- Expectations for joining and application process here.
- Communication: via the Lab Slack, weekly 1:1 meeting, biweekly lab meetings (Thursdays at 11am).
- Project Tracking: Notion page created for each project, README on github.
- Code: All code is in project specific github repositories
- Compute: FASRC, submit a request to join our lab which is under
sneel.
- Space: SEC 6th Floor, suite to the left of the elevators. Our Lab Manager Amanda Chieco (achieco@hbs.edu) will set you up with key card access and a desk.
- Seth's contact info: Morgan Hall 485, Cell: 4016327495, Email: sethneel.ai@gmail.com.
Seminars and Groups to sign up for when joining the lab. Please email the relevant person and cc me when you are signing up.
- Kempner Institute Seminar
- Boston Area DP Seminar
- Privacy Tools mailing list and weekly meeting - achoate@g.harvard.edu
- OpenDP mailing list and slack - lfroess@g.harvard.edu
- Theory of Computation email list and seminar - achoate@g.harvard.edu
- Harvard ML Theory Seminar & Slack
- Contrastive Learning from Lillian Weng
- NTK Theory from Andrea Montanari
- Log-Concave Sampling
- Pattern Recognition and Machine Learning by Christopher Bishop - standard text on ML, light on deep learning
- Probability: Theory and Examples (Durrett) - we will make frequent use of tools like the Chernoff bound or Azuma's inequality.
- Understanding Machine Learning (textbook)
- An Introduction to Computational Learning Theory - standard text on PAC-learning & VC-theory
- Online Algorithms in Machine Learning
- UCB Algorithm - stochastic bandits
- Improved Algorithms for Linear Stochastic Bandits
- Tim Roughgarden notes on no-regret algorithms
- Genetic Membership Inference Attack (Homer)
- Membership Inference Attacks on Machine Learning: A Survey
- Creating Artificial Genomes using Neural Networks
- Label only membership inference attacks
- The Algorithmic Foundations of Differential Privacy - at least ch. 1-3, ideally ch. 4
- Gautham Kamath's lecture notes on differential privacy with videos
- Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods
- Counterfactual Explanations for Machine Learning: A Review
- Transparency: Motivations and Challenges
- Explainable ML Tutorial
- Fairness and Machine Learning Book - ch. 1-3