Timezone: »
The goal of the NIPS 2017 Machine Learning for Health Workshop (ML4H) is to foster collaborations that meaningfully impact medicine by bringing together clinicians, health data experts, and machine learning researchers. We aim to build on the success of the last two NIPS ML4H workshops which were widely attended and helped form the foundations of a new research community.
This year’s program emphasizes identifying previously unidentified problems in healthcare that the machine learning community hasn't addressed, or seeing old challenges through a new lens. While healthcare and medicine are often touted as prime examples for disruption by AI and machine learning, there has been vanishingly little evidence of this disruption to date. To interested parties who are outside of the medical establishment (e.g. machine learning researchers), the healthcare system can appear byzantine and impenetrable, which results in a high barrier to entry. In this workshop, we hope to reduce this activation energy by bringing together leaders at the forefront of both machine learning and healthcare for a dialog on areas of medicine that have immediate opportunities for machine learning. Attendees at this workshop will quickly gain an understanding of the key problems that are unique to healthcare and how machine learning can be applied to addressed these challenges.
The workshop will feature invited talks from leading voices in both medicine and machine learning. A key part of our workshop is the clinician pitch; a short presentation of open clinical problems where data-driven solutions can make an immediate difference. This year’s program will also include spotlight presentations and two poster sessions highlighting novel research contributions at the intersection of machine learning and healthcare. The workshop will conclude with an interactive a panel discussion where all speakers respond to questions provided by the audience.
Fri 8:00 a.m. - 8:20 a.m.
|
Welcome and opening remarks
(
Talk
)
|
🔗 |
Fri 8:20 a.m. - 8:55 a.m.
|
Keynote: Zak Kohane, Harvard DBMI
(
Talk
)
|
Isaac S Kohane 🔗 |
Fri 8:55 a.m. - 9:20 a.m.
|
Jennifer Chayes, Microsoft Research New England
(
Talk
)
|
Jennifer Chayes 🔗 |
Fri 9:20 a.m. - 9:55 a.m.
|
Keynote: Susan Murphy, U. Michigan
(
Talk
)
|
Susan Murphy 🔗 |
Fri 9:55 a.m. - 10:20 a.m.
|
Contributed spotlights
(
Spotlight
)
|
🔗 |
Fri 10:20 a.m. - 10:50 a.m.
|
Coffee break and Poster Session I
(
Poster Session
)
|
Nishith Khandwala · Steve Gallant · Gregory Way · Aniruddh Raghu · Li Shen · Aydan Gasimova · Alican Bozkurt · William Boag · Daniel Lopez-Martinez · Ulrich Bodenhofer · Samaneh Nasiri GhoshehBolagh · Michelle Guo · Christoph Kurz · Kirubin Pillay · Kimis Perros · George H Chen · Alexandre Yahi · Madhumita Sushil · Sanjay Purushotham · Elena Tutubalina · Tejpal Virdi · Marc-Andre Schulz · Samuel Weisenthal · Bharat Srikishan · Petar Veličković · Kartik Ahuja · Andrew Miller · Erin Craig · Disi Ji · Filip Dabek · Chloé Pou-Prom · Hejia Zhang · Janani Kalyanam · Wei-Hung Weng · Harish Bhat · Hugh Chen · Simon Kohl · Mingwu Gao · Tingting Zhu · Ming-Zher Poh · Iñigo Urteaga · Antoine Honoré · Alessandro De Palma · Maruan Al-Shedivat · Pranav Rajpurkar · Matthew McDermott · Vincent Chen · Yanan Sui · Yun-Geun Lee · Li-Fang Cheng · Chen Fang · Sibt ul Hussain · Cesare Furlanello · Zeev Waks · Hiba Chougrad · Hedvig Kjellstrom · Finale Doshi-Velez · Wolfgang Fruehwirt · Yanqing Zhang · Lily Hu · Junfang Chen · Sunho Park · Gatis Mikelsons · Jumana Dakka · Stephanie Hyland · yann chevaleyre · Hyunwoo Lee · Xavier Giro-i-Nieto · David Kale · Michael Hughes · Gabriel Erion · Rishab Mehra · William Zame · Stojan Trajanovski · Prithwish Chakraborty · Kelly Peterson · Muktabh Mayank Srivastava · Amy Jin · Heliodoro Tejeda Lemus · Priyadip Ray · Tamas Madl · Joseph Futoma · Enhao Gong · Syed Rameel Ahmad · Eric Lei · Ferdinand Legros
|
Fri 10:50 a.m. - 11:50 a.m.
|
Invited clinical panel
(
Panel Discussion
)
Susann Beier, U. Auckland James Priest, Stanford Irina Strigo, UCSF Enrique Velazquez, Rady Children’s Hospital |
Enrique Velazquez · James Priest · irina strigo 🔗 |
Fri 11:50 a.m. - 12:25 p.m.
|
Keynote II: Fei-Fei Li, Stanford
(
Talk
)
|
Li Fei-Fei 🔗 |
Fri 1:30 p.m. - 2:30 p.m.
|
Interactive panel
(
Panel Discussion
)
Interactive panel moderated by Zak Kohane: - Atul Butte - Jennifer Chayes - Fei-Fei Li - Jill Mesirov - Susan Murphy - Mustafa Sulyman |
🔗 |
Fri 2:30 p.m. - 2:55 p.m.
|
Jill Mesirov, UCSD
(
Talk
)
|
Jill Mesirov 🔗 |
Fri 2:55 p.m. - 3:20 p.m.
|
Greg Corrado, Google
(
Talk
)
|
Greg Corrado 🔗 |
Fri 3:20 p.m. - 3:50 p.m.
|
Coffee break and Poster Session II
(
Poster Session
)
|
Mohamed Kane · Albert Haque · Vagelis Papalexakis · John Guibas · Peter Li · Carlos Arias · Eric Nalisnick · Padhraic Smyth · Frank Rudzicz · Xia Zhu · Theodore Willke · Noemie Elhadad · Hans Raffauf · Harini Suresh · Paroma Varma · Yisong Yue · Ognjen (Oggi) Rudovic · Luca Foschini · Syed Rameel Ahmad · Hasham ul Haq · Valerio Maggio · Giuseppe Jurman · Sonali Parbhoo · Pouya Bashivan · Jyoti Islam · Mirco Musolesi · Chris Wu · Alexander Ratner · Jared Dunnmon · Cristóbal Esteban · Aram Galstyan · Greg Ver Steeg · Hrant Khachatrian · Marc Górriz · Mihaela van der Schaar · Anton Nemchenko · Manasi Patwardhan · Tanay Tandon
|
Fri 3:50 p.m. - 4:10 p.m.
|
Award session + A word from our affiliates
(
Award Session
)
Award session and a word from affiliates - Eunho Yang, KAIST, Korea - Sung-ju Hwang,UNIST, Korea representing AItrics, DeepMind, IBM, Google, MSR |
🔗 |
Fri 4:10 p.m. - 4:35 p.m.
|
Mihaela Van Der Schaar, Oxford
(
Talk
)
|
🔗 |
Fri 4:35 p.m. - 5:00 p.m.
|
Networking Break
|
🔗 |
Fri 5:00 p.m. - 5:25 p.m.
|
Jure Leskovec, Stanford
(
Talk
)
|
Jure Leskovec 🔗 |
Fri 5:25 p.m. - 6:00 p.m.
|
Keynote: Atul Butte
(
Talk
)
|
Atul Butte 🔗 |
Fri 6:00 p.m. - 6:05 p.m.
|
Closing Remarks
(
Talk
)
|
🔗 |
Author Information
Jason Fries (Stanford University)
Alex Wiltschko (Google)
Andrew Beam (Harvard Medical School)
Isaac S Kohane (Harvard Medical School)
Jasper Snoek (University of Toronto)
Peter Schulam (Johns Hopkins University)
Peter Schulam is a PhD student in computer science at Johns Hopkins University. His research interests include machine learning and its applications to healthcare. Peter has made methodological contributions to advancing the use of electronic health data for individualizing care in chronic diseases. His current work explores applications in autoimmune diseases. He has won the National Science Foundation (NSF) Graduate Research Fellowship and the Whiting School of Engineering Centennial Fellowship. He is working with Prof. Suchi Saria for his PhD. Prior to that, he received his master’s from Carnegie Mellon University and his bachelor’s from Princeton University.
Madalina Fiterau (UMass Amherst)
Madalina Fiterau is an Assistant Professor at the College of College of Information and Computer Sciences at UMass Amherst, with a focus on AI/ML. Previously, she was a Postdoctoral Fellow in the Computer Science Department at Stanford University, working with Professors Chris Ré and Scott Delp in the Mobilize Center. Madalina has obtained a PhD in Machine Learning from Carnegie Mellon University in September 2015, advised by Professor Artur Dubrawski. The focus of her PhD thesis, entitled “Discovering Compact and Informative Structures through Data Partitioning”, is learning interpretable ensembles, with applicability ranging from image classification to a clinical alert prediction system. Madalina is currently expanding her research on interpretable models, in part by applying deep learning to obtain salient representations from biomedical “deep” data, including time series, text and images. Madalina is the recipient of the GE Foundation Scholar Leader Award for Central and Eastern Europe. She is the recipient of the Marr Prize for Best Paper at ICCV 2015 and of Star Research Award at the Annual Congress of the Society of Critical Care Medicine 2016. She has organized two editions of the Machine Learning for Clinical Data Analysis Workshop at NIPS, in 2013 and 2014.
David Kale (University of Southern California)
Rajesh Ranganath (Princeton University)
Rajesh Ranganath is a PhD candidate in computer science at Princeton University. His research interests include approximate inference, model checking, Bayesian nonparametrics, and machine learning for healthcare. Rajesh has made several advances in variational methods, especially in popularising black-box variational inference methods that automate the process of inference by making variational inference easier to use while providing more scalable, and accurate posterior approximations. Rajesh works in SLAP group with David Blei. Before starting his PhD, Rajesh worked as a software engineer for AMA Capital Management. He obtained his BS and MS from Stanford University with Andrew Ng and Dan Jurafsky. Rajesh has won several awards and fellowships including the NDSEG graduate fellowship and the Porter Ogden Jacobus Fellowship, given to the top four doctoral students at Princeton University.
Bruno Jedynak (Portland state university)
Michael Hughes (Tufts University)
Tristan Naumann (Microsoft Research)
Natalia Antropova (The University of Chicago)
Adrian Dalca (MIT)
SHUBHI ASTHANA (IBM Almaden Research Center)
Shubhi Asthana is a Research Senior Software Engineer at IBM Almaden Research Center, USA. She works in the area of Cloud Services, IoT and Predictive Analytics.
Prateek Tandon (UCSD)
Prateek Tandon is a postdoctoral candidate in the Psychiatry department at the University of California, San Diego. He obtained his PhD in Robotics from Carnegie Mellon University with a dissertation on Bayesian sensor fusion. His current research interests involve exploring the application of machine learning and deep learning to complex, nonlinear, and multimodal data problems in genetics and bioinformatics. His postdoctoral research is on utilizing machine learning to predict the pathogenicity of genetic structural variation for neurodevelopmental disorders such as Autism Spectrum Disorder (ASD) and schizophrenia.
Jaz Kandola (Imperial College London)
Uri Shalit (Technion)
Marzyeh Ghassemi (University of Toronto)
Tim Althoff (Stanford Univesity)
Alexander Ratner (Stanford)
Jumana Dakka (Rutgers University)
Interested in fMRI deep learning
More from the Same Authors
-
2020 : Learning MRI contrast agnostic registration »
Malte Hoffmann · Adrian Dalca -
2021 : Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning »
Guy Tennenholtz · Assaf Hallak · Gal Dalal · Shie Mannor · Gal Chechik · Uri Shalit -
2021 : The Tufts fNIRS Mental Workload Dataset & Benchmark for Brain-Computer Interfaces that Generalize »
zhe huang · Liang Wang · Giles Blaney · Christopher Slaughter · Devon McKeon · Ziyu Zhou · Robert Jacob · Michael Hughes -
2021 : Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning »
Zachary Nado · Neil Band · Mark Collier · Josip Djolonga · Mike Dusenberry · Sebastian Farquhar · Qixuan Feng · Angelos Filos · Marton Havasi · Rodolphe Jenatton · Ghassen Jerfel · Jeremiah Liu · Zelda Mariet · Jeremy Nixon · Shreyas Padhy · Jie Ren · Tim G. J. Rudner · Yeming Wen · Florian Wenzel · Kevin Murphy · D. Sculley · Balaji Lakshminarayanan · Jasper Snoek · Yarin Gal · Dustin Tran -
2022 : Predicting Spatiotemporal Counts of Opioid-related Fatal Overdoses via Zero-Inflated Gaussian Processes »
Kyle Heuton · Shikhar Shrestha · Thomas Stopka · Jennifer Pustz · · Michael Hughes -
2022 : Multimodal Checklists for Fair Clinical Decision Support »
Qixuan Jin · Marzyeh Ghassemi -
2022 : DaME: Data Mapping Engine for Financial Services »
SHUBHI ASTHANA · Ruchi Mahindru -
2022 : Fair Active learning by exploiting causal data structure »
Sindhu C M Gowda · Haoran Zhang · Marzyeh Ghassemi -
2022 : Probabilistic Interactive Segmentation for Medical Images »
Hallee Wong · John Guttag · Adrian Dalca -
2022 : UniverSeg: Universal Medical Image Segmentation »
Victor Butoi · Jose Javier Gonzalez Ortiz · Tianyu Ma · John Guttag · Mert Sabuncu · Adrian Dalca -
2022 : Probabilistic Interactive Segmentation for Medical Images »
Hallee Wong · John Guttag · Adrian Dalca -
2022 : Semi-supervised Learning from Uncurated Echocardiogram Images with Fix-A-Step »
Zhe Huang · Mary-Joy Sidhom · Benjamin Wessler · Michael Hughes -
2022 : Contrast Invariant Feature Representations for Medical Image Analysis »
Yue Zhi, Russ Chua · Adrian Dalca -
2022 : Region-of-Interest Adaptive Acquisition for Accelerated MRI »
Zihui Wu · Tianwei Yin · Adrian Dalca · Katherine Bouman -
2022 : Mapping of Financial Services datasets using Human-in-the-Loop »
SHUBHI ASTHANA · Ruchi Mahindru -
2023 Poster: Scale-Space Hypernetworks for Efficient Biomedical Image Analysis »
Jose Javier Gonzalez Ortiz · John Guttag · Adrian Dalca -
2023 Poster: Event Stream GPT: A Data Pre-processing and Modeling Library for Generative, Pre-trained Transformers over Continuous-time Sequences of Complex Events »
Matthew McDermott · Bret Nestor · Peniel Argaw · Isaac S Kohane -
2022 : Prediction-Constrained Markov Models for Medical Time Series with Missing Data and Few Labels »
Preetish Rath · Gabe Hope · Kyle Heuton · Erik Sudderth · Michael Hughes -
2022 : Prediction-Constrained Markov Models for Medical Time Series with Missing Data and Few Labels »
Preetish Rath · Gabe Hope · Kyle Heuton · Erik Sudderth · Michael Hughes -
2022 Poster: Deep Learning Methods for Proximal Inference via Maximum Moment Restriction »
Benjamin Kompa · David Bellamy · Tom Kolokotrones · james m robins · Andrew Beam -
2021 : Data Opportunities: unsolved medical problems and where new data can help »
Bin Yu · Regina Barzilay · Marzyeh Ghassemi · Emma Pierson -
2021 Workshop: Machine learning from ground truth: New medical imaging datasets for unsolved medical problems. »
Katy Haynes · Ziad Obermeyer · Emma Pierson · Marzyeh Ghassemi · Matthew Lungren · Sendhil Mullainathan · Matthew McDermott -
2021 Workshop: Your Model is Wrong: Robustness and misspecification in probabilistic modeling »
Diana Cai · Sameer Deshpande · Michael Hughes · Tamara Broderick · Trevor Campbell · Nick Foti · Barbara Engelhardt · Sinead Williamson -
2021 Poster: Learning Riemannian metric for disease progression modeling »
Samuel Gruffaz · Pierre-Emmanuel Poulet · Etienne Maheux · Bruno Jedynak · Stanley DURRLEMAN -
2021 Poster: Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data »
Andrew Jesson · Panagiotis Tigas · Joost van Amersfoort · Andreas Kirsch · Uri Shalit · Yarin Gal -
2021 Poster: Dynamical Wasserstein Barycenters for Time-series Modeling »
Kevin Cheng · Shuchin Aeron · Michael Hughes · Eric L Miller -
2021 Poster: On Calibration and Out-of-Domain Generalization »
Yoav Wald · Amir Feder · Daniel Greenfeld · Uri Shalit -
2020 : Invited Talk: Mike Hughes - The Case for Prediction Constrained Training »
Michael Hughes -
2020 Workshop: Machine Learning for Health (ML4H): Advancing Healthcare for All »
Stephanie Hyland · Allen Schmaltz · Charles Onu · Ehi Nosakhare · Emily Alsentzer · Irene Y Chen · Matthew McDermott · Subhrajit Roy · Benjamin Akera · Dani Kiyasseh · Fabian Falck · Griffin Adams · Ioana Bica · Oliver J Bear Don't Walk IV · Suproteem Sarkar · Stephen Pfohl · Andrew Beam · Brett Beaulieu-Jones · Danielle Belgrave · Tristan Naumann -
2020 Symposium: COVID-19 Symposium Day 2 »
Andrew Beam · Tristan Naumann · Katherine Heller · Elaine Nsoesie -
2020 Symposium: COVID-19 Symposium Day 1 »
Andrew Beam · Tristan Naumann · Katherine Heller · Elaine Nsoesie -
2020 Affinity Workshop: Muslims in ML »
Marzyeh Ghassemi · Mohammad Norouzi · Shakir Mohamed · Aya Salama · Tasmie Sarker -
2019 Workshop: Machine Learning for Health (ML4H): What makes machine learning in medicine different? »
Andrew Beam · Tristan Naumann · Brett Beaulieu-Jones · Irene Y Chen · Madalina Fiterau · Samuel Finlayson · Emily Alsentzer · Adrian Dalca · Matthew McDermott -
2019 Workshop: Learning Meaningful Representations of Life »
Elizabeth Wood · Yakir Reshef · Jonathan Bloom · Jasper Snoek · Barbara Engelhardt · Scott Linderman · Suchi Saria · Alexander Wiltschko · Casey Greene · Chang Liu · Kresten Lindorff-Larsen · Debora Marks -
2019 Poster: Learning Conditional Deformable Templates with Convolutional Networks »
Adrian Dalca · Marianne Rakic · John Guttag · Mert Sabuncu -
2018 : Poster session »
David Zeng · Marzieh S. Tahaei · Shuai Chen · Felix Meister · Meet Shah · Anant Gupta · Ajil Jalal · Eirini Arvaniti · David Zimmerer · Konstantinos Kamnitsas · Pedro Ballester · Nathaniel Braman · Udaya Kumar · Sil C. van de Leemput · Junaid Qadir · Hoel Kervadec · Mohamed Akrout · Adrian Tousignant · Matthew Ng · Raghav Mehta · Miguel Monteiro · Sumana Basu · Jonas Adler · Adrian Dalca · Jizong Peng · Sungyeob Han · Xiaoxiao Li · Karthik Gopinath · Joseph Cheng · Bogdan Georgescu · Kha Gia Quach · Karthik Sarma · David Van Veen -
2018 : Oral session II »
Sil C. van de Leemput · Adrian Dalca · Karthik Gopinath -
2018 : Poster Session I »
Aniruddh Raghu · Daniel Jarrett · Kathleen Lewis · Elias Chaibub Neto · Nicholas Mastronarde · Shazia Akbar · Chun-Hung Chao · Henghui Zhu · Seth Stafford · Luna Zhang · Jen-Tang Lu · Changhee Lee · Adityanarayanan Radhakrishnan · Fabian Falck · Liyue Shen · Daniel Neil · Yusuf Roohani · Aparna Balagopalan · Brett Marinelli · Hagai Rossman · Sven Giesselbach · Jose Javier Gonzalez Ortiz · Edward De Brouwer · Byung-Hoon Kim · Rafid Mahmood · Tzu Ming Hsu · Antonio Ribeiro · Rumi Chunara · Agni Orfanoudaki · Kristen Severson · Mingjie Mai · Sonali Parbhoo · Albert Haque · Viraj Prabhu · Di Jin · Alena Harley · Geoffroy Dubourg-Felonneau · Xiaodan Hu · Maithra Raghu · Jonathan Warrell · Nelson Johansen · Wenyuan Li · Marko Järvenpää · Satya Narayan Shukla · Sarah Tan · Vincent Fortuin · Beau Norgeot · Yi-Te Hsu · Joel H Saltz · Veronica Tozzo · Andrew Miller · Guillaume Ausset · Azin Asgarian · Francesco Paolo Casale · Antoine Neuraz · Bhanu Pratap Singh Rawat · Turgay Ayer · Xinyu Li · Mehul Motani · Nathaniel Braman · Laetitia M Shao · Adrian Dalca · Hyunkwang Lee · Emma Pierson · Sandesh Ghimire · Yuji Kawai · Owen Lahav · Anna Goldenberg · Denny Wu · Pavitra Krishnaswamy · Colin Pawlowski · Arijit Ukil · Yuhui Zhang -
2018 Workshop: Machine Learning for Health (ML4H): Moving beyond supervised learning in healthcare »
Andrew Beam · Tristan Naumann · Marzyeh Ghassemi · Matthew McDermott · Madalina Fiterau · Irene Y Chen · Brett Beaulieu-Jones · Michael Hughes · Farah Shamout · Corey Chivers · Jaz Kandola · Alexandre Yahi · Samuel Finlayson · Bruno Jedynak · Peter Schulam · Natalia Antropova · Jason Fries · Adrian Dalca · Irene Chen -
2018 Workshop: All of Bayesian Nonparametrics (Especially the Useful Bits) »
Diana Cai · Trevor Campbell · Michael Hughes · Tamara Broderick · Nick Foti · Sinead Williamson -
2018 Poster: Gaussian Process Prior Variational Autoencoders »
Francesco Paolo Casale · Adrian Dalca · Luca Saglietti · Jennifer Listgarten · Nicolo Fusi -
2017 : Poster session - Afternoon »
Yongchan Kwon · Young-geun Kim · Ender Konukoglu · Peter Li · John Guibas · Tejpal Virdi · Kuldeep Kumar · Morteza Mardani · Jelmer Wolterink · Enhao Gong · Natalia Antropova · Johannes Stelzer · Rene Bidart · Wei-Hung Weng · Martin Rajchl · Marc Górriz · Vineeta Singh · Christopher Sandino · Hiba Chougrad · Bob Hu · Isaac Godfried · Ke Xiao · Heliodoro Tejeda Lemus · Jordan Harrod · ILSANG WOO · Vincent Chen · Joseph Cheng · Vikash Gupta · Chuck-Hou Yee · Ben Glocker · Hervé Lombaert · Maximilian Ilse · Aneta Lisowska · Andrew Doyle · Milad Makkie -
2017 : Poster session - Morning »
Yongchan Kwon · Young-geun Kim · Ender Konukoglu · Peter Li · John Guibas · Tejpal Virdi · Kuldeep Kumar · Morteza Mardani · Jelmer Wolterink · Enhao Gong · Natalia Antropova · Johannes Stelzer · Rene Bidart · Wei-Hung Weng · Martin Rajchl · Marc Górriz · Vineeta Singh · Christopher Sandino · Hiba Chougrad · Bob Hu · Isaac Godfried · Ke Xiao · Heliodoro Tejeda Lemus · Jordan Harrod · ILSANG WOO · Vincent Chen · Joseph Cheng · Vikash Gupta · Chuck-Hou Yee · Ben Glocker · Hervé Lombaert · Maximilian Ilse · Aneta Lisowska · Andrew Doyle · Milad Makkie -
2017 : Introduction and opening remarks »
Alex Wiltschko -
2017 Workshop: The future of gradient-based machine learning software & techniques »
Alex Wiltschko · Bart van Merriënboer · Pascal Lamblin -
2017 Workshop: Learning with Limited Labeled Data: Weak Supervision and Beyond »
Isabelle Augenstein · Stephen Bach · Eugene Belilovsky · Matthew Blaschko · Christoph Lampert · Edouard Oyallon · Emmanouil Antonios Platanios · Alexander Ratner · Christopher Ré -
2017 : Coffee break and Poster Session II »
Mohamed Kane · Albert Haque · Vagelis Papalexakis · John Guibas · Peter Li · Carlos Arias · Eric Nalisnick · Padhraic Smyth · Frank Rudzicz · Xia Zhu · Theodore Willke · Noemie Elhadad · Hans Raffauf · Harini Suresh · Paroma Varma · Yisong Yue · Ognjen (Oggi) Rudovic · Luca Foschini · Syed Rameel Ahmad · Hasham ul Haq · Valerio Maggio · Giuseppe Jurman · Sonali Parbhoo · Pouya Bashivan · Jyoti Islam · Mirco Musolesi · Chris Wu · Alexander Ratner · Jared Dunnmon · Cristóbal Esteban · Aram Galstyan · Greg Ver Steeg · Hrant Khachatrian · Marc Górriz · Mihaela van der Schaar · Anton Nemchenko · Manasi Patwardhan · Tanay Tandon -
2017 : Competition II: Learning to Run »
Łukasz Kidziński · Carmichael Ong · Sharada Mohanty · Jason Fries · Jennifer Hicks · Zhuobin Zheng · Chun Yuan · Sergey Plis -
2017 : Coffee break and Poster Session I »
Nishith Khandwala · Steve Gallant · Gregory Way · Aniruddh Raghu · Li Shen · Aydan Gasimova · Alican Bozkurt · William Boag · Daniel Lopez-Martinez · Ulrich Bodenhofer · Samaneh Nasiri GhoshehBolagh · Michelle Guo · Christoph Kurz · Kirubin Pillay · Kimis Perros · George H Chen · Alexandre Yahi · Madhumita Sushil · Sanjay Purushotham · Elena Tutubalina · Tejpal Virdi · Marc-Andre Schulz · Samuel Weisenthal · Bharat Srikishan · Petar Veličković · Kartik Ahuja · Andrew Miller · Erin Craig · Disi Ji · Filip Dabek · Chloé Pou-Prom · Hejia Zhang · Janani Kalyanam · Wei-Hung Weng · Harish Bhat · Hugh Chen · Simon Kohl · Mingwu Gao · Tingting Zhu · Ming-Zher Poh · Iñigo Urteaga · Antoine Honoré · Alessandro De Palma · Maruan Al-Shedivat · Pranav Rajpurkar · Matthew McDermott · Vincent Chen · Yanan Sui · Yun-Geun Lee · Li-Fang Cheng · Chen Fang · Sibt ul Hussain · Cesare Furlanello · Zeev Waks · Hiba Chougrad · Hedvig Kjellstrom · Finale Doshi-Velez · Wolfgang Fruehwirt · Yanqing Zhang · Lily Hu · Junfang Chen · Sunho Park · Gatis Mikelsons · Jumana Dakka · Stephanie Hyland · yann chevaleyre · Hyunwoo Lee · Xavier Giro-i-Nieto · David Kale · Michael Hughes · Gabriel Erion · Rishab Mehra · William Zame · Stojan Trajanovski · Prithwish Chakraborty · Kelly Peterson · Muktabh Mayank Srivastava · Amy Jin · Heliodoro Tejeda Lemus · Priyadip Ray · Tamas Madl · Joseph Futoma · Enhao Gong · Syed Rameel Ahmad · Eric Lei · Ferdinand Legros -
2017 : Keynote: Zak Kohane, Harvard DBMI »
Isaac S Kohane -
2017 Poster: Reliable Decision Support using Counterfactual Models »
Peter Schulam · Suchi Saria -
2017 Poster: Learning to Compose Domain-Specific Transformations for Data Augmentation »
Alexander Ratner · Henry Ehrenberg · Zeshan Hussain · Jared Dunnmon · Christopher Ré -
2017 Poster: Hierarchical Implicit Models and Likelihood-Free Variational Inference »
Dustin Tran · Rajesh Ranganath · David Blei -
2017 Poster: Causal Effect Inference with Deep Latent-Variable Models »
Christos Louizos · Uri Shalit · Joris Mooij · David Sontag · Richard Zemel · Max Welling -
2017 Oral: Reliable Decision Support using Counterfactual Models »
Peter Schulam · Suchi Saria -
2017 Poster: Variational Inference via $\chi$ Upper Bound Minimization »
Adji Bousso Dieng · Dustin Tran · Rajesh Ranganath · John Paisley · David Blei -
2016 Workshop: Machine Learning for Health »
Uri Shalit · Marzyeh Ghassemi · Jason Fries · Rajesh Ranganath · Theofanis Karaletsos · David Kale · Peter Schulam · Madalina Fiterau -
2016 Workshop: Practical Bayesian Nonparametrics »
Nick Foti · Tamara Broderick · Trevor Campbell · Michael Hughes · Jeffrey Miller · Aaron Schein · Sinead Williamson · Yanxun Xu -
2016 Poster: Disease Trajectory Maps »
Peter Schulam · Raman Arora -
2016 Poster: Operator Variational Inference »
Rajesh Ranganath · Dustin Tran · Jaan Altosaar · David Blei -
2016 Poster: Data Programming: Creating Large Training Sets, Quickly »
Alexander Ratner · Christopher M De Sa · Sen Wu · Daniel Selsam · Christopher Ré -
2016 Tutorial: ML Foundations and Methods for Precision Medicine and Healthcare »
Suchi Saria · Peter Schulam -
2016 Tutorial: Variational Inference: Foundations and Modern Methods »
David Blei · Shakir Mohamed · Rajesh Ranganath -
2015 Workshop: Machine Learning For Healthcare (MLHC) »
Theofanis Karaletsos · Rajesh Ranganath · Suchi Saria · David Sontag -
2015 Poster: A Framework for Individualizing Predictions of Disease Trajectories by Exploiting Multi-Resolution Structure »
Peter Schulam · Suchi Saria -
2015 Poster: The Population Posterior and Bayesian Modeling on Streams »
James McInerney · Rajesh Ranganath · David Blei -
2015 Demonstration: An interactive system for the extraction of meaningful visualizations from high-dimensional data »
Madalina Fiterau · Artur Dubrawski · Donghan Wang -
2015 Poster: Automatic Variational Inference in Stan »
Alp Kucukelbir · Rajesh Ranganath · Andrew Gelman · David Blei -
2015 Spotlight: Automatic Variational Inference in Stan »
Alp Kucukelbir · Rajesh Ranganath · Andrew Gelman · David Blei -
2015 Poster: Scalable Adaptation of State Complexity for Nonparametric Hidden Markov Models »
Michael Hughes · William Stephenson · Erik Sudderth -
2014 Workshop: 3rd NIPS Workshop on Probabilistic Programming »
Daniel Roy · Josh Tenenbaum · Thomas Dietterich · Stuart J Russell · YI WU · Ulrik R Beierholm · Alp Kucukelbir · Zenna Tavares · Yura Perov · Daniel Lee · Brian Ruttenberg · Sameer Singh · Michael Hughes · Marco Gaboardi · Alexey Radul · Vikash Mansinghka · Frank Wood · Sebastian Riedel · Prakash Panangaden -
2014 Workshop: Machine Learning for Clinical Data Analysis, Healthcare and Genomics »
Gunnar Rätsch · Madalina Fiterau · Julia Vogt -
2013 Workshop: Machine Learning for Clinical Data Analysis and Healthcare »
Jenna Wiens · Finale P Doshi-Velez · Can Ye · Madalina Fiterau · Shipeng Yu · Le Lu · Balaji R Krishnapuram -
2013 Poster: Memoized Online Variational Inference for Dirichlet Process Mixture Models »
Michael Hughes · Erik Sudderth -
2012 Poster: Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data »
Michael Hughes · Emily Fox · Erik Sudderth -
2012 Poster: Projection Retrieval for Classification »
Madalina Fiterau · Artur Dubrawski -
2010 Spotlight: Online Learning in The Manifold of Low-Rank Matrices »
Uri Shalit · Daphna Weinshall · Gal Chechik -
2010 Poster: Online Learning in The Manifold of Low-Rank Matrices »
Uri Shalit · Daphna Weinshall · Gal Chechik -
2009 Poster: An Online Algorithm for Large Scale Image Similarity Learning »
Gal Chechik · Uri Shalit · Varun Sharma · Samy Bengio