Research

Recently submitted

N.P. Santhanam and M.J. Wainwright.
Learning sparse graphical models.
To be presented at ITA 2008

N.P. Santhanam and M.J. Wainwright.
Information-theoretic limits of graphical model selection in high dimensions.
Submitted to ISIT 2008

H. Das, A. Orlitsky, N.P. Santhanam, J. Zhang.
Further results on relative redundancy.
Submitted to ISIT 2008

Publications in print, (1-5) are journals, rest are conferences

1

A. Orlitsky, N.P. Santhanam, K. Viswanathan, and J. Zhang.
Limit results on pattern entropy.
IEEE Transactions on Information Theory, July 2006.

2

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

3

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

4

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.

5

A. Orlitsky, N.P. Santhanam, and J. Zhang.
Always Good Turing: Asymptotically optimal probability estimation.
Science, 302(5644):427--431, October 17 2003.
See also Proceedings of the 44th Annual Symposium on Foundations of Computer Science, October 2003.

6

A. Orlitsky, N.P. Santhanam, and J. Zhang.
Reflections on universal compression of memoryless sources.
In Information theory newsletter, 2007.

7

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.

8

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

9

A. Orlitsky, N.P. Santhanam, and J. Zhang.
Relative redundancy of large alphabets.
In Proceedings of IEEE Symposium on Information Theory, 2006.

10

D. Modha and N.P. Santhanam.
Making the correct mistakes.
In Proceedings of the Data Compression Conference, 2006.

11

A. Orlitsky and N.P. Santhanam.
On the redundancy of gaussian distributions.
In Proceedings of the 42nd Annual Allerton Conference on Communication, Control, and Computing, 2005.

12

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.

13

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.

14

A. Orlitsky, N.P. Santhanam, K. Viswanathan, and J. Zhang.
Limit results on pattern entropy.
In Information Theory Workshop, 2004.

15

A. Orlitsky, N.P. Santhanam, K. Viswanathan, and J. Zhang.
Information theoretic approach to modeling low probabilities.
In Proceedings of the 42nd Annual Allerton Conference on Communication, Control, and Computing, 2004.

16

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.

17

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

18

A. Orlitsky, N.P. Santhanam, and J. Zhang.
Relative redundancy: A more stringent performance guarantee for universal coding.
In Proceedings of IEEE Symposium on Information Theory, 2004.

19

A. Orlitsky, N.P. Santhanam, and J. Zhang.
Bounds on compression of unknown alphabets.
In Proceedings of IEEE Symposium on Information Theory, July 2003.

20

A. Orlitsky, N.P. Santhanam, K. Viswanathan, and J. Zhang.
On compression and modeling of sparse data.
In Third Asian European Workshop on Coding and Information Theory, June 2003.

21

A. Orlitsky and N.P. Santhanam.
Performance of universal codes over infinite alphabets.
In Proceedings of the Data Compression Conference, March 2003.

22

N. Jevtic, A. Orlitsky, and N.P. Santhanam.
Universal compression of unknown alphabets.
In Proceedings of IEEE Symposium on Information Theory, 2002.