Online Learning of Independent Cascade Models with Node-Level Feedback
References
- [1] (2011) Improved algorithms for linear stochastic bandits. Shawe-Taylor J, Zemel RS, Bartlett PL, Pereira F, Weinberger KQ, eds. NIPS’11: Proc. 25th Internat. Conf. Adv. Neural Inform. Processing Systems (Curran Associates Inc., Red Hook, NY), 2312–2320.Google Scholar
- [2] (2017) Thompson sampling for the MNL-bandit. Conf. Learning Theory (PMLR, New York), 76–78.Google Scholar
- [3] (2019) MNL-bandit: A dynamic learning approach to assortment selection. Oper. Res. 67(5):1453–1485.Link, Google Scholar
- [4] (2014) Regret in online combinatorial optimization. Math. Oper. Res. 39(1):31–45.Link, Google Scholar
- [5] (2007) Competitive influence maximization in social networks. Deng X, Graham FC, eds. Internet Network Econom. WINE 2007, Lecture Notes in Computer Science, vol. 4858 (Springer, Berlin, Heidelberg), 306–311.Google Scholar
- [6] (2009) Simultaneous analysis of lasso and Dantzig selector. Ann. Statist. 37(4):1705–1732.Crossref, Google Scholar
- [7] (1999) Strong consistency of maximum quasi-likelihood estimators in generalized linear models with fixed and adaptive designs. Ann. Statist. 27(4):1155–1163.Crossref, Google Scholar
- [8] (2020) Revenue maximization and learning in products ranking. Preprint, submitted December 7, https://arxiv.org/abs/2012.03800.Google Scholar
- [9] (2013) Combinatorial multi-armed bandit: General framework and applications. ICML’13: Proc. 30th Internat. Conf. Machine Learn., vol. 28 (PMLR, New York), 151–159.Google Scholar
- [10] (2016) Combinatorial multi-armed bandit and its extension to probabilistically triggered arms. J. Machine Learn. Res. 17(1):1746–1778.Google Scholar
- [11] (2016) Robust influence maximization. KDD’16: Proc. 22nd ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 795–804.Google Scholar
- [12] (2019) A Thompson sampling algorithm for cascading bandits. 22nd Internat. Conf. Artificial Intelligence Statist. (PMLR, New York), 438–447.Google Scholar
- [13] Combes R, Talebi MS, Proutiere A, Lelarge M (2015) Combinatorial bandits revisited. Cortes C, Lee DD, Sugiyama M, Garnett R, eds. NIPS’15: Proc. 29th Internat. Conf. Adv. Neural Inform. Processing Systems, vol. 2 (MIT Press, Cambridge, MA), 2116–2124. Google Scholar
- [14] (1997) On an absolute criterion for fitting frequency curves. Statist. Sci. 12(1):39–41.Google Scholar
- [15] (2012) Combinatorial network optimization with unknown variables: Multi-armed bandits with linear rewards and individual observations. IEEE/ACM Trans. Networking 20(5):1466–1478.Crossref, Google Scholar
- [16] (2010) Learning influence probabilities in social networks. WSDM ‘10: Proc. 3rd ACM Internat. Conf. Web Search Data Mining (Association for Computing Machinery, New York), 241–250.Google Scholar
- [17] (2016) Robust influence maximization. Proc. 22nd ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 885–894.Google Scholar
- [18] (2018) Stability and robustness in influence maximization. ACM Trans. Knowledge Discovery Data 12(6):1–34.Crossref, Google Scholar
- [19] (2016) DCM bandits: Learning to rank with multiple clicks. Balcan MF, Weinberger KQ, eds. ICML’16: Proc. 33rd Internat. Conf. Machine Learn., vol. 48 (PMLR, New York), 1215–1224.Google Scholar
- [20] (2003) Maximizing the spread of influence through a social network. KDD’03: Proc. 9th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 137–146.Google Scholar
- [21] (2015) Cascading bandits: Learning to rank in the cascade model. Bach F, Blei D, eds. ICML’15: Proc. 32nd Internat. Conf. Machine Learn., vol. 36 (PMLR, New York), 767–776.Google Scholar
- [22] (2015a) Combinatorial cascading bandits. Cortes C, Lee DD, Sugiyama M, Garnett R, eds. NIPS’15: Proc. 29th Internat. Conf. Adv. Neural Inform. Processing Systems, vol. 1 (MIT Press, Cambridge, MA), 1450–1458.Google Scholar
- [23] (2015b) Tight regret bounds for stochastic combinatorial semi-bandits. Proc. 18th Internat. Conf. Intelligence Statist. (PMLR, New York), 535–543.Google Scholar
- [24] (2020) Bandit Algorithms (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
- [25] (2015) Online influence maximization. PKDD’15: Proc. 21st ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 645–654.Google Scholar
- [26] (2017) Provably optimal algorithms for generalized linear contextual bandits. Proc. 34th Internat. Conf. Machine Learn., vol. 70 (PMLR, New York), 2071–2080.Google Scholar
- [27] (2020) Online influence maximization under linear threshold model. Larochelle H, Ranzato M, Hadsell R, Balcan MF, Lin H, eds. NIPS’20: Proc. 34th Internat. Conf. Adv. Neural Inform. Processing Systems (Curran Associates, Red Hook, NY), 1192–1204.Google Scholar
- [28] (2011) From bandits to experts: On the value of side-observations. Shawe-Taylor J, Zemel RS, Bartlett PL, Pereira F, Weinberger KQ, eds. NIPS’11: Proc. 25th Internat. Conf. Adv. Neural Inform. Processing Systems (Curran Associates Inc., Red Hook, NY), 684–692.Google Scholar
- [29] McAuley J, Leskovec J (2012) Learning to discover social circles in ego networks. NIPS’12: Proc. 26th Internat. Conf. Neural Inform. Processing Systems, vol. 1 (Curran Associates Inc., Red Hook, NY), 539–547.Google Scholar
- [30] (1978) An analysis of approximations for maximizing submodular set functions—I. Math. Programming 14:265–294.Crossref, Google Scholar
- [31] (2012) Learning the graph of epidemic cascades. Sigmetrics Performance Eval. Rev. 40(1):211–222.Crossref, Google Scholar
- [32] (2021) Multinomial logit contextual bandits: Provable optimality and practicality. Proc. AAAI Conf Artificial Intelligence, vol. 35 (AAAI Press, Washington, DC), 9205–9213.Google Scholar
- [33] (2008) Prediction of information diffusion probabilities for independent cascade model. Lovrek I, Howlett RJ, Jain LC, eds. Knowledge-Based Intelligent Inform. Engrg. Systems KES 2008, Lecture Notes in Computer Science, vol. 5179 (Springer, Berlin, Heidelberg), 67–75.Google Scholar
- [34] (2014) Influence maximization: Near-optimal time complexity meets practical efficiency. SIGMOD’14: Proc. 2014 ACM SIGMOD Internat. Conf. Management Data (Association for Computing Machinery, New York), 75–86.Google Scholar
- [35] (1999) SDPT3—A Matlab software package for semidefinite programming, version 1.3. Optim. Methods Software 11(1–4):545–581.Crossref, Google Scholar
- [36] (2016) Bandits on graphs and structures. Unpublished PhD thesis, École normale supérieure de Cachan (ENS Cachan), Paris.Google Scholar
- [37] (2013) Learning influence diffusion probabilities under the linear threshold model. Computer Science Department, University of British Columbia, Vancouver.Google Scholar
- [38] (2015) Influence maximization with bandits. Preprint, submitted February 27, https://arxiv.org/abs/1503.00024.Google Scholar
- [39] (2017) Improving regret bounds for combinatorial semi-bandits with probabilistically triggered arms and its applications. von Luxburg U, Guyon I, Bengio S, Wallach H, Fergus R, eds. NIPS’17: Proc. 31st Internat. Conf. Adv. Neural Inform. Processing Systems (Curran Associates Inc., Red Hook, NY), 1161–1171.Google Scholar
- [40] (2017) Online influence maximization under independent cascade model with semi-bandit feedback. von Luxburg U, Guyon I, Bengio S, Wallach H, Fergus R, eds. NIPS’17: Proc. 31st Internat. Conf. Adv. Neural Inform. Processing Systems (Curran Associates Inc., Red Hook, NY), 3022–3032.Google Scholar
- [41] (2019) Online learning and optimization under a new linear-threshold model with negative influence. Preprint, submitted November 8, https://arxiv.org/abs/1911.03276.Google Scholar
- [42] (2016) Cascading bandits for large-scale recommendation problems. Ihler A, Janzing D, eds. UAI’16: Proc. 32nd Conf. Uncertainty Artificial Intelligence (AUAI Press, Arlington, VA), 835–844.Google Scholar

