Women in Machine Learning will be organizing the first “un-workshop” at ICML 2020. This is a new event format that encourages interaction between participants. The un-workshop is based on the concept of an “un-conference”, a form of discussion on a pre-selected topic that is primarily driven by participants. Different from the long-running WiML Workshop, the un-workshop’s main focus is topical breakout sessions, with short invited talks and casual, informal poster presentations.
Sun 11:00 p.m. - 11:40 p.m.
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Social #1: Informal Socializing
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Social
)
link »
Meet the organizers and other participants for informal socializing on Zoom. |
🔗 |
Sun 11:00 p.m. - 4:00 p.m.
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Join Slack Workspace
(
Link
)
link »
Please join our Slack workspace, which will be active for the duration of the un-workshop and ICML. |
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Sun 11:05 p.m. - 4:00 p.m.
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QuantumBlack, a McKinsey company
(
Sponsor booth
)
link »
To reach the booth please join us on Zoom and if you are asked for a password, use 559104. Staffed Times (UTC/GMT): 8.30 to 9.00 Fannie (Recruiter) 10.40 to 11.10 Maren (Principal Data Scientist) 11.00 to 12.00 Fannie (Recruiter) 13.00 to 14.00 Marta (Recruiter) 17:40 to 18:00 Marta (Recruiter) Drop by any of these times to chat with one of our representatives! Special Events (UTC/GMT): 10.25 - 10.40 please join us in the WiML Expo to hear Maren Eckhoff, Principal Data Scientist, present on ‘Using AI for social and global good’. 10.40 - 11.10 please join us in the QuantumBlack booth where Maren will be available for Q&A. More Information: Visit our careers page for latest job postings. If you want to get in touch, please email us at info@quantumblack.com. |
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Sun 11:05 p.m. - 4:00 p.m.
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Netflix
(
Sponsor booth
)
link »
To reach the booth please join our ICML sponsor page. Staffed Times (UTC/GMT): ICML booth in the sponsor hall is open to ICML attendees! Special Events (UTC/GMT): 10:40 - 10:55 Maria Dimakopoulou will be giving a talk on “Slate Bandit Learning and Evaluation”. 17:00 - 19:00 she will be available at our booth to answer questions. More Information is available at our research site - NETFLIX RESEARCH. |
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Sun 11:05 p.m. - 4:00 p.m.
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IBM
(
Sponsor booth
)
link »
To reach the booth please join our ICML sponsor page. Staffed Times (UTC/GMT): The booth will be staffed from 13.00 to 15.00 More Information is available at our research site - IBM Research. |
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Sun 11:05 p.m. - 4:00 p.m.
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Apple
(
Sponsor booth
)
link »
To reach the booth please join our ICML sponsor page. Staffed Times (UTC/GMT): Please visit the Apple booth in ICML's virtual sponsor expo to chat with members of our machine learning teams and recruiting teams. Special Events (UTC/GMT): Join the sponsor expo at the WiML un-workshop to hear Lizi Ottens from our CoreML team talk about machine learning at Apple. Lizi will also be available to chat 1:1 at our ICML booth on Tuesday, July 14th 18:00 - 19:00, and during our “Q&A with Apple” session on Tuesday, July 14th 19:30-20:00. More Information: Visit the AI/ML team page on Apple Jobs. |
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Sun 11:05 p.m. - 4:00 p.m.
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DeepMind
(
Sponsor booth
)
link »
To reach the booth please join our ICML sponsor page. Staffed Times (UTC/GMT): Visit our booth by any of the below times to chat with a DeepMinder: 07:30 - 08:00 10:00 - 14:00 16:45 - 17:00 Special Events (UTC/GMT): 15:40 – 15:55 Meire Fortunato will be introducing the work of DeepMind during our Sponsor Expo. She will be available at the booth to answer questions right after her talk ends. |
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Sun 11:05 p.m. - 4:00 p.m.
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Google
(
Sponsor booth
)
link »
To reach the booth please join our Google Meet. Staffed Times (UTC/GMT): We are excited to connect with you at WiML! Visit our booth by any of the below times to chat with a Googler: 08:00 - 09:00 14:00 - 15:10 17:00 - 18:00 Special Events (UTC/GMT): We also welcome you to hear from Jennifer Wei during the WiML Expo, who will be giving a talk entitled “Smell: Learning Generalizable Perceptual Representations of Small Molecules.” The talk will be streamed from 15:10 - 15:25 and then Jennifer Wei will give a live Q&A in our booth from 15:25 - 15:55. More Information: Learn more about the research we’re presenting, including accepted papers, workshops, and tutorials at our ICML Virtual booth. |
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Sun 11:05 p.m. - 4:00 p.m.
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Facebook
(
Sponsor booth
)
link »
To reach the booth please join our ICML sponsor page. Special events (UTC/GMT): 15:55 - 16:10 Kalesha Bullard will be giving a talk on “Learning to Communicate Nonverbally for Embodied Agent Populations”. She will be available at the booth to answer questions right after her talk ends. More information: For more information, please visit our careers page. |
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Sun 11:40 p.m. - 11:50 p.m.
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Introduction and Opening Remarks
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Opening Remarks
)
link »
Please watch the pre-recorded talk on SlidesLive. |
Tatjana Chavdarova 🔗 |
Sun 11:50 p.m. - 12:00 a.m.
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WiML D&I Chairs Remarks: Sinead Williamson and Rachel Thomas
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Workshop
)
link »
SlidesLive Video » Please watch the pre-recorded talk on SlidesLive. |
Sinead A Williamson 🔗 |
Mon 12:00 a.m. - 12:30 a.m.
|
Invited Talk: Sara van de Geer on Total Variation Regularization
(
Talk
)
link »
SlidesLive Video » Please watch the pre-recorded talk on SlidesLive. Abstract: Consider the classical problem of learning a signal when observed with noise. One way to do this is to expand the signal in terms of basis functions and then try to learn the coefficients. The collection of basis functions is called a dictionary and the approach is sometimes called “synthesis” because the signal is synthesised from the coefficients. Another learning approach, called “analysis”, is based on an l_1 regularization of a linear operator that describes the signal’s structure. As an example one may think of a signal that lives on a graph, and the linear operator describes the change when going from one node to the next in the graph. The sum of the absolute values of the changes is called the total variation of the signal over the graph. A simple special case is the path graph, and a more complicated one is the two-dimensional grid. We will consider the regularized least squares estimator for such examples and also regularization using total variation of higher order discrete derivatives and Hardy Krause total variation. We will introduce the concept “effective sparsity” which is related to the dimensionality of the unknown signal. The regularized least squares estimator will be shown to mimic an oracle that trades off approximation error and “estimation error”, where the latter depends on the effective sparsity. |
Sara A van de Geer 🔗 |
Mon 12:30 a.m. - 12:35 a.m.
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Invited Talk Q&A: Sara van de Geer
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Q&A
)
link »
Please post & upvote questions using Sli.do. |
🔗 |
Mon 12:35 a.m. - 1:35 a.m.
|
Breakout Session 1.1: Inference of Cross-lingual Language Models
(
Breakout Sessions #1
)
link »
Leaders: Gagana Coolga, Stuti Gupta Facilitators: Akash Smaran If you are asked for a Zoom password, please use 931226. |
🔗 |
Mon 12:35 a.m. - 1:35 a.m.
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Breakout Session 1.2: ML4GlobalHealth: Issues and Opportunities in Resource-Limited Settings
(
Breakout Sessions #1
)
link »
Leaders: Mary-Anne Hartley, Danielle Belgrave Facilitators: Berthine Nyunga If you are asked for a Zoom password, please use 353535. |
🔗 |
Mon 12:35 a.m. - 1:35 a.m.
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Breakout Session 1.4: AI and Creativity: Generative Art
(
Breakout Sessions #1
)
link »
Leaders: Aneta Neumann, Frehiwot Girmay Facilitators: Aparna Akula, Tina Raissi If you are asked for a Zoom password, please use 566888. |
🔗 |
Mon 12:35 a.m. - 1:35 a.m.
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Breakout Session 1.3: Deep Learning for Natural Language Processing in Low Resource Settings
(
Breakout Sessions #1
)
link »
Leaders: Surangika Ranathunga, Rishemjit Kaur, Annie En-Shiun Lee Facilitators: Marjana Skenduli, Mehreen Alam If you are asked for a Zoom password, please use 312711. |
🔗 |
Mon 1:35 a.m. - 1:50 a.m.
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Virtual Coffee Break
|
🔗 |
Mon 1:50 a.m. - 2:20 a.m.
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Invited Talk: Naila Murray on Predicting Aesthetic Appreciation of Images
(
Talk
)
link »
SlidesLive Video » Please watch the pre-recorded talk on SlidesLive. Abstract: Image aesthetics has become an important criterion for visual content curation on social media sites and media content repositories. Previous work on aesthetic prediction models in the computer vision community has focused on aesthetic score prediction or binary image labeling. However, raw aesthetic annotations are in the form of score histograms and provide richer and more precise information than binary labels or mean scores. In this talk I will present recent work at NAVER LABS Europe on the rarely-studied problem of predicting aesthetic score distributions. The talk will cover the large-scale dataset we collected for this problem, called AVA, and will describe the novel deep architecture and training procedure for our score distribution model. Our model achieves state-of-the-art results on AVA for three tasks: (i) aesthetic quality classification; (ii) aesthetic score regression; and (iii) aesthetic score distribution prediction, all while using one model trained only for the distribution prediction task. I will also discuss our proposed method for modifying an image such that its predicted aesthetics changes, and describe how this modification can be used to gain insight into our model. |
Naila Murray 🔗 |
Mon 2:20 a.m. - 2:25 a.m.
|
Invited Talk Q&A: Naila Murray
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Q&A
)
link »
Please post & upvote questions using Sli.do. |
🔗 |
Mon 2:25 a.m. - 3:25 a.m.
|
Breakout Session 2.1: Well-specified Scalable Models with Variational Inference
(
Breakout Sessions #2
)
link »
Leaders: Ines Krissaane, Samrudhdhi Rangrej Facilitators: Laya Rafiee If you are asked for a Zoom password, please use 392590. |
🔗 |
Mon 2:25 a.m. - 3:25 a.m.
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Breakout Session 2.2: Future of data: How will data diversity become a requirement for training AI models
(
Breakout Sessions #2
)
link »
Leaders: Adepeju Oshisanya, Allison Gardner, Aylin Cakiroglu, Simone Larsson Facilitators: Celine Lature If you are asked for a Zoom password, please use 156371. |
🔗 |
Mon 3:25 a.m. - 3:40 a.m.
|
QuantumBlack: Using AI for social and global good
(
Sponsor Expo
)
link »
SlidesLive Video » Please watch the pre-recorded talk on SlidesLive. |
Maren Eckhoff 🔗 |
Mon 3:40 a.m. - 3:55 a.m.
|
Netflix: Slate Bandit Learning & Evaluation
(
Sponsor Expo
)
link »
SlidesLive Video » Please watch the pre-recorded talk on SlidesLive. |
Maria Dimakopoulou 🔗 |
Mon 3:55 a.m. - 4:10 a.m.
|
IBM: IBM Research AI
(
Sponsor Expo
)
link »
SlidesLive Video » Please watch the pre-recorded talk on SlidesLive. |
Lisa Amini 🔗 |
Mon 4:10 a.m. - 8:10 a.m.
|
Break
|
🔗 |
Mon 7:40 a.m. - 8:10 a.m.
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Social #2: Informal Socializing
(
Social
)
link »
Meet the organizers and other participants for informal socializing on Zoom. |
🔗 |
Mon 8:10 a.m. - 8:25 a.m.
|
Google: Machine Learning for Smell: Learning Generalizable Perceptual Representations of Small Molecules
(
Sponsor Expo
)
link »
SlidesLive Video » Please watch the pre-recorded talk on SlidesLive. |
Jennifer Wei 🔗 |
Mon 8:25 a.m. - 8:35 a.m.
|
Apple: Machine Learning at Apple
(
Sponsor Expo
)
link »
SlidesLive Video » Please watch the pre-recorded talk on SlidesLive. |
Lizi Ottens 🔗 |
Mon 8:35 a.m. - 8:40 a.m.
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Short Break
|
🔗 |
Mon 8:40 a.m. - 8:55 a.m.
|
DeepMind: DeepMind at WiML Un-workshop
(
Sponsor Expo
)
link »
SlidesLive Video » Please watch the pre-recorded talk on SlidesLive. |
Meire Fortunato 🔗 |
Mon 8:55 a.m. - 9:10 a.m.
|
Facebook: Learning to Communicate Nonverbally for Embodied Agent Populations
(
Sponsor Expo
)
link »
SlidesLive Video » Please watch the pre-recorded talk on SlidesLive. |
Kalesha Bullard 🔗 |
Mon 9:10 a.m. - 9:40 a.m.
|
Invited Talk: Nancy Reid on Distributions for Parameters
(
Talk
)
link »
SlidesLive Video » Please watch the pre-recorded talk on SlidesLive. Abstract: There has been considerable recent controversy over the use of p-values and the phrase “statistically significant”, both in subject matter settings and in the statistical literature. One approach to avoiding the dichotomization associated with hypothesis testing is to provide distributions for parameters. A familiar distribution is the posterior density of Bayesian inference, but there are renewed efforts to provide something like probability statements for parameter while avoiding specification of a prior probability. I will discuss the strengths and limitations of these procedures, with special attention to so-called objective Bayesian approaches. |
Nancy Reid 🔗 |
Mon 9:40 a.m. - 9:45 a.m.
|
Invited Talk Q&A: Nancy Reid
(
Q&A
)
link »
Please post & upvote questions using Sli.do. |
🔗 |
Mon 9:45 a.m. - 10:45 a.m.
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Breakout Session 3.1: Healthcare and Machine Learning: Real World Applications and Challenges
(
Breakout Sessions #3
)
link »
Leaders: Olga Liakhovich, Tempest van Schaik, Summer Elasady, Bianca Furtuna Facilitators: Katie Claveau If you are asked for a Zoom password, please use 021583. |
🔗 |
Mon 9:45 a.m. - 10:45 a.m.
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Breakout Session 3.2: Recommender System Research in Industry
(
Breakout Sessions #3
)
link »
Leaders: Ghazal Fazelnia, Zahra Nazari, Mozhgan Saeidi Facilitators: Krystal Maughan, Sneha Srinivasan If you are asked for a Zoom password, please use 309398. |
🔗 |
Mon 9:45 a.m. - 10:45 a.m.
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Breakout Session 3.3: Applied Category Theory
(
Breakout Sessions #3
)
link »
Leaders: Tai-Danae Bradley Facilitators: Melanie Weber If you are asked for a Zoom password, please use 549805. |
🔗 |
Mon 9:45 a.m. - 10:45 a.m.
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Breakout Session 3.4: Mining Biological and Biomedical Data with Graph-Based Algorithms
(
Breakout Sessions #3
)
link »
Leaders: Natalie Stanley, Huda Nassar, Ina Stelzer Facilitators: Jolene Ranek If you are asked for a Zoom password, please use 683278. |
🔗 |
Mon 9:45 a.m. - 10:45 a.m.
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Breakout Session 3.5: Feminist Perspectives for Machine Learning & Computer Vision
(
Breakout Sessions #3
)
link »
Leaders: Fatemehsadat Mireshghallah, Srishti Yadav, Mary Anne Smart Facilitators: Jin (Alice) Qixuan If you are asked for a Zoom password, please use 996705. |
🔗 |
Mon 9:45 a.m. - 10:45 a.m.
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Breakout Session 3.6: Tackling Climate Change with Machine Learning
(
Breakout Sessions #3
)
link »
Leaders: Priya Donti, Sasha Luccioni Facilitators: David Rolnick If you are asked for a Zoom password, please use 77918249. |
🔗 |
Mon 9:45 a.m. - 10:45 a.m.
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Breakout Session 3.7: Not Just Another Application - Applications for Social Good
(
Breakout Sessions #3
)
link »
Leaders: Jennifer Hobbs, Saba Dadsetan, Naira Hovakimyan Facilitators: Tania Lorido Botran, Lori Liu If you are asked for a Zoom password, please use 252034. |
🔗 |
Mon 9:45 a.m. - 10:45 a.m.
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Breakout Session 3.8: Optimization Challenges of Generative Adversarial Networks
(
Breakout Sessions #3
)
link »
Leaders: Reyhane Askari Hemmat, Alexia Jolicoeur-Martineau, Laya Rafiee Facilitators: Xing Han If you are asked for a Zoom password, please use 893180. |
🔗 |
Mon 9:45 a.m. - 10:45 a.m.
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Breakout Session 3.9: Challenges and Practices in Deploying AI in Medical Imaging
(
Breakout Sessions #3
)
link »
Leaders: Weiwei Zong, Manju Liu Facilitators: Zhen Sun If you are asked for a Zoom password, please use 344525. |
🔗 |
Mon 9:45 a.m. - 10:45 a.m.
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Breakout Session 3.10: Entangled Conversations on Disentangled Representations (EnCoDR)
(
Breakout Sessions #3
)
link »
Leaders: Chhavi Yadav, Irina Higgins, Jovana Mitrović Facilitators: Laure Delisle, Niveditha Kalavakonda If you are asked for a Zoom password, please use 07132020. |
🔗 |
Mon 10:45 a.m. - 11:00 a.m.
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Virtual Coffee Break
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Mon 11:00 a.m. - 11:30 a.m.
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Invited Talk: Doina Precup on Building Knowledge for AI Agents with Reinforcement Learning
(
Talk
)
link »
SlidesLive Video » Please watch the pre-recorded talk on SlidesLive. Abstract: Reinforcement learning allows autonomous agents to learn how to act in a stochastic, unknown environment, with which they can interact. Deep reinforcement learning, in particular, has achieved great success in well-defined application domains, such as Go or chess, in which an agent has to learn how to act and there is a clear success criterion. In this talk, I will focus on the potential role of reinforcement learning as a tool for building knowledge representations in AI agents whose goal is to perform continual learning. I will examine a key concept in reinforcement learning, the value function, and discuss its generalization to support various forms of predictive knowledge. I will also discuss the role of temporally extended actions, and their associated predictive models, in learning procedural knowledge. Finally, I will discuss the challenge of how to evaluate reinforcement learning agents whose goal is not just to control their environment, but also to build knowledge about their world. |
Doina Precup 🔗 |
Mon 11:30 a.m. - 11:35 a.m.
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Invited Talk Q&A: Doina Precup
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Q&A
)
link »
Please post & upvote questions using Sli.do. |
🔗 |
Mon 11:35 a.m. - 12:35 p.m.
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Breakout Session 4.1: Un-Bookclub - Race After Technology
(
Breakout Sessions #4
)
link »
Leaders: Anoush Najarian, Ishaani, Aleshia Hayes Facilitators: Sindhuja Parimalarangan, Louvere Walker-Hannon If you are asked for a Zoom password, please use 565501. |
🔗 |
Mon 11:35 a.m. - 12:35 p.m.
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Breakout Session 4.2: Fairness and Bias in ML and NLP
(
Breakout Sessions #4
)
link »
Leaders: Swetasudha Panda, Emily Black, Xueru Zhang Facilitators: Shikha Bordia If you are asked for a Zoom password, please use 21647420. |
🔗 |
Mon 11:35 a.m. - 12:35 p.m.
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Breakout Session 4.3: Coping with Sample Inefficiency of Deep-Reinforcement Learning (DRL) for Embodied AI
(
Breakout Sessions #4
)
link »
Leaders: Vidhi Jain, Simin Liu Facilitators: Ganesh Iyer If you are asked for a Zoom password, please use 890211. |
🔗 |
Mon 11:35 a.m. - 12:35 p.m.
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Breakout Session 4.4: Performative Prediction - When Predictions Impact the Predicted
(
Breakout Sessions #4
)
link »
Leaders: Celestine Mendler-Dünner, Tijana Zrnic Facilitators: Juan Carlos Perdomo If you are asked for a Zoom password, please use 13072020. |
🔗 |
Mon 11:35 a.m. - 12:35 p.m.
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Breakout Session 4.5: Robust Machine Learning with Bad Training Data
(
Breakout Sessions #4
)
link »
Leaders: Sergul Aydore, Haleh Akrami Facilitators: Berna Kabadayi If you are asked for a Zoom password, please use 221530. |
🔗 |
Mon 11:35 a.m. - 12:35 p.m.
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Breakout Session 4.6: Machine Learning for Neuroimaging
(
Breakout Sessions #4
)
link »
Leaders: Elvisha Dhamala, Meenakshi Khosla Facilitators: Carmen Khoo If you are asked for a Zoom password, please use 112211. |
🔗 |
Mon 11:35 a.m. - 12:35 p.m.
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Breakout Session 4.7: A Review of Early Exit Training and Inference Techniques
(
Breakout Sessions #4
)
link »
Leaders: Vaidheeswaran Archana, Sherin Mathews, Yashika Sharma Facilitators: Zahra Vaseqi If you are asked for a Zoom password, please use 179681. |
🔗 |
Mon 11:35 a.m. - 12:35 p.m.
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Breakout Session 4.8: Continual Reinforcement Learning
(
Breakout Sessions #4
)
link »
Leaders: Khimya Khetarpal, Rose E. Wang, Feryal Behbahani Facilitators: Arundhati Banerjee If you are asked for a Zoom password, please use 925172. |
🔗 |
Mon 11:35 a.m. - 12:35 p.m.
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Breakout Session 4.9: Uncertainty Estimation in Bayesian Deep Learning
(
Breakout Sessions #4
)
link »
Leaders: Polina Kirichenko, Melanie F. Pradier, Weiwei Pan Facilitators: Ana-Denisa Secuiu If you are asked for a Zoom password, please use 294409. |
🔗 |
Mon 11:35 a.m. - 12:35 p.m.
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Breakout Session 4.10: Towards Children-Aware Machine Learning with a Focus on NLP Challenges and Applications
(
Breakout Sessions #4
)
link »
Leaders: Belen Saldias, Safinah Ali Facilitators: Tamara Covacevich, Clare Liu If you are asked for a Zoom password, please use 123789. |
🔗 |
Mon 12:35 p.m. - 1:35 p.m.
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Mentoring Panel: Doina Precup, Deborah Raji, Anima Anandkumar, Angjoo Kanazawa and Sinead Williamson (moderator).
(
Panel
)
link »
Please post & upvote questions using Sli.do. |
Doina Precup · Inioluwa Raji · Angjoo Kanazawa · Sinead A Williamson · Animashree Anandkumar 🔗 |
Mon 1:35 p.m. - 1:50 p.m.
|
WiML President's Remarks: Sarah Osentoski
(
Closing Remarks
)
link »
SlidesLive Video » Please watch the pre-recorded talk on SlidesLive. |
Sarah Osentoski 🔗 |
Mon 1:50 p.m. - 2:00 p.m.
|
Break
|
🔗 |
Mon 2:00 p.m. - 4:00 p.m.
|
Joint Affinity Groups' Poster Session with Latinx in AI and Queer in AI
(
Affinity Workshop
)
link »
This live poster session will be held in Gather.town. See here for the listing of posters that will be presented. Please note: ICML registration required to enter. Entry is first-come-first-serve. If you are not able to enter, please check back again later, as people will be coming in and out of the Gather.town space, just like any in-person space. Besides this live poster session in Gather.town, each WiML poster has a Slack channel in the WiML Slack that is active for the duration of ICML, and certain posters also pre-recorded 5-min talks on SlidesLive. |
🔗 |
Mon 4:00 p.m. - 4:00 p.m.
|
Exit Survey Link
(
Link
)
link »
Please fill-in the form. |
🔗 |
-
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poster #1
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poster
)
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Melanie Weber 🔗 |
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Poster #2
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poster
)
SlidesLive Video » |
Liyue Shen 🔗 |
-
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Poster #3
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poster
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-
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Poster #4
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poster
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Shikha Verma 🔗 |
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Poster #5
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poster
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-
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Poster #6
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poster
)
SlidesLive Video » |
Shiran Dudy 🔗 |
-
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Poster #7
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poster
)
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Vijayasri Iyer 🔗 |
-
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Poster #8
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poster
)
SlidesLive Video » |
Rhythm Bhatia 🔗 |
-
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Poster #9
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poster
)
SlidesLive Video » |
Niharika Dsouza 🔗 |
-
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Poster #10
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poster
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-
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Poster #11
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poster
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Manvi Agarwal 🔗 |
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Poster #12
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poster
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Angela Zhou 🔗 |
-
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Poster #13
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poster
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Chinasa T Okolo 🔗 |
-
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Poster #14
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poster
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Cristina Garbacea 🔗 |
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Poster #15
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poster
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Emily McQuillin 🔗 |
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Poster #16
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poster
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SlidesLive Video » |
Aishwarya N Jadhav 🔗 |
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Poster #17
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poster
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Sara El Mekkaoui 🔗 |
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Poster #18
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poster
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Gozde Ozcan 🔗 |
-
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Poster #19
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poster
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SlidesLive Video » |
Zahra Fatemi 🔗 |
-
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Poster #20
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poster
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zohreh ovaisi 🔗 |
-
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Poster #21
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poster
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Shiva Ebrahimi 🔗 |
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Poster #22
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poster
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SlidesLive Video » |
Ankita Saha 🔗 |
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Poster #23
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poster
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Xueru Zhang 🔗 |
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Poster #24
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poster
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SlidesLive Video » |
Gwenaelle Cunha Sergio 🔗 |
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Poster #25
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poster
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Ghadeer Abuoda 🔗 |
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Poster #26
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poster
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SlidesLive Video » |
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