Fetching the paper…
Reading the bibliography…
We investigate the contraction coefficients derived from strong data processing inequalities for the $E_\gamma$-divergence.
R. L. Dobrushin, “Central limit theorem for nonstationary markov chains. I,”
1956
Earlier work this paper cites.
M. Frank and P. Wolfe, “An algorithm for quadratic programming,”
1956
Earlier work this paper cites.
M. H. DeGroot, “Uncertainty, information, and sequential experiments,”
1962
Earlier work this paper cites.
S. L. Warner, “Randomized response: A survey technique for eliminating evasive answer bias,”
1965
Earlier work this paper cites.
S. M. Ali and S. D. Silvey, “A general class of coefficients of divergence of one distribution from another,”
1966
Earlier work this paper cites.
I. Csiszár, “Information-type measures of difference of probability distributions and indirect observations,”
1967
Earlier work this paper cites.
I. Vajda, “Note on discrimination information and variation (corresp.),”
1970
Earlier work this paper cites.
L. LeCam, “Convergence of estimates under dimensionality restrictions,”
1973
Earlier work this paper cites.
R. Ahlswede and P. Gács, “Spreading of sets in product spaces and hypercontraction of the markov operator,”
1976
Earlier work this paper cites.
J. Bretagnolle and C. Huber, “Estimation des densités: risque minimax,”
1978
Earlier work this paper cites.
T. M. Cover, “Universal portfolios,”
1991
Earlier work this paper cites.
P. Billingsley,
1995
Earlier work this paper cites.
J. Cohen, J. Kemperman, and G. Zbăganu,
1998
Earlier work this paper cites.
Y. Yang and A. Barron, “Information-theoretic determination of minimax rates of convergence,”
1999
Earlier work this paper cites.
2001
Earlier work this paper cites.
A. Kalai and S. Vempala, “Efficient algorithms for universal portfolios,”
2002
Earlier work this paper cites.
A. Evfimievski, J. Gehrke, and R. Srikant, “Limiting privacy breaches in privacy preserving data mining,” in
2003
Earlier work this paper cites.
E. Takimoto and M. K. Warmuth, “Path kernels and multiplicative updates,”
2003
Earlier work this paper cites.
M. Zinkevich, “Online convex programming and generalized infinitesimal gradient ascent,” in
2003
Earlier work this paper cites.
P. Del Moral, M. Ledoux, and L. Miclo, “On contraction properties of markov kernels,”
2003
Earlier work this paper cites.
I. Csiszár and P. C. Shields, “Information theory and statistics: A tutorial,”
2004
Earlier work this paper cites.
K. Crammer, O. Dekel, J. Keshet, S. Shalev-Shwartz, and Y. Singer, “Online passive-aggressive algorithms,”
2006
Earlier work this paper cites.
K. Chaudhuri and N. Mishra, “When random sampling preserves privacy,” in
2006
Earlier work this paper cites.
C. Dwork, F. McSherry, K. Nissim, and A. Smith, “Calibrating noise to sensitivity in private data analysis,” in
2006
Earlier work this paper cites.
S. Shalev-Shwartz and Y. Singer, “A primal-dual perspective of online learning algorithms,”
2007
Earlier work this paper cites.
J. Abernethy, E. Hazan, and A. Rakhlin, “Competing in the dark: An efficient algorithm for bandit linear optimization,” 2008. [Online]. Available:
2008
Earlier work this paper cites.
E. Hazan, “Extracting certainty from uncertainty: Regret bounded by variation in costs,” in
2008
Earlier work this paper cites.
A. B. Tsybakov,
2008
Earlier work this paper cites.
J. Ma, L. K. Saul, S. Savage, and G. M. Voelker, “Identifying suspicious urls: An application of large-scale online learning,” in
2009
Earlier work this paper cites.
K. Chaudhuri and C. Monteleoni, “Privacy-preserving logistic regression,” in
2009
Earlier work this paper cites.
S. M. Kakade and A. Tewari, “On the generalization ability of online strongly convex programming algorithms,” in
2009
Earlier work this paper cites.
F. McSherry and I. Mironov, “Differentially private recommender systems: Building privacy into the netflix prize contenders,” in
2009
Earlier work this paper cites.
Y. Polyanskiy, H. V. Poor, and S. Verdú, “Channel coding rate in the finite blocklength regime,”
2010
Earlier work this paper cites.
C. Dwork, M. Naor, T. Pitassi, and G. N. Rothblum, “Differential privacy under continual observation,” in
2010
Earlier work this paper cites.
B. Kulis and P. L. Bartlett, “Implicit online learning,” in
2010
Earlier work this paper cites.
J. C. Duchi, S. Shalev-shwartz, Y. Singer, and A. Tewari, “Composite objective mirror descent,” in
2010
Earlier work this paper cites.
S. P. Kasiviswanathan, H. K. Lee, K. Nissim, S. Raskhodnikova, and A. Smith, “What can we learn privately?”
2011
Earlier work this paper cites.
B. Li, S. Hoi, P. Zhao, and V. Gopalkrishnan, “Confidence weighted mean reversion strategy for on-line portfolio selection,” in
2011
Earlier work this paper cites.
B. McMahan, “Follow-the-regularized-leader and mirror descent: Equivalence theorems and l1 regularization,” in
2011
Earlier work this paper cites.
K. Chaudhuri, C. Monteleoni, and A. D. Sarwate, “Differentially private empirical risk minimization,”
2011
Earlier work this paper cites.
P. Jain, P. Kothari, and A. Thakurta, “Differentially private online learning,” in
2012
Cited alongside, same era.
S. Shalev-Shwartz, “Online learning and online convex optimization,”
2012
Cited alongside, same era.
A. Rakhlin, O. Shamir, and K. Sridharan, “Making gradient descent optimal for strongly convex stochastic optimization,” in
2012
Cited alongside, same era.
T. M. Cover and J. A. Thomas,
2012
Cited alongside, same era.
2013
Cited alongside, same era.
R. Subramanian, B. N. Vellambi, and I. Land, “An improved bound on information loss due to finite block length in a gaussian line network,” in
J. Liu, P. Cuff, and S. Verdú, “
2017
Later among the works it cites.
F. d. P. Calmon, Y. Polyanskiy, and Y. Wu, “Strong data processing inequalities for input constrained additive noise channels,”
2018
Later among the works it cites.
A. Bhowmick, J. Duchi, J. Freudiger, G. Kapoor, and R. Rogers, “Protection against reconstruction and its applications in private federated learning,” 2018
2018
Later among the works it cites.
A. Makur and L. Zheng, “Bounds between contraction coefficients,” 2018. [Online]. Available:
2018
Later among the works it cites.
A. Makur and Y. Polyanskiy, “Comparison of channels: Criteria for domination by a symmetric channel,”
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2013
Cited alongside, same era.
J. C. Duchi, M. I. Jordan, and M. J. Wainwright, “Local privacy, data processing inequalities, and statistical minimax rates,” in
2013
Cited alongside, same era.
A. Guha Thakurta and A. Smith, “(Nearly) optimal algorithms for private online learning in full-information and bandit settings,” in
2013
Cited alongside, same era.
O. Shamir and T. Zhang, “Stochastic gradient descent for non-smooth optimization: Convergence results and optimal averaging schemes,” in
2013
Cited alongside, same era.
A. G. Thakurta and A. Smith, “Differentially private feature selection via stability arguments, and the robustness of the lasso,” in
2013
Cited alongside, same era.
S. Song, K. Chaudhuri, and A. D. Sarwate, “Stochastic gradient descent with differentially private updates,” in
2013
Cited alongside, same era.
J. C. Duchi, M. I. Jordan, and M. J. Wainwright, “Local privacy and statistical minimax rates,” in
2013
Cited alongside, same era.
P. Kamalaruban, “Transitions, losses, and re-parameterizations: Elements of prediction games,” Ph.D. dissertation, The Australian National University, 2018
2018
Later among the works it cites.
M. Ye and A. Barg, “Optimal schemes for discrete distribution estimation under locally differential privacy,”
2018
Later among the works it cites.
V. Feldman, I. Mironov, K. Talwar, and A. Thakurta, “Privacy amplification by iteration,”
2018
Later among the works it cites.
B. Balle and Y.-X. Wang, “Improving the gaussian mechanism for differential privacy: Analytical calibration and optimal denoising,” in
2018
Later among the works it cites.
E. Nozari, P. Tallapragada, and J. Cortés, “Differentially private distributed convex optimization via functional perturbation,”
2018
Later among the works it cites.
Y.-X. Wang, B. Balle, and S. P. Kasiviswanathan, “Subsampled Rényi differential privacy and analytical moments accountant,” in
2018
Later among the works it cites.
2018
Later among the works it cites.
L. Chen, C. Harshaw, H. Hassani, and A. Karbasi, “Projection-free online optimization with stochastic gradient: From convexity to submodularity,” in
2018
Later among the works it cites.
J. Duchi and R. Rogers, “Lower bounds for locally private estimation via communication complexity,” in
2019
Later among the works it cites.
J. Acharya and Z. Sun, “Communication complexity in locally private distribution estimation and heavy hitters,” in
2019
Later among the works it cites.
A. R. Cardoso and R. Cummings, “Differentially private online submodular minimization,” in
2019
Later among the works it cites.
M. Joseph, J. Kulkarni, J. Mao, and S. Z. Wu, “Locally private gaussian estimation,” in
2019
Later among the works it cites.
M. Gaboardi, R. Rogers, and O. Sheffet, “Locally private mean estimation:
2019
Later among the works it cites.
J. Abernethy, K. A. Lai, and A. Wibisono, “Last-iterate convergence rates for min-max optimization,” 2019
2019
Later among the works it cites.
Y. Lei, P. Yang, K. Tang, and D.-X. Zhou, “Optimal stochastic and online learning with individual iterates,” in
2019
Later among the works it cites.
B. Balle, G. Barthe, M. Gaboardi, and J. Geumlek, “Privacy amplification by mixing and diffusion mechanisms,” in
2019
Later among the works it cites.
R. Bassily, V. Feldman, K. Talwar, and A. Guha Thakurta, “Private stochastic convex optimization with optimal rates,” in
2019
Later among the works it cites.
2019
Later among the works it cites.
TensorFlow Privacy, 2019. [Online]. Available:
2019
Later among the works it cites.
V. Feldman and J. Vondrak, “High probability generalization bounds for uniformly stable algorithms with nearly optimal rate,” in
2019
Later among the works it cites.
S. Asoodeh, M. Diaz, and F. P. Calmon, “Privacy amplification of iterative algorithms via contraction coefficients,” in
2020
Closest in time.
A. Makur and L. Zheng, “Comparison of contraction coefficients for
2020
Closest in time.
A. Rohde and L. Steinberger, “Geometrizing rates of convergence under local differential privacy constraints,”
2020
Closest in time.
L. P. Barnes, W. N. Chen, and A. Özgür, “Fisher information under local differential privacy,”
2020
Closest in time.
J. Acharya, C. L. Canonne, and H. Tyagi, “Inference under information constraints i: Lower bounds from chi-square contraction,”
2020
Closest in time.
D. Wang and J. Xu, “On sparse linear regression in the local differential privacy model,”
2020
Closest in time.
T. Berrett and C. Butucea, “Locally private non-asymptotic testing of discrete distributions is faster using interactive mechanisms,” in
2020
Closest in time.
Q. Lei, S. G. Nagarajan, I. Panageas, and X. Wang, “Last iterate convergence in no-regret learning: constrained min-max optimization for convex-concave landscapes,” 2020
2020
Closest in time.
V. Feldman, T. Koren, and K. Talwar, “Private stochastic convex optimization: Optimal rates in linear time,” in
2020
Closest in time.
S. Augenstein, H. B. McMahan, D. Ramage, S. Ramaswamy, P. Kairouz, M. Chen, R. Mathews, and B. A. y Arcas, “Generative models for effective ML on private, decentralized datasets,” in
2020
Closest in time.
R. Bassily, V. Feldman, C. Guzmán, and K. Talwar, “Stability of stochastic gradient descent on nonsmooth convex losses,” in
2020
Closest in time.
S. Asoodeh, M. Aliakbarpour, and F. P. Calmon, “Local differential privacy is equivalent to contraction of an
2021
Closest in time.
J. Acharya, C. L. Canonne, C. Freitag, Z. Sun, and H. Tyagi, “Inference under information constraints iii: Local privacy constraints,”
2021
Closest in time.
S. Asoodeh, J. Liao, F. P. Calmon, O. Kosut, and L. Sankar, “Three variants of differential privacy: Lossless conversion and applications,”
2021
Closest in time.
M. Sordello, Z. Bu, and J. Dong, “Privacy amplification via iteration for shuffled and online PNSGD,” in
2021
Closest in time.