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d'Amore, F., Marchetti-Spaccamela A., & Nanni U. (1992).  The Weighted List Update Problem and the Lazy Adversary.
Dagum, P., & Luby M. (1989).  Approximating the Permanent of Graphs with Large Factors.
Dagum, P., Karp R. M., Luby M., & Ross S. (2000).  An Optimal Algorithm for Monte Carlo Estimation. 29,
Dagum, P. (1990).  On the Magnification of Exchange Graphs with Applications to Enumeration Problems (Thesis).
Dagum, P., Karp R. M., Luby M., & Ross S. (2000).  An Optimal Algorithm for Monte-Carlo Estimation. 29(5), 1484-1496.
Dagum, P. (1989).  A Linear-Time Algorithm for Enumerating Perfect Matchings in Skew Bipartite Graphs.
Dagum, P., Karp R. M., Luby M., & Ross S. (1995).  An optimal algorithm for Monte Carlo estimation. Proceedings of the 36th Annual Symposium on Foundations of Computer Science (FOCS'95). 142-149.
Dahlhaus, E., & Karpinski M. (1989).  An Efficient Parallel Algorithm for the 3MIS Problem.
Dahlhaus, E., Hajnal P., & Karpinski M. (1989).  Optimal Parallel Algorithm for the Hamiltonian Cycle Problem on Dense Graphs.
Dahlhaus, E., & Karpinski M. (1994).  On the Computational Complexity of Matching on Chordal and Strongly Chordal Graphs.
Dahlhaus, E., Karpinski M., & Novick M. B. (1989).  Fast Parallel Algorithms for the Clique Separator Decomposition.
Dahlhaus, E., & Karpinski M. (1989).  An Efficient Parallel Algorithm for the Minimal Elimination Ordering (MEO).
Dahlhaus, E., Karpinski M., & Kelsen P. (1992).  An Efficient Parallel Algorithm for Computing a Maximal Independent Set in a Hypergraph of Dimension 3.
Damaskos, S., & Verma D. C. (1989).  Multiplexing Real-Time Channels.
Damaskos, S., & Verma D. C. (1989).  Fast Establishment of Real-Time Channels.
Darken, C., & Moody J. (1991).  Note on Learning Rate Schedules for Stochastic Optimization. 3,
Darken, C., & Moody J. (1992).  Towards Faster Stochastic Gradient Search. 4,
Darken, C., Chang J.., & Moody J. (1992).  Learning Rate Schedules for Faster Stochastic Gradient Search. Neural Networks for Signal Processing.
Darken, C., & Moody J. (1990).  Fast, Adaptive K-Means Clustering: Some Empirical Results. Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN 1990).
Darrell, T., Kloft M., Pontil M., Rätsch G., & Rodner E. (2015).  Machine Learning with Interdependent and Non-identically Distributed Data (Dagstuhl Seminar 15152). Dagstuhl Reports. 5, 18–55.
Dasgupta, S., & Gupta A. (1999).  An Elementary Proof of the Johnson-Lindenstrauss Lemma.
Daskalakis, C., Dimakis A.. G., Karp R. M., & Wainwright M.. J. (2008).  Probabilistic Analysis of Linear Programming Decoding. IEEE Transactions on Information Theory. 54(8), 3565-3578.
Daskalakis, C., Karp R. M., Mossel E., Riesenfeld S., & Verbin E.. (2009).  Sorting and Selection in Posets. 392-401.
Datta, A., Tschantz M. Carl, & Datta A. (2015).  Automated Experiments on Ad Privacy Settings: A Tale of Opacity, Choice, and Discrimination. Proceedings on Privacy Enhancing Technologies. 2015(1), 92-112.
Datta, A., Lu J., & Tschantz M. Carl (2019).  Evaluating Anti-Fingerprinting Privacy Enhancing Technologies. Proceedings of the WWW'19 (World Wide Web Conference).