Research

Bibliography

1

A. Orlitsky, N.P. Santhanam, and J. Zhang.
Always Good Turing: Asymptotically optimal probability estimation.
In Proceedings of the 44th Annual Symposium on Foundations of Computer Sciece, October 2003.

2

J.C. Kieffer.
A unified approach to weak universal source coding.
IEEE Transactions on Information Theory, 24(6):674--682, November 1978.

3

A. Orlitsky and N.P. Santhanam.
Speaking of infinity.
IEEE Transactions on Information Theory, 50(10):2215--2230, October 2004.

4

I.J. Good.
The population frequencies of species and the estimation of population parameters.
Biometrika, 40(3/4):237--264, December 1953.

5

A. Nadas.
On Turing's formula for word probabilities.
IEEE Transactions on Acoustics, Speech and Signal Processing, ASSP-33(6):1414--1416, December 1985.

6

D. McAllester and R. Schapire.
On the convergence rate of Good Turing estimators.
In Proceedings of the Thirteenth Annual Conference on Computational Learning Theory, 2000.

7

A. Orlitsky, N.P. Santhanam, and J. Zhang.
Universal compression of memoryless sources over unknown alphabets.
IEEE Transactions on Information Theory, 50(7):1469--1481, July 2004.

8

G.H. Hardy and S. Ramanujan.
Asymptotic formulae in combinatory analysis.
Proceedings of London Mathematics Society, 17(2):75--115, 1918.

9

J.H. van Lint and R.M. Wilson.
A course in combinatorics.
Cambridge University Press, 1996.

10

N. Jevtic, A. Orlitsky, and N.P. Santhanam.
A lower bound on compression of unknown alphabets.
Theoretical Computer Science, Feb 2005.

11

A. K. Dhulipala and A. Orlitsky.
Bounds on the redundancy of HMM patterns.
Submitted to 2004 Proceedings of IEEE Symposium on Information Theory.

12

A. Orlitsky, N.P. Santhanam, and J. Zhang.
Always Good Turing: Asymptotically optimal probability estimation.
Science, 302(5644):427--431, October 17 2003.

13

A. Orlitsky, N.P. Santhanam, K. Viswanathan, and J.Zhang.
On modeling profiles instead of values.
In Uncertainty in Artificial Intelligence, 2004.

14

A. Orlitsky, Sajama, N.P. Santhanam, K. Viswanathan, and J. Zhang.
Practical algorithms for modeling sparse data.
Proceedings of the 2004 Proceedings of IEEE Symposium on Information Theory.

15

A. Orlitsky, N.P. Santhanam, K. Viswanathan, and J. Zhang.
Convergence of profile based estimators.
In Proceedings of the IEEE Symposium on Information Theory, 2005.

16

L.G.Valiant.
The complexity of computing the permanent.
Theoretical Computer Science, 8:189-201, 1979.

17

M. Jerrum, A.Sinclair, and Vigoda.
A polynomial-time approximation algorithm for the permanent of a matrix with non-negative entries.
Theory of computing, pages 712-721, 2001.

18

A. Dhulipala and A. Orlitsky.
Experiments with text.
In preparation.

19

A. Orlitsky, N.P. Santhanam, K. Viswanathan, and J. Zhang.
Innovation and pattern entropy of stationary processes.
In Proceedings of the IEEE Symposium on Information Theory, 2005.

20

A. Orlitsky, N.P. Santhanam, and K. Viswanathan.
Population estimation with performance guarantees.
In Proceedings of IEEE Symposium on Information Theory, 2007.

21

A. Wyner and D. Foster.
On the lower limits of entropy estimation.
Submitted to IEEE Transactions on Information Theory.

22

N.P. Santhanam, A. Orlitsky, and K. Viswanathan.
New tricks for old dogs: Large alphabet probability estimation.
In Information Theory Workshop (invited), Lake Tahoe, CA, 2007.

23

H. Bensmail, B. Aruna, O.J. Semmes, and A. Haoudi.
Functional clustering algorithm for high-dimensional proteomics data.
J. Biomed. Biotechnol., v2005(2):80--86, 2005.

24

Workshop on clustering high dimensional data and its applications, Lake Buena Vista, Florida, April 2004.
Held in conjuction with Fourth SIAM International conference on data mining.