Machine Learning Reading Group

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.

Meetings from previous years (back to current page)

Spring 2020
date	lead	topic
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

Fall 2019
date	lead	topic
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

Summer 2019
date	lead	topic
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

Spring 2019
date	lead	topic
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

Fall 2018
date	lead	topic
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