References & Further Reading
References
- N. Cesa-Bianchi and G. Lugosi, Prediction, Learning, and Games, Cambridge University Press, 2006
The canonical reference for adversarial online learning. Theorem 2.2 is the multiplicative-weights regret bound cited in Section 25.2.
- E. Hazan, Introduction to Online Convex Optimization, 2016
Modern treatment of OCO and regret minimization. Used for the online-to-batch conversion and projected-gradient regret analysis.
- S. Bubeck and N. Cesa-Bianchi, Regret Analysis of Stochastic and Nonstochastic Multi-Armed Bandit Problems, 2012
Comprehensive survey of stochastic and adversarial bandits. Covers UCB, EXP3, and the lower-bound techniques used in Section 25.2.
- P. Auer, N. Cesa-Bianchi, and P. Fischer, Finite-Time Analysis of the Multiarmed Bandit Problem, 2002
Original paper introducing UCB1 with the logarithmic regret bound cited in Theorem 25.2.2.
- H. Robbins, Some Aspects of the Sequential Design of Experiments, 1952
The foundational paper framing sequential decision problems in statistics — the ancestor of modern bandit theory.
- H. Robbins and S. Monro, A Stochastic Approximation Method, 1951
Origin of stochastic approximation, the mathematical parent of modern online estimation and SGD.
- L. Xiao and S. Boyd, Fast Linear Iterations for Distributed Averaging, 2004
The reference for optimal symmetric gossip weights and the spectral-gap convergence rate used in Section 25.3.
- S. Boyd, A. Ghosh, B. Prabhakar, and D. Shah, Randomized Gossip Algorithms, 2006
Rigorous analysis of averaging and pairwise gossip, including the message-complexity lower bounds compared in the simulation.
- R. Olfati-Saber, J. A. Fax, and R. M. Murray, Consensus and Cooperation in Networked Multi-Agent Systems, 2007
Broad survey of consensus dynamics, Laplacian spectra, and applications to distributed estimation and control.
- S. Kar, J. M. F. Moura, and K. Ramanan, Distributed Parameter Estimation in Sensor Networks: Nonlinear Observation Models and Imperfect Communication, 2012
Distributed consensus+innovations estimation with convergence guarantees under random link failures.
- Q. Berthet and P. Rigollet, Computational Lower Bounds for Sparse PCA, 2013
Landmark result establishing a polynomial-time statistical- computational gap for sparse PCA under the planted clique hypothesis — the basis for Section 25.1.
- F. Krzakala, C. Moore, E. Mossel, J. Neeman, A. Sly, L. Zdeborova, and P. Zhang, Spectral Redemption in Clustering Sparse Networks, 2013
Non-backtracking spectral methods closing part of the statistical- computational gap for stochastic block models.
- C. Dwork, F. McSherry, K. Nissim, and A. Smith, Calibrating Noise to Sensitivity in Private Data Analysis, 2006
Introduces differential privacy and the Laplace mechanism — the starting point for privacy-constrained estimation.
- M. J. Wainwright, High-Dimensional Statistics: A Non-Asymptotic Viewpoint, Cambridge University Press, 2019
Comprehensive reference for minimax rates, concentration, and non-asymptotic analysis underlying the tradeoffs in this chapter.
- A. Nemirovski, A. Juditsky, G. Lan, and A. Shapiro, Robust Stochastic Approximation Approach to Stochastic Programming, 2009
Modern convergence analysis for online / stochastic gradient estimation used throughout Section 25.2.
- Z. Chen, E. Björnson, G. Caire, Scalable Cell-Free Massive MIMO Systems With Distributed Processing, 2021
- Y. Wang, G. Caire, Beam Training and Data Transmission Optimization in Millimeter-Wave Vehicular Networks, 2021