Journal and Refereed Conference Publications

Winning an Election: On Emergent Strategic Communication in MultiAgent Networks
S. Gupta and A. Dukkipati
In AAMAS: 2020.

Networked MultiAgent Reinforcement Learning with Emergent Communication
S. Gupta, R. Hazra and A. Dukkipati
In AAMAS: 2020.

CUDA : Contradistinguisher for Unsupervised Domain Adaptation
S. Balgi and A. Dukkipati
In Proceedings of the IEEE International Conference on Data Mining (ICDM): 2019.
[arXiv]

Instancebased Inductive Deep Transfer Learning by CrossDataset Querying with Locality Sensitive Hashing.
S. Chowdhury, Annervaz K. M and A. Dukkipati.
In EMNLP Workshop on Deep Learning for Lowresource NLP : 2019.

A generative model for dynamic networks with
applications.
S. Gupta, G. Sharma and A. Dukkipati
In Proceedings of 33rd AAAI Conference on Artificial Intelligence (AAAI): 2019.

Skip Residual Pairwise Networks with Learnable Comparative
Functions for Fewshot Learning.
A. Mehrotra and A. Dukkipati
In IEEE Winter Conference on Applications of Computer Vision (WACV): 2019.

Learning to segment with imagelevel supervision.
G. Pandey and A. Dukkipati
In IEEE Winter Conference on Applications of Computer Vision (WACV): 2019.
[arXiv] 
On Consistency of Compressive Spectral Clustering.
M. S. Pydi and A. Dukkipati
In Proceedings of IEEE International Symposium on Information Theory (ISIT): 21022106, 2018.
[arXiv]

Learning beyond datasets: Knowledge Graph Augmented
Neural Networks for Natural language Processing.
Annervaz K.M, S. Chowdhury and A. Dukkipati
In Proceedings of NAACL HLT: 313322, 2018.
[arXiv] [blog]

On Grobner bases and Krull dimension of residue class
rings of polynomial
rings over integral domains.
M. Francis and A. Dukkipati
Journal of Symbolic Computation 86:119, 2018.
[Link] [arXiv]

On Ideal Lattices, Grobner Bases and Generalized Hash
Functions.
M. Francis and A. Dukkipati
Journal of Algebra and its Applications 17 (06):1850112, 2018.
[arXiv] 
Unsupervised Feature Learning with Discriminative
Encoder.
G. Pandey and A. Dukkipati
In Proceedings of the IEEE International Conference on Data Mining (ICDM):367376, 2017.

Uniform Hypergraph Partitioning: Provable
Tensor Methods and Sampling Techniques.
D. Ghoshdastidar and A. Dukkipati
The Journal of Machine Learning Research 18(50):141, 2017.
[arXiv]

Attentive Recurrent Comparators.
P. Shyam, S. Gupta and A. Dukkipati
In Proceedings of the International Conference on Machine Learning (ICML):31733181, 2017.
[arXiv] [blog]

Variational methods for conditional multimodal deep
learning.
G. Pandey and A. Dukkipati
In Proceedings of the International Joint Conference on Neural Networks (IJCNN): 2017.
[arXiv] [DEEPimagine]

Consistency of Spectral Hypergraph Partitioning under
Planted Partition Model.
D. Ghoshdastidar and A. Dukkipati
The Annals of Statistics 45(1):289315, 2017.
[bibtex] [pdf] [arXiv]

Primes of the form
${x}^{2}+d{y}^{2}$
with
$x\equiv 0\phantom{\rule{0.1em}{0ex}}\left(\mathrm{mod}\phantom{\rule{0.1em}{0ex}}N\right)$
or
$y\equiv 0\phantom{\rule{0.1em}{0ex}}\left(\mathrm{mod}\phantom{\rule{0.1em}{0ex}}N\right)$
.
A. Dukkipati and S. Palimar
Proc. Indian Acad. Sci. (Math. Sci.) 127(1): 3543, 2017. 
On Collapsed representations of hierarchical Completely Random
Measures.
G. Pandey and A. Dukkipati
In Proceedings of the 33rd International Conference on Machine Learning (ICML): 16051613, 2016.
[pdf]

Learning with JensenTsallis kernels.
D. Ghoshdastidar, A. Adsul and A. Dukkipati
IEEE Transactions on Neural Networks and Learning Systems 27(10): 21082119, 2016.
[pdf]

Mixture Modeling with Compact Support Distributions for
Unsupervised Learning .
A. Dukkipati, D. Ghoshdastidar and J. Krishnan
In Proceedings of the International Joint Conference on Neural Networks (IJCNN): 27062713, 2016.
[pdf]

A Provable Generalized Tensor
Spectral Method for Uniform Hypergraph
Partitioning.
D. Ghoshdastidar and A. Dukkipati
In Proceedings of the 32nd International Conference on Machine Learning (ICML), 2015.
[pdf]

A faster algorithm for testing
polynomial representability of functions over finite integer
rings.
A. Guha and A. Dukkipati
Theoretical Computer Science 579:88–99, 2015.
[bibtex] [arXiv]

An algorithmic characterization
of polynomial functions over Z_{p^n}.
A. Guha and A. Dukkipati
Algorithmica 71(1):201218, 2015.
[bibtex] [arXiv]
 Spectral Clustering using
Multilinear SVD: Analysis, Approximations and
Applications.
D. Ghoshdastidar and A. Dukkipati
In Proceedings of 29th AAAI Conference on Artificial Intelligence (AAAI): 26102616, 2015.
[pdf] 
Consistency of Spectral Partitioning
of Uniform Hypergraphs under Planted Partition
Model.
D. Ghoshdastidar and A. Dukkipati
In Advances in Neural Information Processing Systems (NIPS): 397405, 2014.
[pdf]

Newton based Stochastic Optimization using
qGaussian Smoothed Functional Algorithms.
D. Ghoshdastidar, A. Dukkipati and S. Bhatnagar
Automatica 50(10):2606–2614, 2014.

Smoothed functional algorithms for
stochastic optimization using qGaussian
distributions.
D. Ghoshdastidar, A. Dukkipati and S. Bhatnagar
ACM Transactions on Modeling and Computer Simulation 24(3):126, 2014.

Learning by stretching deep networks.
G. Pandey and A. Dukkipati
In Proceedings of the 31st International Conference on Machine Learning (ICML): 17191727, 2014.
[pdf]

Spectral clustering with
Jensentype kernels and their multipoint
extensions.
D. Ghoshdastidar, A. Dukkipati, A. P. Adsul and A. S. Vijayan
In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR): 14721477, 2014.
[pdf]
 To go deep or wide in learning?
G. Pandey and A. Dukkipati
In Proceedings of Seventeenth International Conference on Artificial Intelligence and Statistics (AISTATS): 724732, 2014.
[pdf]

Reduced Grobner bases and
MacaulayBuchberger basis theorem over Noetherian
rings.
M. Francis and A. Dukkipati
Journal of Symbolic Computation 65:114, 2014.
[bibtex] [Link] [arXiv]

Generative maximum entropy learning
for multiclass classification.
A. Dukkipati, G. Pandey, D. Ghoshdastidar, P. Koley and D. M. V. S. Sriram
In Proceedings of IEEE International Conference on Data Mining (ICDM), pp. 141150, IEEE press, 2013. (Regular Paper)
[bibtex] [pdf]

Minimum description length
principle for maximum entropy model selection.
G. Pandey and A. Dukkipati
In Proceedings of IEEE International Symposium on Information Theory (ISIT), pp. 15211525, IEEE press, 2013.
[bibtex] [pdf]

On power law kernels, corresponding
reproducing kernel Hilbert space and applications.
D. Ghoshdastidar and A. Dukkipati
In Proceedings of 27th AAAI Conference on Artificial Intelligence (AAAI) 2013.
[bibtex] [pdf]

Complexity of Gröbner basis
detection and border basis detection.
P. V. Ananth and A. Dukkipati
Theoretical Computer Science 459: 115, 2012.
[bibtex] [Link]

On maximum entropy and minimum
KLdivergence optimization by Gröbner basis
methods.
A. Dukkipati
Applied Mathematics and Computation 218: 1167411687, 2012.
[bibtex]

An algebraic characterization of
rainbow connectivity.
P. V. Ananth and A. Dukkipati
V. P. Gerdt et al. (Eds.) In Proceedings of International Workshop on Computer Algebra in Scientific Computing (CASC): 1221, Springer Lecture Notes in Computer Science, 2012.
[bibtex]

qGaussian based smoothed
functional algorithms for stochastic optimization.
D. Ghoshdastidar, A. Dukkipati and S. Bhatnagar
In Proceedings of IEEE International Symposium on Information Theory (ISIT): 10591063, IEEE press, 2012.

A two stage selective averaging LDPC decoding.
P. D. Kumar and A. Dukkipati
In Proceedings of IEEE International Symposium on Information Theory (ISIT), pp. 28662870, IEEE press, 2012.

Border basis detection is NPcomplete.
P. V. Ananath and A. Dukkipati
In Proceedings of ACM 36th International Symposium on Symbolic and Algebraic Computation (ISSAC): 1118, ACM 2011
[bibtex] [arXiv] [Link]

An algebraic implicitization
and specialization of minimum KLdivergence
models.
A. Dukkipati and J. G. Manathara
V. P. Gerdt et al. (Eds.) In Proceedings of International Workshop on Computer Algebra in Scientific Computing (CASC): 8596, Springer Lecture Notes in Computer Science, 2010.

On KolmogorovNagumo averages and nonextensive
entropy.
A. Dukkipati
In Proceedings of International Symposium on Information Theory and its Applications(ISITA), pp. 446451 IEEE press 2010.

Maximum entropy model based classification with feature
selection.
A. Dukkipati, A. K. Yadav and M. N. Murty
In Proceedings of IEEE International Conference on Pattern Recognition (ICPR), pp. 565568, IEEE press, 2010.

Embedding maximum entropy models in
algebraic varieties by Grobner bases methods.
A. Dukkipati
In Proceedings of IEEE International Symposium on Information Theory (ISIT), pp.19041908, IEEE press, 2009.
[bibtex] 
On measuretheoretic aspects of
nonextensive entropy functionals and corresponding
maximum entropy prescriptions.
A. Dukkipati, S. Bhatnagar and M. N. Murty
Physica A: Statistical Mechanics and its Applications, 384:124138, 2007. [Link]

GelfandYaglomPerez theorem for generalized relative
entropy functionals.
A. Dukkipati, S. Bhatnagar and M. N. Murty
Information Sciences, 177:57075714, 2007.

Nonextensive triangle equality and
other properties of Tsallis
relativeentropy minimization.
A. Dukkipati, M. N. Murty and S. Bhatnagar
Physica A: Statistical Mechanics and its Applications, Vol. 361, pp 124138, 2006.

Information theoretic justification of Boltzmann selection
and its generalization to Tsallis case.
A. Dukkipati, M. N. Murty and S. Bhatnagar
In Proceedings of IEEE Congress on Evolutionary Computation (CEC) . Vol. 2, pp. 16671674, IEEE press, 2005. pdf

Properties of KullbackLeibler crossentropy minimization in
nonextensive framework.
A. Dukkipati, M. N. Murty and S. Bhatnagar
In Proceedings of IEEE International Symposium on Information Theory (ISIT), pp. 23742378, IEEE press, 2005.
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