The Machine Learning Reading Group is a biweekly reading group for discussing theory-oriented machine learning research papers and topics. Since it's a reading group and not a simply a talk, all attendees should read a session's material in advance so that we can critically discuss core ideas.
The next session will be on Wednesday, May 6th 3pm via Zoom.
|May 6||Nishant||The Algorithmic Foundations of Differential Privacy, Pages 37–52 (Sections 3.4–3.5.2)|
|April 27||Bingshan||The Algorithmic Foundations of Differential Privacy, Pages 11–34|
|February 19||Hamid||Reconciling modern machine learning practice and the bias-variance trade-off|
|January 23||Sajjad||Envy-free classification|
|November 20||Sharoff||A reduction of imitation learning and structured prediction to no-regret online learning|
|September 4||Sajjad||On preserving non-discrimination when combining expert advice|
|August 22||Bingshan||Building bridges: Viewing active learning from the multi-armed bandit lens|
|August 8||Hamid||Active Learning: Part II - Active learning with disagreement graphs|
|August 1||Hamid||Active Learning: Part I - Importance weighted active learning|
|July 11||Sharoff||Sequential transfer in multi-armed bandit with finite set of models|
|June 20||Sajjad||Sample compression schemes for VC classes|
|June 6||Hamid||Learnability can be undecidable|
|May 10||Mahdi||Influence maximization with bandits|
|Apr 12||Bingshan||Near-optimal regret bounds for Thompson sampling|
|Mar 15||Sharoff||Exploiting easy data in online optimization|
|Feb 15||Sajjad||Regret to the best vs. regret to the average|
|Feb 1||Hamid||Online convex programming and generalized infinitesimal gradient ascent|
|Nov 23||Hamid||Regret bounds for lifelong learning|
|Nov 16||Sharoff||Regret bound for the stochastic multi-armed bandit problem|
|Nov 9||Sajjad||Agnostic online learnability|
|Nov 5||Bingshan||Matroid bandits: Fast combinatorial optimization with learning|
If you are interested in participating in MLRG, send an email to sharoff8[at]gmail[dot]com with the subject: "Join MLRG your first name" and include a short academic description of yourself in the body. Once you join, you will be subscribed to the MLRG mailing list which announces the papers to be discussed in future meetings.