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Poster spotlights ID: 10, 11, 16, 17, 20, 24, 31
Author Information
Hongseok Namkoong (Stanford University)
Marie Charpignon (MIT)
Marie grew up in Burgundy, France and studied engineering in Paris. She moved to the US to study Computational and Mathematical Engineering at Stanford University for her master’s (’16). She is passionate about statistics for education and healthcare. After graduation, she joined Microsoft as a data scientist focusing on education technology. There, she built models to better understand online collaboration, studied the impact of technology usage at school and organized workshops for high school girls. She is currently a second-year graduate student in the MIT PhD program in Social & Engineering Systems. Her work on causal inference for drug repurposing using Electronic Health Records combines mathematical modelling, data analysis and policy.
Maja Rudolph (BCAI)
Amanda Coston (Carnegie Mellon University)
Yuta Saito (Tokyo Institute of Technology)
I am a fourth year undergraduate at Tokyo Institute of Technology. My research lies at the intersection of machine learning and causal inference called counterfactual machine learning. I am interested in the counterfactual nature of logged bandit feedback obtained from interactive systems, and ways of using biased real-world datasets to assist better decision making. Most recently, I have been focusing on the intersection of counterfactual machine learning and unsupervised domain adaptation.
Paramveer Dhillon (University of Michigan)
Assistant Professor at the University of Michigan
Alexander Markham (University of Vienna)
I'm a third year PhD student in the Neuroinformatics research group at the University of Vienna. My research focuses on causal discovery methods and especially their application to the brain sciences.
More from the Same Authors
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2021 : Open Bandit Dataset and Pipeline: Towards Realistic and Reproducible Off-Policy Evaluation »
Yuta Saito · Shunsuke Aihara · Megumi Matsutani · Yusuke Narita -
2021 : Robust fine-tuning of zero-shot models »
Mitchell Wortsman · Gabriel Ilharco · Jong Wook Kim · Mike Li · Hanna Hajishirzi · Ali Farhadi · Hongseok Namkoong · Ludwig Schmidt -
2022 : Counterfactual Risk Assessments under Unmeasured Confounding »
Amanda Coston · Edward Kennedy · Ashesh Rambachan -
2022 : Counterfactual Decision Support Under Treatment-Conditional Outcome Measurement Error »
Luke Guerdan · Amanda Coston · Kenneth Holstein · Steven Wu -
2022 : Causality Roundtable »
Dhanya Sridhar · Amanda Coston -
2021 Workshop: Machine Learning in Public Health »
Rumi Chunara · Daniel Lizotte · Laura Rosella · Esra Suel · Marie Charpignon -
2021 Poster: Evaluating model performance under worst-case subpopulations »
Mike Li · Hongseok Namkoong · Shangzhou Xia -
2020 : Discussion Panel with Amanda Coston »
Amanda Coston · Elaine Nsoesie · Catherine Nakalembe · Santiago Saavedra · Xiaoxiang Zhu · Ernest Mwebaze -
2020 Poster: Counterfactual Predictions under Runtime Confounding »
Amanda Coston · Edward Kennedy · Alexandra Chouldechova -
2019 : Coffee break, posters, and 1-on-1 discussions »
Julius von Kügelgen · David Rohde · Candice Schumann · Grace Charles · Victor Veitch · Vira Semenova · Mert Demirer · Vasilis Syrgkanis · Suraj Nair · Aahlad Puli · Masatoshi Uehara · Aditya Gopalan · Yi Ding · Ignavier Ng · Khashayar Khosravi · Eli Sherman · Shuxi Zeng · Aleksander Wieczorek · Hao Liu · Kyra Gan · Jason Hartford · Miruna Oprescu · Alexander D'Amour · Jörn Boehnke · Yuta Saito · Théophile Griveau-Billion · Chirag Modi · Shyngys Karimov · Jeroen Berrevoets · Logan Graham · Imke Mayer · Dhanya Sridhar · Issa Dahabreh · Alan Mishler · Duncan Wadsworth · Khizar Qureshi · Rahul Ladhania · Gota Morishita · Paul Welle -
2019 : Coffee break, posters, and 1-on-1 discussions »
Yangyi Lu · Daniel Chen · Hongseok Namkoong · Marie Charpignon · Maja Rudolph · Amanda Coston · Julius von Kügelgen · Niranjani Prasad · Paramveer Dhillon · Yunzong Xu · Yixin Wang · Alexander Markham · David Rohde · Rahul Singh · Zichen Zhang · Negar Hassanpour · Ankit Sharma · Ciarán Lee · Jean Pouget-Abadie · Jesse Krijthe · Divyat Mahajan · Nan Rosemary Ke · Peter Wirnsberger · Vira Semenova · Dmytro Mykhaylov · Dennis Shen · Kenta Takatsu · Liyang Sun · Jeremy Yang · Alexander Franks · Pak Kan Wong · Tauhid Zaman · Shira Mitchell · min kyoung kang · Qi Yang -
2019 : Poster Session I »
Shuangjia Zheng · Arnav Kapur · Umar Asif · Eyal Rozenberg · Cyprien Gilet · Oleksii Sidorov · Yogesh Kumar · Tom Van Steenkiste · William Boag · David Ouyang · Paul Jaeger · Sheng Liu · Aparna Balagopalan · Deepta Rajan · Marta Skreta · Nikhil Pattisapu · Jann Goschenhofer · Viraj Prabhu · Di Jin · Laura-Jayne Gardiner · Irene Li · sriram kumar · Qiyuan Hu · Mehul Motani · Justin Lovelace · Usman Roshan · Lucy Lu Wang · Ilya Valmianski · Hyeonwoo Lee · Sunil Mallya · Elias Chaibub Neto · Jonas Kemp · Marie Charpignon · Amber Nigam · Wei-Hung Weng · Sabri Boughorbel · Alexis Bellot · Lovedeep Gondara · Haoran Zhang · Taha Bahadori · John Zech · Rulin Shao · Edward Choi · Laleh Seyyed-Kalantari · Emily Aiken · Ioana Bica · Yiqiu Shen · Kieran Chin-Cheong · Subhrajit Roy · Ioana Baldini · So Yeon Min · Dirk Deschrijver · Pekka Marttinen · Damian Pascual Ortiz · Supriya Nagesh · Niklas Rindtorff · Andriy Mulyar · Katharina Hoebel · Martha Shaka · Pierre Machart · Leon Gatys · Nathan Ng · Matthias Hüser · Devin Taylor · Dennis Barbour · Natalia Martinez · Clara McCreery · Benjamin Eyre · Vivek Natarajan · Ren Yi · Ruibin Ma · Chirag Nagpal · Nan Du · Chufan Gao · Anup Tuladhar · Sam Shleifer · Jason Ren · Pouria Mashouri · Ming Yang Lu · Farideh Bagherzadeh-Khiabani · Olivia Choudhury · Maithra Raghu · Scott Fleming · Mika Jain · GUO YANG · Alena Harley · Stephen Pfohl · Elisabeth Rumetshofer · Alex Fedorov · Saloni Dash · Jacob Pfau · Sabina Tomkins · Colin Targonski · Michael Brudno · Xinyu Li · Yiyang Yu · Nisarg Patel -
2019 Workshop: Machine Learning for the Developing World (ML4D): Challenges and Risks »
Maria De-Arteaga · Amanda Coston · Tejumade Afonja -
2017 Poster: Variance-based Regularization with Convex Objectives »
Hongseok Namkoong · John Duchi -
2017 Oral: Variance-based Regularization with Convex Objectives »
Hongseok Namkoong · John Duchi