bloom filters and related data structures using machine learning techniques.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E----------------------------------\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Ca href=\u0022http:\/\/www.eecs.harvard.edu\/~michaelm\/\u0022\u003ESpeaker\u0026#39;s Webpage\u003C\/a\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cem\u003EVideos of recent talks are available at: \u003C\/em\u003E\u003Ca href=\u0022https:\/\/smartech.gatech.edu\/handle\/1853\/46836\u0022\u003E\u003Cem\u003Ehttps:\/\/smartech.gatech.edu\/handle\/1853\/46836\u003C\/em\u003E\u003C\/a\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Ca href=\u0022https:\/\/mailman.cc.gatech.edu\/mailman\/listinfo\/arc-colloq\u0022\u003E\u003Cem\u003EClick here to subscribe to the seminar email list: arc-colloq@cc.gatech.edu \u003C\/em\u003E\u003C\/a\u003E\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Bloom Filters, Cuckoo Hashing, Cuckoo Filters, Adaptive Cuckoo Filters, and Learned Bloom Filters - Klaus 1116E at 11 am"}],"uid":"27544","created_gmt":"2018-08-23 12:03:55","changed_gmt":"2018-11-02 18:00:23","author":"Francella Tonge","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2018-11-26T11:00:00-05:00","event_time_end":"2018-11-26T12:00:00-05:00","event_time_end_last":"2018-11-26T12:00:00-05:00","gmt_time_start":"2018-11-26 16:00:00","gmt_time_end":"2018-11-26 17:00:00","gmt_time_end_last":"2018-11-26 17:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"70263","name":"ARC"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"607600":{"#nid":"607600","#data":{"type":"event","title":"ARC-TRIAD Colloquium: Mary Wootters (Stanford)","body":[{"value":"\u003Cp align = \u0022center\u0022\u003E\u003Cstrong\u003EARC-TRIAD Colloquium\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp align = \u0022center\u0022\u003E\u003Cstrong\u003EMary Wootters\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp align = \u0022center\u0022\u003E\u003Cstrong\u003EMonday, October 1, 2018\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp align = \u0022center\u0022\u003E\u003Cstrong\u003EMiRC Pettit 102A\u0026amp;B - 11:00 am\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ETitle:\u0026nbsp; \u003C\/strong\u003EImproved Decoding of Folded Reed-Solomon and Multiplicity Codes\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u0026nbsp; List-decoding is an important primitive in the theory of error correcting codes, and it has long been a goal to obtain explicit constructions of capacity-achieving, efficiently list-decodable codes.\u0026nbsp; Folded Reed-Solomon Codes (Guruswami-Rudra 2008) and Multiplicity codes (Guruswami-Wang 2011, Kopparty 2012) are two such constructions.\u0026nbsp; However, previous analysis of these codes could not guarantee optimal parameters.\u0026nbsp; In particular, the \u0026ldquo;list-size\u0026rdquo; of these codes was only shown to be polynomial, while ideally it would be constant.\u0026nbsp; Thus, over the past decade or so, there have been several modifications of these codes aimed at reducing the list size to constant.\u0026nbsp; In this work, we show that in fact the list-sizes were constant all along, with no modifications required!\u0026nbsp; Further, we use our result for univariate multiplicity codes to establish improved local list-decoding results for multivariate multiplicity codes.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EIn this talk, I\u0026rsquo;ll define all the terms in the paragraph above (in particular, no prior knowledge of error correcting codes is necessary!), and sketch the proofs of the results mentioned above.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EJoint work with Swastik Kopparty, Noga Ron-Zewi, and Shubhangi Saraf.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E----------------------------------\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Ca href=\u0022https:\/\/sites.google.com\/site\/marywootters\/\u0022\u003ESpeaker\u0026#39;s Webpage\u003C\/a\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cem\u003EVideos of recent talks are available at: \u003C\/em\u003E\u003Ca href=\u0022https:\/\/smartech.gatech.edu\/handle\/1853\/46836\u0022\u003E\u003Cem\u003Ehttps:\/\/smartech.gatech.edu\/handle\/1853\/46836\u003C\/em\u003E\u003C\/a\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Ca href=\u0022https:\/\/mailman.cc.gatech.edu\/mailman\/listinfo\/arc-colloq\u0022\u003E\u003Cem\u003EClick here to subscribe to the seminar email list: arc-colloq@cc.gatech.edu \u003C\/em\u003E\u003C\/a\u003E\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Improved Decoding of Folded Reed-Solomon and Multiplicity Codes - MiRC Pettit 102 A\u0026B at 11 am"}],"uid":"27544","created_gmt":"2018-07-10 14:31:26","changed_gmt":"2018-10-01 13:10:14","author":"Francella Tonge","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2018-10-01T12:00:00-04:00","event_time_end":"2018-10-01T13:00:00-04:00","event_time_end_last":"2018-10-01T13:00:00-04:00","gmt_time_start":"2018-10-01 16:00:00","gmt_time_end":"2018-10-01 17:00:00","gmt_time_end_last":"2018-10-01 17:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"70263","name":"ARC"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1792","name":"Arts and Performance"},{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"607350":{"#nid":"607350","#data":{"type":"event","title":"ARC-TRIAD Colloquium: Leslie Valiant (Harvard)","body":[{"value":"\u003Cp align = \u0022center\u0022\u003E\u003Cstrong\u003EARC-TRIAD Colloquium\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp align = \u0022center\u0022\u003E\u003Cstrong\u003ELeslie Valiant\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp align = \u0022center\u0022\u003E\u003Cstrong\u003EMonday, October 22, 2018\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp align = \u0022center\u0022\u003E\u003Cstrong\u003EKlaus 1116 East \u0026amp; West\u0026nbsp; \u0026ndash; 11:00 am\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ETitle:\u0026nbsp; \u003C\/strong\u003EWhere Computer Science Meets Neuroscience\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u0026nbsp; For some problems in science there are several plausible theories and it remains to experimenters to determine which of them, if any, are valid. There exist other problems for which, in contrast, no known theory is widely accepted as plausible. Currently computational neuroscience is a field full of opportunity that offers several fundamental problems of the latter kind. We shall discuss one of these problems: Over a lifetime the brain performs hundreds of thousands of individual cognitive acts, of a variety of kinds, including the formation of new associations. Each such act depends on past experience, and, in turn, can have long lasting effects on future behavior. It is difficult to reconcile such large scale capabilities, including fast reaction times on new inputs when using knowledge acquired at various earlier times, with the known resource constraints on cortex, such as low connectivity and low average synaptic strength. Here we shall describe an approach to this fundamental problem that attempts to explain these phenomena in terms of concrete algorithms for a model of computation that is faithful to the most basic quantitative resources.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E----------------------------------\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Ca href=\u0022https:\/\/www.seas.harvard.edu\/directory\/valiant\u0022\u003ESpeaker\u0026#39;s Webpage\u003C\/a\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cem\u003EVideos of recent talks are available at: \u003C\/em\u003E\u003Ca href=\u0022https:\/\/smartech.gatech.edu\/handle\/1853\/46836\u0022\u003E\u003Cem\u003Ehttps:\/\/smartech.gatech.edu\/handle\/1853\/46836\u003C\/em\u003E\u003C\/a\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Ca href=\u0022https:\/\/mailman.cc.gatech.edu\/mailman\/listinfo\/arc-colloq\u0022\u003E\u003Cem\u003EClick here to subscribe to the seminar email list: arc-colloq@cc.gatech.edu \u003C\/em\u003E\u003C\/a\u003E\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Where Computer Science Meets Neuroscience - Klaus 1116 E \u0026 W at 11 am"}],"uid":"27544","created_gmt":"2018-06-27 19:15:09","changed_gmt":"2018-10-01 13:07:01","author":"Francella Tonge","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2018-10-22T12:00:00-04:00","event_time_end":"2018-10-22T13:00:00-04:00","event_time_end_last":"2018-10-22T13:00:00-04:00","gmt_time_start":"2018-10-22 16:00:00","gmt_time_end":"2018-10-22 17:00:00","gmt_time_end_last":"2018-10-22 17:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"70263","name":"ARC"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"601435":{"#nid":"601435","#data":{"type":"event","title":"ARC-TRIAD Colloquium:  Piotr Indyk (MIT)","body":[{"value":"\u003Cp align = \u0022center\u0022\u003E\u003Cstrong\u003EAlgorithms \u0026amp; Randomness Center (ARC) and TRIAD\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp align = \u0022center\u0022\u003E\u003Cstrong\u003EPiotr Indyk\u0026nbsp;(MIT)\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp align = \u0022center\u0022\u003E\u003Cstrong\u003EMonday, March 5, 2018\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp align = \u0022center\u0022\u003E\u003Cstrong\u003EKlaus 1116 East - 11am\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E\u0026nbsp; \u0026nbsp;\u0026quot;Below P vs. NP: Conditional Quadratic-Time Hardness for Big Data Problems\u0026quot;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract:\u0026nbsp; \u003C\/strong\u003E \u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EThe theory of NP-hardness has been very successful in identifying problems that are unlikely to have general purpose polynomial time algorithms. However, many other important problems do have polynomial time algorithms, but large exponents in their time bounds can make them run for days, weeks or more. For example, quadratic time algorithms, although practical on moderately sized inputs, can become inefficient on problems that involve gigabytes or more of data. Although for many problems no subquadratic time algorithms are known, evidence of quadratic-time hardness has remained elusive.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EIn this talk, I will give an overview of recent research that aims to remedy this situation. In particular, I will describe hardness results for problems in string processing (e.g., edit distance computation or regular expression matching) and machine learning (e.g., support vector machines or batch gradient computation in neural networks). All of them have polynomial time algorithms, but despite an extensive amount of research, no near-linear time algorithms have been found for many variants of these problems. I will show that, under a natural complexity-theoretic conjecture, such algorithms do not exist. I will also describe how this framework has led to the development of new algorithms.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E--------------------------------------\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Ca href=\u0022https:\/\/people.csail.mit.edu\/indyk\/\u0022\u003ESpeaker\u0026#39;s webpage\u003C\/a\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cem\u003EVideos of recent talks are available at: \u003C\/em\u003E\u003Ca href=\u0022https:\/\/smartech.gatech.edu\/handle\/1853\/46836\u0022\u003E\u003Cem\u003Ehttps:\/\/smartech.gatech.edu\/handle\/1853\/46836\u003C\/em\u003E\u003C\/a\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Ca href=\u0022https:\/\/mailman.cc.gatech.edu\/mailman\/listinfo\/arc-colloq\u0022\u003E\u003Cem\u003EClick here to subscribe to the seminar email list: arc-colloq@cc.gatech.edu \u003C\/em\u003E\u003C\/a\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"\u0022Below P vs. NP: Conditional Quadratic-Time Hardness for Big Data Problems\u0022 - Klaus 1116E at 11am"}],"uid":"32895","created_gmt":"2018-01-26 19:32:00","changed_gmt":"2018-02-27 14:01:59","author":"Eric Vigoda","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2018-03-05T11:00:00-05:00","event_time_end":"2018-03-05T12:00:00-05:00","event_time_end_last":"2018-03-05T12:00:00-05:00","gmt_time_start":"2018-03-05 16:00:00","gmt_time_end":"2018-03-05 17:00:00","gmt_time_end_last":"2018-03-05 17:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"70263","name":"ARC"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"78771","name":"Public"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"600861":{"#nid":"600861","#data":{"type":"event","title":"ARC-TRIAD Seminar - Yan Shuo Tan (Michigan)","body":[{"value":"\u003Cp align = \u0022center\u0022\u003E\u003Cstrong\u003EARC-TRIAD\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp align = \u0022center\u0022\u003E\u003Cstrong\u003EYan Shuo Tan (Michigan)\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp align = \u0022center\u0022\u003E\u003Cstrong\u003EMonday, January 22, 2018\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp align = \u0022center\u0022\u003E\u003Cstrong\u003EPettit Microelectonics Bldg. \u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp align = \u0022center\u0022\u003E\u003Cstrong\u003EPettit Rm 102A  -  2:00 pm\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ETitle:\u0026nbsp; \u003C\/strong\u003EEfficient algorithms for phase retrieval in high dimensions\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u0026nbsp; Mathematical phase retrieval is the problem of solving systems of rank-1 quadratic equations. Over the last few years, there has been much interest in constructing algorithms with provable guarantees. Both theoretically and empirically, the most successful approaches have involved direct optimization of non-convex loss functions. In the first half of this talk, we will discuss how SGD for one of these loss functions provably results in (rapid) linear convergence with high probability. In the second half of the talk, we will discuss a semidefinite programming algorithm that simultaneously makes use of a sparsity prior on the solution vector, while overcoming possible model misspecification.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E----------------------------------\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Ca href=\u0022http:\/\/www-personal.umich.edu\/~yanshuo\/\u0022\u003ESpeaker\u0026#39;s Webpage\u003C\/a\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cem\u003EVideos of recent talks are available at: \u003C\/em\u003E\u003Ca href=\u0022https:\/\/smartech.gatech.edu\/handle\/1853\/46836\u0022\u003E\u003Cem\u003Ehttps:\/\/smartech.gatech.edu\/handle\/1853\/46836\u003C\/em\u003E\u003C\/a\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Ca href=\u0022https:\/\/mailman.cc.gatech.edu\/mailman\/listinfo\/arc-colloq\u0022\u003E\u003Cem\u003EClick here to subscribe to the seminar email list: arc-colloq@cc.gatech.edu \u003C\/em\u003E\u003C\/a\u003E\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Efficient algorithms for phase retrieval in high dimensions"}],"uid":"27544","created_gmt":"2018-01-16 17:22:12","changed_gmt":"2018-01-19 15:33:31","author":"Francella Tonge","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2018-01-22T14:00:00-05:00","event_time_end":"2018-01-22T15:00:00-05:00","event_time_end_last":"2018-01-22T15:00:00-05:00","gmt_time_start":"2018-01-22 19:00:00","gmt_time_end":"2018-01-22 20:00:00","gmt_time_end_last":"2018-01-22 20:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"70263","name":"ARC"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"600607":{"#nid":"600607","#data":{"type":"event","title":"ARC-TRIAD Seminar - Cong Han Lim (Wisconsin)","body":[{"value":"\u003Cp align = \u0022center\u0022\u003E\u003Cstrong\u003EARC-TRIAD\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp align = \u0022center\u0022\u003E\u003Cstrong\u003ECong Han Lim (Wisconsin)\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp align = \u0022center\u0022\u003E\u003Cstrong\u003EWednesday, January 17, 2018\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp align = \u0022center\u0022\u003E\u003Cstrong\u003EGroseclose 402 - 10:00 am\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ETitle:\u0026nbsp; \u003C\/strong\u003ETowards Large-Scale Nonconvex\/Stochastic Discrete Optimization\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u0026nbsp; Modern data analytics is powered by scalable mathematical optimization methods. For decision-making, we want to be able to solve large-scale mathematical problems that include discrete choices or structures. These can already be very challenging to solve exactly even when the objective and feasible region are convex. We want to be able to model more general concepts that naturally lead to huge or nonconvex formulations, such as robustness to uncertainty, economic ideas like economies of scale, and physical concepts in engineering applications such as power systems and water network design.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;In this talk, I will present techniques for handling two such families of problems. I will demonstrate a new class of cutting planes for mixed-integer programs with separable concave costs and show that they can be combined with existing cuts for canonical mixed-integer linear sets. For stochastic mixed-integer programs, I will describe a new subgradient method for solving the dual decomposition that parallelizes significantly better than traditional subgradient on modern distributed and multi-core computer architectures. I will conclude by discussing some future directions in machine learning and (stochastic) mixed-integer programming.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E----------------------------------\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Ca href=\u0022https:\/\/limconghan.github.io\/\u0022\u003ESpeaker\u0026#39;s Webpage\u003C\/a\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cem\u003EVideos of recent talks are available at: \u003C\/em\u003E\u003Ca href=\u0022https:\/\/smartech.gatech.edu\/handle\/1853\/46836\u0022\u003E\u003Cem\u003Ehttps:\/\/smartech.gatech.edu\/handle\/1853\/46836\u003C\/em\u003E\u003C\/a\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Ca href=\u0022https:\/\/mailman.cc.gatech.edu\/mailman\/listinfo\/arc-colloq\u0022\u003E\u003Cem\u003EClick here to subscribe to the seminar email list: arc-colloq@cc.gatech.edu \u003C\/em\u003E\u003C\/a\u003E\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Towards Large-Scale Nonconvex\/Stochastic Discrete Optimization"}],"uid":"27544","created_gmt":"2018-01-10 13:32:13","changed_gmt":"2018-01-12 13:35:03","author":"Francella Tonge","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2018-01-17T10:00:00-05:00","event_time_end":"2018-01-17T11:00:00-05:00","event_time_end_last":"2018-01-17T11:00:00-05:00","gmt_time_start":"2018-01-17 15:00:00","gmt_time_end":"2018-01-17 16:00:00","gmt_time_end_last":"2018-01-17 16:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"70263","name":"ARC"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}