Opening Remarks
Please join us immediately after the opening remarks in Emmanuel Candes' Invited Talk
Reliable Predictions? Counterfactual Predictions? Equitable Treatment? Some Recent Progress in Predictive Inference
Recent progress in machine learning provides us with many potentially effective tools to learn from datasets of ever increasing sizes and make useful predictions. How do we know that these tools can be trusted in critical and high-sensitivity systems? If a learning algorithm predicts the GPA of a prospective college applicant, what guarantees do I have concerning the accuracy of this prediction? How do we know that it is not biased against certain groups of applicants? This talk introduces statistical ideas to ensure that the learned models satisfy some crucial properties, especially reliability and fairness (in the sense that the models need to apply to individuals in an equitable manner). To achieve these important objectives, we shall not “open up the black box” and try understanding its underpinnings. Rather we discuss broad methodologies that can be wrapped around any black box to produce results that can be trusted and are equitable. We also show how our ideas can inform causal inference predictive; for instance, we will answer counterfactual predictive problems: i.e. predict the outcome of a treatment would have been given that the patient was actually not treated.
Please use the Mementor portal to schedule virtual mentor sessions during AISTATS and beyond. The goal is to enable mentorship opportunities for researchers in machine learning, both as mentors and mentees, with a special focus on under-represented minorities.
The mentorship session serves as a platform to share experiences. These could be technical and research related (e.g., research topics and technical discussions), or could be about scientific communication (e.g., paper writing, presentation, networking), or could also be mental health, burnouts, work ethics, PhD life etc. The goal is to facilitate sharing of experiences between members of the community which would not happen otherwise.
WiML and Caucus for Women in Statistics
WiML and the Caucus for Women in Statistics (CWS) are excited to announce a joint event at AISTATS 2021. The event has two components: community-driven mentoring, and a panel. The event will be held on the Icebreaker.video platform on Tuesday, April 13, 2021, 12.30pm – 2pm PT.
WiML Homepage
Caucus for Women in Statistics Homepage
Joining Instructions
How to join: Join the Icebreaker event Event limited to 200 participants. You’ll be asked to sign in to Google, and give Icebreaker permission to access your camera and microphone. Google Chrome browser recommended.
Participant instructions: Whether you will participate as a mentor or mentee, we suggest preparing one or two lines to describe your work and research, as well as any other topics you may want to discuss. During the panel, you can type questions for the panelists in Icebreaker chat, so bring any questions on reviewing and publishing! See below for more information on Icebreaker.
Questions? Email workshop@wimlworkshop.org or cws@cwstat.org. Note that this is a separate event from the AISTATS mentoring sessions. By joining the event, you agree to abide by the AISTATS Code of Conduct and WiML Code of Conduct.