{"659546":{"#nid":"659546","#data":{"type":"news","title":"Georgia Tech Researchers Present New Machine Learning Methods and Applications at ICML 2022","body":[{"value":"\u003Cp\u003EGeorgia Tech researchers have new published research this week at the International Conference on Machine Learning (ICML), one of the leading international academic conferences in machine learning, the field of computer science that gives computer systems the ability to learn from data. ICML 2022 runs through Saturday in Baltimore, Maryland.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EThe research venue is globally renowned for presenting and publishing\u0026nbsp;cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EGeorgia Tech researchers are featured throughout the technical program for new contributions to ML methods and applications. There are 15 papers from Tech in the main program and workshops.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EICML\u0026rsquo;s top research tier \u0026ndash; oral papers \u0026ndash; includes two works from Georgia Tech\u0026rsquo;s H. Milton Stewart School of Industrial and Systems Engineering.\u003C\/p\u003E\r\n\r\n\u003Cp\u003ETech researchers are presenting in the following sessions:\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003EAdaptive Experimental Design and Active Learning in the Real World (ReALML)\u003C\/li\u003E\r\n\t\u003Cli\u003EBeyond Bayes: Paths Towards Universal Reasoning Systems\u003C\/li\u003E\r\n\t\u003Cli\u003EDeep Learning: SSL\/GNN\u003C\/li\u003E\r\n\t\u003Cli\u003EDeep Learning\/Optimization\u003C\/li\u003E\r\n\t\u003Cli\u003EDeep Learning: Theory\u003C\/li\u003E\r\n\t\u003Cli\u003ENew Frontiers in Adversarial Machine Learning\u003C\/li\u003E\r\n\t\u003Cli\u003EOptimization: Convex\u003C\/li\u003E\r\n\t\u003Cli\u003EPM: Monte Carlo and Sampling Methods\u003C\/li\u003E\r\n\t\u003Cli\u003EPM: Variational Inference\/Bayesian Models and Methods\u003C\/li\u003E\r\n\t\u003Cli\u003EStable Conformal Prediction Sets\u003C\/li\u003E\r\n\t\u003Cli\u003ET: Online Learning and Bandits\u003C\/li\u003E\r\n\t\u003Cli\u003ETheory\/Social Aspects\u003C\/li\u003E\r\n\t\u003Cli\u003ETopology, Algebra, and Geometry in Machine Learning\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003EDetails about the ICML research from Georgia Tech are at the links below. To learn more about the Machine Learning Center at Georgia Tech visit\u0026nbsp;\u003Ca href=\u0022https:\/\/ml.gatech.edu\/\u0022\u003Ehttps:\/\/ml.gatech.edu\u003C\/a\u003E.\u003C\/p\u003E\r\n\r\n\u003Ch2\u003E\u003Cstrong\u003EGeorgia Tech at ICML 2022\u003C\/strong\u003E\u003C\/h2\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EORALS\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EStable Conformal Prediction Sets\u003Cbr \/\u003E\r\n\u003Ca href=\u0022https:\/\/icml.cc\/Conferences\/2022\/Schedule?showEvent=16842\u0022\u003EMISC: General Machine Learning Techniques\u003C\/a\u003E\u003Cbr \/\u003E\r\nEugene Ndiaye\u003C\/p\u003E\r\n\r\n\u003Cp\u003ETheory\/Social Aspects\u003Cbr \/\u003E\r\n\u003Ca href=\u0022https:\/\/icml.cc\/Conferences\/2022\/Schedule?showEvent=16656\u0022\u003EFederated Reinforcement Learning: Linear Speedup Under Markovian Sampling\u003C\/a\u003E\u003Cbr \/\u003E\r\nSajad Khodadadian, Pranay Sharma, Gauri Joshi, Siva Theja Maguluri\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EPAPERS\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDeep Learning\/Optimization\u003Cbr \/\u003E\r\n\u003Ca href=\u0022https:\/\/icml.cc\/Conferences\/2022\/Schedule?showEvent=16096\u0022\u003ENISPA: Neuro-Inspired Stability-Plasticity Adaptation for Continual Learning in Sparse Networks\u003C\/a\u003E\u003Cbr \/\u003E\r\nMustafa Burak Gurbuz, Constantine Dovrolis\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ESPOTLIGHTS\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EApplications\u003Cbr \/\u003E\r\n\u003Ca href=\u0022https:\/\/icml.cc\/Conferences\/2022\/Schedule?showEvent=17018\u0022\u003EPLATON: Pruning Large Transformer Models with Upper Confidence Bound of Weight Importance\u003C\/a\u003E\u003Cbr \/\u003E\r\nQingru Zhang, Simiao Zuo, Chen Liang, Alexander Bukharin, Pengcheng He, Weizhu Chen, Tuo Zhao\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDeep Learning: SSL\/GNN\u003Cbr \/\u003E\r\n\u003Ca href=\u0022https:\/\/icml.cc\/Conferences\/2022\/Schedule?showEvent=16824\u0022\u003EVariational Wasserstein gradient flow\u003C\/a\u003E\u003Cbr \/\u003E\r\nJiaojiao Fan, Qinsheng Zhang, Amirhossein Taghvaei, Yongxin Chen\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDL: Theory\u003Cbr \/\u003E\r\n\u003Ca href=\u0022https:\/\/icml.cc\/Conferences\/2022\/Schedule?showEvent=18120\u0022\u003EBenefits of Overparameterized Convolutional Residual Networks: Function Approximation under Smoothness Constraint\u003C\/a\u003E\u003Cbr \/\u003E\r\nHao Liu, Minshuo Chen, Siawpeng Er, Wenjing Liao, Tong Zhang, Tuo Zhao\u003C\/p\u003E\r\n\r\n\u003Cp\u003EOptimization: Convex\u003Cbr \/\u003E\r\n\u003Ca href=\u0022https:\/\/icml.cc\/Conferences\/2022\/Schedule?showEvent=18332\u0022\u003EActive Sampling for Min-Max Fairness\u003C\/a\u003E\u003Cbr \/\u003E\r\nJacob Abernethy, Pranjal Awasthi, Matth\u0026auml;us Kleindessner, Jamie Morgenstern, Chris Russell, Jie Zhang\u003C\/p\u003E\r\n\r\n\u003Cp\u003EPM: Monte Carlo and Sampling Methods\u003Cbr \/\u003E\r\n\u003Ca href=\u0022https:\/\/icml.cc\/Conferences\/2022\/Schedule?showEvent=16596\u0022\u003EHessian-Free High-Resolution Nesterov Acceleration For Sampling\u003C\/a\u003E\u003Cbr \/\u003E\r\nRuilin Li, Hongyuan Zha, Molei Tao\u003C\/p\u003E\r\n\r\n\u003Cp\u003EPM: Variational Inference\/Bayesian Models and Methods\u003Cbr \/\u003E\r\n\u003Ca href=\u0022https:\/\/icml.cc\/Conferences\/2022\/Schedule?showEvent=16430\u0022\u003EVariational Sparse Coding with Learned Thresholding\u003C\/a\u003E\u003Cbr \/\u003E\r\nKion Fallah, Christopher J. Rozell\u003C\/p\u003E\r\n\r\n\u003Cp\u003ET: Online Learning and Bandits\u003Cbr \/\u003E\r\n\u003Ca href=\u0022https:\/\/icml.cc\/Conferences\/2022\/Schedule?showEvent=16806\u0022\u003EUniversal and data-adaptive algorithms for model selection in linear contextual bandits\u003C\/a\u003E\u003Cbr \/\u003E\r\nVidya Muthukumar, Akshay Krishnamurthy\u003C\/p\u003E\r\n\r\n\u003Cp\u003ETheory\u003Cbr \/\u003E\r\n\u003Ca href=\u0022https:\/\/icml.cc\/Conferences\/2022\/Schedule?showEvent=16966\u0022\u003EActiveHedge: Hedge meets Active Learning\u003C\/a\u003E\u003Cbr \/\u003E\r\nBhuvesh Kumar, Jacob Abernethy, Venkatesh Saligrama\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EWORKSHOPS\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EAdaptive Experimental Design and Active Learning in the Real World (ReALML) workshop\u003Cbr \/\u003E\r\n\u003Ca href=\u0022https:\/\/arxiv.org\/pdf\/2206.10120.pdf\u0022\u003EDECAL: DEployable Clinical Active Learning\u003C\/a\u003E\u003Cbr \/\u003E\r\nY. Logan, M. Prabhushankar and G. AlRegib\u003C\/p\u003E\r\n\r\n\u003Cp\u003EBeyond Bayes: Paths Towards Universal Reasoning Systems\u003Cbr \/\u003E\r\n\u003Ca href=\u0022https:\/\/arxiv.org\/abs\/2202.11838\u0022\u003EExplanatory Paradigms in Neural Networks\u003C\/a\u003E\u003Cbr \/\u003E\r\nGhassan AlRegib, Mohit Prabhushankar\u003C\/p\u003E\r\n\r\n\u003Cp\u003ENew Frontiers in Adversarial Machine Learning\u003Cbr \/\u003E\r\n\u003Ca href=\u0022https:\/\/arxiv.org\/abs\/2206.08255\u0022\u003EGradient-Based Adversarial and Out-of-Distribution Detection\u003C\/a\u003E\u003Cbr \/\u003E\r\nJinsol Lee, Mohit Prabhushankar, Ghassan AlRegib\u003C\/p\u003E\r\n\r\n\u003Cp\u003ETopology, Algebra, and Geometry in Machine Learning (Workshop)\u003Cbr \/\u003E\r\n\u003Ca href=\u0022https:\/\/arxiv.org\/abs\/2206.06563\u0022\u003EZeroth-Order Topological Insights into Iterative Magnitude Pruning\u003C\/a\u003E\u003Cbr \/\u003E\r\nAishwarya Balwani, Jakob Krzyston\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EGeorgia Tech researchers have new published research at the International Conference on Machine Learning (ICML), a leading academic conference in machine learning, the field of computer science that gives computer systems the ability to learn from data.\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Georgia Tech researchers have new published research at the International Conference on Machine Learning (ICML), a leading academic conference in machine learning, the field of computer science that gives computer systems the ability to learn from data."}],"uid":"27592","created_gmt":"2022-07-20 20:44:22","changed_gmt":"2022-07-20 20:57:01","author":"Joshua Preston","boilerplate_text":"","field_publication":"","field_article_url":"","dateline":{"date":"2022-07-18T00:00:00-04:00","iso_date":"2022-07-18T00:00:00-04:00","tz":"America\/New_York"},"extras":[],"hg_media":{"659547":{"id":"659547","type":"image","title":"ICML 2022","body":null,"created":"1658349915","gmt_created":"2022-07-20 20:45:15","changed":"1658349915","gmt_changed":"2022-07-20 20:45:15","alt":"","file":{"fid":"249974","name":"ICML22 people collage_final3.png","image_path":"\/sites\/default\/files\/images\/ICML22%20people%20collage_final3.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/images\/ICML22%20people%20collage_final3.png","mime":"image\/png","size":3617727,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/images\/ICML22%20people%20collage_final3.png?itok=kwUwjR5u"}}},"media_ids":["659547"],"groups":[{"id":"576481","name":"ML@GT"}],"categories":[],"keywords":[],"core_research_areas":[{"id":"39431","name":"Data Engineering and Science"},{"id":"39521","name":"Robotics"}],"news_room_topics":[],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003EJoshua Preston\u003Cbr \/\u003E\r\n\u003Ca href=\u0022mailto:jpreston7@gatech.edu?subject=ICML%202022\u0022\u003EResearch Communications Manager\u003C\/a\u003E\u003Cbr \/\u003E\r\nCollege of Computing\u003C\/p\u003E\r\n","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}}}