• Avrim Blum
• Sebastien Bubeck
• Moritz Hardt
• Elad Hazan,
• Sham Kakade
• Sasha Rakhlin
• Aaron Roth
• Rob Schapire
• Shai Shalev-Schwartz
• Karthik Sridharan,
• Vasilis Syrgkanis

Generalization bounds and sample complexity

• Sample based generalization bounds, Williamson, Shawe-Taylor, Scholkopf, Smola.
• Adaptive learning with robust generalization guarantees, Cummings et al.
• Generalized topic modeling by Blum and Haghtalab (see references inside for more related papers)

Online learning

• The computational power of optimization in online learning, Hazan and Koren.
• Kernel-based methods for bandit convex optimization, Bubeck, Eldan, Lee
• Regularization techniques for learning with matrices, Kakade, Shalev-Shwartz, Tewari.
• The best of both worlds: stochastic and adversarial bandits, Bubeck and Slivkins.
• Know your customer - multi-armed bandits with capacity constraints, Johari, Kamble, Kanoria.
• The importance of exploration in online marketplaces, Banerjee, Zhou and Johari.
• Approximation algorithms for restless bandits, Guha, Munagala, Shi.
• Taming the monster: A fast and simple algorithm for contextual bandits
• Analysis of Thompson Sampling for multi-armed bandits, Agrawal and Goyal.
• Projection-free online learning, Hazan, Kale
• Oracle-efficient online learning and auctions design by Dudik et al

Optimization related

• Linear coupling: an ultimate unification of gradient and mirror descent, Allen-Zhu and Orecchia.
• Katyusha: The first direct acceleration of stochastic gradient methods, Allen-Zhu.

Learning with malicious, untrusted, costly or manipulated data, game theoretic aspects

• Algorithmic game theory and data science references, by Vasilis Syrgkanis
• Oracle efficient online learning and auction design Dudik et al.
• Fast convergence of regularized learning in games, Syrgkanis et al.
• Incentive compatible regression learning Dekel et al
• Truthful univariate estimators, Caragiannis et al
• Truthful linear regression, Cummings et al
• Accuracy for sale, Cummings et al
• Double or nothing: multiplicative incentive mechanisms for crowdsourcing Shah et al
• Efficient PAC learning from the crowd Awasthi et al.
• No oops, you won't do it again: mechanisms for self-correction in crowdsourcing, by Shah et al
• Learning from untrusted data, Charikar, Steinhardt and Valiant.
• Low-cost learning via active data procurement Abernethy, Chen, Ho and Waggoner.
• Optimum statistical estimation with strategic data resources Cai, Daskalakis, Papadimitriou.
• A bandit framework for strategic regression, Liu and Chen.
• Provably manipulation-resistant reputation systems, Christiano.
• Bayesian exploration: incentivizing exploration in Bayesian games , Mansour et al
• Incentivizing exploration Frazier et al.
• Watch and learn: optimizing from revealed preferences feedback, Roth et al
• Peer prediction with heterogeneous users Agarwal et al
• Avoiding impostors and delinquents: adversarial crowdsourcing and peer prediction, by Steinhardt et al
• Regularized minimax conditional entropy for crowdsourcing

Other

• Clustering with Bregman divergences, Banerjee, Merugu, Dhillon, Ghosh