Fetching the paper…
Reading the bibliography…
We tackle the problem of online optimization with a general, possibly unbounded, loss function.
Problem complexity and method efficiency in optimization
Nemirovski, A. and Yudin, D · 1983
Earlier work this paper cites.
Aggregating strategies
Vovk, V. G · 1990
Earlier work this paper cites.
Entropic proximal mappings with applications to nonlinear programming
Teboulle, M · 1992
Earlier work this paper cites.
The weighted majority algorithm
Littlestone, N. and Warmuth, M. K · 1994
Earlier work this paper cites.
A PAC analysis of a Bayesian estimator
Shawe-Taylor, J. and Williamson, R. C · 1997
Earlier work this paper cites.
Some PAC-Bayesian theorems
McAllester, D. A · 1999
Earlier work this paper cites.
Worst-case bounds for the logarithmic loss of predictors
Cesa-Bianchi, N. and Lugosi, G · 2001
Earlier work this paper cites.
The nonstochastic multiarmed bandit problem
Auer, P., Cesa-Bianchi, N., Freund, Y., and Schapire, R. E · 2002
Earlier work this paper cites.
Utilisation des divergences entre mesures en statistique inferentielle
Keziou, A · 2003
Earlier work this paper cites.
Convex optimization
Boyd, S. P. and Vandenberghe, L · 2004
Earlier work this paper cites.
Statistical Learning Theory and Stochastic Optimization
Catoni, O · 2004
Earlier work this paper cites.
Game theory, maximum entropy, minimum discrepancy and robust Bayesian decision theory
Grünwald, P. D. and Dawid, A. P · 2004
Earlier work this paper cites.
Incomplete information and internal regret in prediction of individual sequences
Stoltz, G · 2005
Earlier work this paper cites.
Prediction, learning, and games
Cesa-Bianchi, N. and Lugosi, G · 2006
Earlier work this paper cites.
PAC-Bayesian supervised classification: the thermodynamics of statistical learning
Catoni, O · 2007
Earlier work this paper cites.
Improved second-order bounds for prediction with expert advice
Cesa-Bianchi, N., Mansour, Y., and Stoltz, G · 2007
Earlier work this paper cites.
Logarithmic regret algorithms for online convex optimization
Hazan, E., Agarwal, A. and Kale, S · 2007
Earlier work this paper cites.
PAC-Bayesian bounds for randomized empirical risk minimizers
Alquier, P · 2008
Earlier work this paper cites.
The weak aggregating algorithm and weak mixability
Kalnishkan, Y. and Vyugin, M. V · 2008
Earlier work this paper cites.
Fast learning rates in statistical inference through aggregation
Audibert, J. Y · 2009
Earlier work this paper cites.
Minimax policies for adversarial and stochastic bandits
Audibert, J.-Y. and Bubeck, S · 2009
Earlier work this paper cites.
Dual averaging methods for regularized stochastic learning and online optimization
Xiao, L · 2010
Earlier work this paper cites.
Prédiction de suites individuelles et cadre statistique classique: étude de quelques liens autour de la régression parcimonieuse et des techniques d’agrégation
Gerchinovitz, S · 2011
Cited alongside, same era.
Online learning and online convex optimization
Shalev-Shwartz, S · 2012
Cited alongside, same era.
Forecasting electricity consumption by aggregating specialized experts
Devaine, M., Gaillard, P., Goude, Y., and Stoltz, G · 2013
Cited alongside, same era.
Convex foundations for generalized MaxEnt models
Frongillo, R. and Reid, M. D · 2014
Cited alongside, same era.
Tight bounds for the expected risk of linear classifiers and PAC-Bayes finite-sample guarantees
Honorio, J. and Jaakkola, T · 2014
Cited alongside, same era.
A generalized online mirror descent with applications to classification and regression
A generalization bound for online variational inference
Chérief-Abdellatif, B.-E., Alquier, P., and Khan, M. E · 2019
Later among the works it cites.
Provable smoothness guarantees for black-box variational inference
Domke, J · 2019
Later among the works it cites.
A primer on PAC-Bayesian learning
Guedj, B · 2019
Later among the works it cites.
PAC-Bayes under potentially heavy tails
Holland, M · 2019
Later among the works it cites.
Generalized variational inference: Three arguments for deriving new posteriors
Knoblauch, J., Jewson, J., and Damoulas, T · 2019
Later among the works it cites.
A modern introduction to online learning
Orabona, F · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Orabona, F., Crammer, K., and Cesa-Bianchi, N · 2015
Cited alongside, same era.
Generalized mixability via entropic duality
Reid, M. D., Frongillo, R. M., Williamson, R. C., and Mehta, N · 2015
Cited alongside, same era.
On the properties of variational approximations of Gibbs posteriors
Alquier, P., Ridgway, J., and Chopin, N · 2016
Cited alongside, same era.
PAC-Bayesian bounds based on the Rényi divergence
Bégin, L., Germain, P., Laviolette, F., and Roy, J.-F · 2016
Cited alongside, same era.
Introduction to online convex optimization
Hazan, E · 2016
Cited alongside, same era.
Rényi divergence variational inference
Li, Y. and Turner, R. E · 2016
Cited alongside, same era.
Variational inference: A review for statisticians
Blei, D. M., Kucukelbir, A., and McAuliffe, J. D · 2017
Cited alongside, same era.
Later among the works it cites.
Practical deep learning with Bayesian principles
Osawa, K., Swaroop, S., Khan, M. E., Jain, A., Eschenhagen, R., Turner, R. E., and Yokota, R · 2019
Later among the works it cites.
An optimal algorithm for stochastic and adversarial bandits
Zimmert, J. and Seldin, Y · 2019
Later among the works it cites.
Optimal bounds between f f -divergences and integral probability metrics
Agrawal, R. and Horel, T · 2020
Closest in time.
Approximate Bayesian inference
Alquier, P · 2020
Closest in time.
Concentration of tempered posteriors and of their variational approximations
Alquier, P. and Ridgway, J · 2020
Closest in time.
Contributions to the theoretical study of variational inference and robustness
Cherief-Abdellatif, B.-E · 2020
Closest in time.
Asymptotic consistency of loss-calibrated variational Bayes
Jaiswal, P., Honnappa, H., and Rao, V. A · 2020
Closest in time.
Dynamics of coordinate ascent variational inference: A case study in 2d ising models
Plummer, S., Pati, D., and Bhattacharya, A · 2020
Closest in time.
PAC-Bayes analysis beyond the usual bounds
Rivasplata, O., Kuzborskij, I., Szepesvári, C., and Shawe-Taylor, J · 2020
Closest in time.
α \alpha -variational inference with statistical guarantees
Yang, Y., Pati, D., and Bhattacharya, A · 2020
Closest in time.
Convergence rates of variational posterior distributions
Zhang, F. and Gao, C · 2020
Closest in time.
PAC-Bayes bounds on Variational Tempered Posteriors for Markov Models
Banerjee, I., Rao, V. A., and Honnappa, H · 2021
Closest in time.
Loss-based variational Bayes prediction
Frazier, D. T., Loaiza-Maya, R., Martin, G. M., and Koo, B · 2021
Closest in time.
On the robustness to misspecification of α \alpha -posteriors and their variational approximations
Medina, M. A., Olea, J. L. M., Rush, C., and Velez, A · 2021
Closest in time.
Novel change of measure inequalities with applications to PAC-Bayesian bounds and Monte Carlo estimation
Ohnishi, Y. and Honorio, J · 2021
Closest in time.