Fetching the paper…
Reading the bibliography…
This paper considers a problem of distributed hypothesis testing and social learning.
W. Hoeffding, “Probability inequalities for sums of bounded random variables,” Journal of the American Statistical Association , vol. 58, no. 301, pp. pp. 13–30, 1963. [Online]. Available: http://www.jstor.org/stable/2282952
1963
Earlier work this paper cites.
C. J. S. Paul G. Hoel, Sidney C. Port, Introduction to Stochastic Processes . Waveland Press, 1972
1972
Earlier work this paper cites.
M. H. DeGroot, “Reaching a consensus,” Journal of the American Statistical Association , vol. 69, no. 345, pp. 118–121, 1974. [Online]. Available: http://www.jstor.org/stable/2285509
1974
Earlier work this paper cites.
R. Ahlswede and I. Csiszar, “Hypothesis testing with communication constraints,” IEEE Transactions on Information Theory , vol. 32, no. 4, pp. 533–542, Jul 1986
1986
Earlier work this paper cites.
T. Han, “Hypothesis testing with multiterminal data compression,” IEEE Transactions on Information Theory , vol. 33, no. 6, pp. 759–772, Nov 1987
1987
Earlier work this paper cites.
M. Longo, T. D. Lookabaugh, and R. M. Gray, “Quantization for decentralized hypothesis testing under communication constraints,” IEEE Transactions on Information Theory , vol. 36, no. 2, pp. 241–255, Mar 1990
1990
Earlier work this paper cites.
T. M. Cover and J. A. Thomas, Elements of Information Theory . New York, NY, USA: Wiley-Interscience, 1991
1991
Earlier work this paper cites.
V. V. Veeravalli, T. Basar, and H. V. Poor, “Decentralized sequential detection with a fusion center performing the sequential test,” IEEE Transactions on Information Theory , vol. 39, no. 2, pp. 433–442, Mar 1993
1993
Earlier work this paper cites.
H. Shimokawa, T. S. Han, and S. Amari, “Error bound of hypothesis testing with data compression,” in Information Theory, 1994. Proceedings., 1994 IEEE International Symposium on , Jun 1994, pp. 114–
1994
Earlier work this paper cites.
T. S. Han and S. Amari, “Statistical inference under multiterminal data compression,” IEEE Transactions on Information Theory , vol. 44, no. 6, pp. 2300–2324, Oct 1998
1998
Earlier work this paper cites.
B. Chen, R. Jiang, T. Kasetkasem, and P. K. Varshney, “Channel aware decision fusion in wireless sensor networks,” IEEE Transactions on Signal Processing , vol. 52, no. 12, pp. 3454–3458, Dec 2004
2004
Earlier work this paper cites.
M. Alanyali, S. Venkatesh, O. Savas, and S. Aeron, “Distributed bayesian hypothesis testing in sensor networks,” in American Control Conference, 2004. Proceedings of the 2004 , vol. 6, June 2004, pp. 5369–5374 vol.6
2004
Earlier work this paper cites.
B. Chen and P. K. Willett, “On the optimality of the likelihood-ratio test for local sensor decision rules in the presence of nonideal channels,” IEEE Transactions on Information Theory , vol. 51, no. 2, pp. 693–699, Feb 2005
2005
Earlier work this paper cites.
R. Olfati-Saber, E. Franco, E. Frazzoli, and J. S. Shamma, “Belief consensus and distributed hypothesis testing in sensor networks,” in Workshop on Network Embedded Sensing and Control , Notre Dame University, South Bend, IN, October 2005
2005
Earlier work this paper cites.
A. Anandkumar and L. Tong, “Distributed statistical inference using type based random access over multi-access fading channels,” in 2006 40th Annual Conference on Information Sciences and Systems , March 2006, pp. 38–43
2006
Earlier work this paper cites.
S. Boyd, A. Ghosh, B. Prabhakar, and D. Shah, “Randomized gossip algorithms,” IEEE Transactions on Information Theory , vol. 52, no. 6, pp. 2508–2530, June 2006
2006
Cited alongside, same era.
V. Saligrama, M. Alanyali, and O. Savas, “Distributed detection in sensor networks with packet losses and finite capacity links,” Signal Processing, IEEE Transactions on , vol. 54, no. 11, pp. 4118–4132, Nov 2006
2006
Cited alongside, same era.
Y. Mei, “Asymptotic optimality theory for decentralized sequential hypothesis testing in sensor networks,” IEEE Transactions on Information Theory , vol. 54, no. 5, pp. 2072–2089, May 2008
2008
Cited alongside, same era.
D. Mosk-Aoyama and D. Shah, “Fast distributed algorithms for computing separable functions,” IEEE Transactions on Information Theory , vol. 54, no. 7, pp. 2997–3007, July 2008
2008
Cited alongside, same era.
S. Kar, J. M. F. Moura, and H. V. Poor, “cal Q cal D -learning: A collaborative distributed strategy for multi-agent reinforcement learning through consensus + innovations,” IEEE Transactions on Signal Processing , vol. 61, no. 7, pp. 1848–1862, April 2013
2013
Later among the works it cites.
P. Bianchi, G. Fort, and W. Hachem, “Performance of a distributed stochastic approximation algorithm,” IEEE Transactions on Information Theory , vol. 59, no. 11, pp. 7405–7418, Nov 2013
2013
Later among the works it cites.
Y. Yang and R. S. Blum, “Broadcast-based consensus with non-zero-mean stochastic perturbations,” IEEE Transactions on Information Theory , vol. 59, no. 6, pp. 3971–3989, June 2013
2013
Later among the works it cites.
S. Shahrampour and A. Jadbabaie, “Exponentially fast parameter estimation in networks using distributed dual averaging,” in Decision and Control (CDC), 2013 IEEE 52nd Annual Conference on , Dec 2013, pp. 6196–6201
2013
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
J. B. Predd, S. R. Kulkarni, and H. V. Poor, “A collaborative training algorithm for distributed learning,” IEEE Transactions on Information Theory , vol. 55, no. 4, pp. 1856–1871, April 2009
2009
Cited alongside, same era.
F. Benezit, A. G. Dimakis, P. Thiran, and M. Vetterli, “Order-optimal consensus through randomized path averaging,” IEEE Transactions on Information Theory , vol. 56, no. 10, pp. 5150–5167, Oct 2010
2010
Cited alongside, same era.
T. C. Aysal and K. E. Barner, “Convergence of consensus models with stochastic disturbances,” IEEE Transactions on Information Theory , vol. 56, no. 8, pp. 4101–4113, Aug 2010
2010
Cited alongside, same era.
S. Kar and J. M. F. Moura, “Distributed consensus algorithms in sensor networks: Quantized data and random link failures,” IEEE Transactions on Signal Processing , vol. 58, no. 3, pp. 1383–1400, March 2010
2010
Cited alongside, same era.
K. Rahnama Rad and A. Tahbaz-Salehi, “Distributed parameter estimation in networks,” in Decision and Control (CDC), 2010 49th IEEE Conference on , Dec 2010, pp. 5050–5055
2010
Cited alongside, same era.
A. G. Dimakis, S. Kar, J. M. Moura, M. G. Rabbat, and A. Scaglione, “Gossip algorithms for distributed signal processing,” Proceedings of the IEEE , vol. 98, no. 11, pp. 1847–1864, November 2010. [Online]. Available: http://dx.doi.org/10.1109/JPROC.2010.2052531
2010
Cited alongside, same era.
M. S. Rahman and A. B. Wagner, “On the optimality of binning for distributed hypothesis testing,” IEEE Transactions on Information Theory , vol. 58, no. 10, pp. 6282–6303, Oct 2012
2012
Cited alongside, same era.
A. Jadbabaie, P. Molavi, A. Sandroni, and A. Tahbaz-Salehi, “Non-Bayesian social learning,” Games and Economic Behavior , vol. 76, no. 1, pp. 210–225, 2012
2012
Cited alongside, same era.
A. Jadbabaie, P. Molavi, and A. Tahbaz-salehi, “Information heterogeneity and the speed of learning in social networks,” Working Paper 2013
2013
Later among the works it cites.
M. Mueller-Frank, “A general framework for rational learning in social networks,” Theoretical Economics , vol. 8, no. 1, pp. 1–40, 2013
2013
Later among the works it cites.
A. Lalitha, A. Sarwate, and T. Javidi, “Social learning and distributed hypothesis testing,” in Information Theory (ISIT), 2014 IEEE International Symposium on , June 2014, pp. 551–555
2014
Closest in time.
S. Shahrampour, A. Rakhlin, and A. Jadbabaie, “Distributed Detection : Finite-time Analysis and Impact of Network Topology,” ArXiv e-prints , Sep. 2014
2014
Closest in time.
A. Nedić, A. Olshevsky, and C. A. Uribe, “Nonasymptotic Convergence Rates for Cooperative Learning Over Time-Varying Directed Graphs,” ArXiv e-prints , Oct. 2014
2014
Closest in time.
2014
Closest in time.
A. Lalitha and T. Javidi, “On the rate of learning in distributed hypothesis testing,” in To appear in the Proceedings of the 53rd Annual Allerton Conference on Communication, Control and Computation , Monticello, IL, USA, 2015
2015
Closest in time.
——, “Large deviation analysis for learning rate in distributed hypothesis testing,” in 2015 49th Asilomar Conference on Signals, Systems and Computers , Nov 2015, pp. 1065–1069
2015
Closest in time.
A. Nedić, A. Olshevsky, and C. A. Uribe, “Fast Convergence Rates for Distributed Non-Bayesian Learning,” ArXiv e-prints , Aug. 2015
2015
Closest in time.
A. K. Sahu and S. Kar, “Distributed sequential detection for gaussian shift-in-mean hypothesis testing,” IEEE Transactions on Signal Processing , vol. 64, no. 1, pp. 89–103, Jan 2016
2016
Closest in time.