Statistics and Machine Learning Group

Statistics and Machine Learning Group

@ Indian Institute of Science, Bangalore

Journals and Refereed Conference Proceedings

R. Venkataramnai, P. Dutta, V. Melapudi and A. Dukkipati. Causal Feature Alignment: Learning to Ignore Spurious Background Features. WACV: 2024.
P. Dutta, Kawin M, P. Sinha and A. Dukkipati. Deep Representation Learning for Prediction of Temporal Event Sets in the Continuous Time Domain. ACML: 2023. [arxiv] [code] [blog] [presentation]
R. S. Ayyagari and A. Dukkipati. Risk-Aware Algorithms for Combinatorial Semi-Bandits. (accepted) In Proceedings of International Symposium on Information Theory (ISIT): 2023.
A. Adiga, S. Athreya, K. R. Bhimala, A. Dukkipati, T. Gracious, S. Gupta, B. Hurt, G. Kaur, B. Lewis, M. Marathe, V. Mudkavi, G. K. Patra, N. Rathod, R. Sundaresan, S. Venkataramanan and S. Yasodharan.A Multi-Team Multi-Model Collaborative COVID-19 Forecasting Hub for India. . In Proceedings of Winter Simulation Conference (WSC): 2023
T. Gracious and A. Dukkipati. Dynamic Representation Learning with Temporal Point Processes for Higher-Order Interaction Forecasting. In Proceedings of 37th AAAI Conference on Artificial Intelligence (AAAI): 2023
S. Gupta and A. Dukkipati. Consistency of Constrained Spectral Clustering under Graph Induced Fair Planted Partitions. Conference on Neural Inforation Processing Systems (NeurIPS): 2022.
A. Dukkipati, R. Banerjee, R. S. Ayyagari and D. P. Udaybhai. Learning Skills to Navigate without a Master: A Sequential Multi-Policy Reinforcement Learning Algorithm. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): 2022. [arxiv][video]
S. Gupta, Gururaj K, A. Dukkipati and R. M. Castro. Equipping SBMs with RBMs: An interpretable approach for analysis of networks with covariates. Journal of Complex Networks. (Accepted): 2022. [bibtex][arxiv]
S. Balgi and A. Dukkipati. Contradistinguisher: A Vapnik's Imperative to Unsupervised Domain Adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence: 2021. [bibtex][paper][ code]
R. Hazra, P. Dutta, S. Gupta, M. A. Qaathir, and A. Dukkipati. Active² Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation. In Proceedings of NAACL: 2021. [bibtex][paper][code]
T. Gracious, S. Gupta, A. Kanthali, R. M. Castro and A. Dukkipati. Neural Latent Space Model for Dynamic Networks and Temporal Knowledge Graphs. In Proceedings of 35rd AAAI Conference on Artificial Intelligence (AAAI): 2021. [bibtex][paper,slides, video][code]
N. A. Kande, R. Dkshane, A. Dukkipati and P. K. Yalavarthy. SiameseGAN: A Generative model for Denoising of Spectral Domain Optical Coherence Tomography Images. IEEE Transactions on Medical Imaging 40(1):180-192, 2021. [bibtex][paper][code][github]
S. Gupta and A. Dukkipati. Winning an Election: On Emergent Strategic Communication in Multi-Agent Networks 19th International Conference on Autonomous Agents and Multi-Agent Systems 2020. [bibtex][Extended Abstract, video, arXiv][code]
S. Gupta, R. Hazra and A. Dukkipati. Networked Multi-Agent Reinforcement Learning with Emergent Communication 19th International Conference on Autonomous Agents and Multi-Agent Systems 2020. [bibtex][Extended Abstract, video, arXiv][code]
S. Chowdhury, Annervaz K. M and A. Dukkipati. Instance-based Inductive Deep Transfer Learning by Cross-Dataset Querying with Locality Sensitive Hashing. EMNLP Workshop on Deep Learning for Low-resource NLP, 2019. [code]
S. Balgi and A. Dukkipati. CUDA : Contradistinguisher for Unsupervised Domain Adaptation. In Proceedings of the IEEE International Conference on Data Mining (ICDM): 2019. (Acceptance Rate: 9.08%) [bibtex][code][github]
A. Mehrotra and A. Dukkipati. Skip Residual Pairwise Networks with Learnable Comparative Functions for Few-shot Learning. In IEEE Winter Conference on Applications of Computer Vision (WACV): 2019
S. Gupta, G. Sharma and A. Dukkipati. A generative model for dynamic networks with applications. In Proceedings of 33rd AAAI Conference on Artificial Intelligence (AAAI), 2019. (Acceptance Rate: 16.2%) [bibtex][code]
G. Pandey and A. Dukkipati. Learning to segment with image-level supervision using CRF-CNN. In IEEE Winter Conference on Applications of Computer Vision (WACV): 2019. [code]
M.S. Pydi and A. Dukkipati. On Consistency of Compressive Spectral Clustering. In Proceedings of IEEE International Symposium on Information Theory (ISIT), 2018.
Annervaz K. M, S. Chowdhury and A. Dukkipati. Learning beyond datasets: Knowledge Graph Augmented Neural Networks for Natural language Processing. In Proceedings of NAACL HLT, 2018. (oral) [arXiv] [code] [blog]
G. Pandey and A. Dukkipati. Unsupervised Feature Learning with Discriminative Encoder. In Proceedings of the IEEE International Conference on Data Mining (ICDM): 2017. (Acceptance Rate (Regular): 9.25%). [arxiv] [code]
D. Ghoshdastidar and A. Dukkipati. Uniform Hypergraph Partitioning: Provable Tensor Methods and Sampling Techniques. In The Journal of Machine Learning Research, 18(50):1-41, 2017. [arxiv]
P. Shyam, S. Gupta and A. Dukkipati. Attentive Recurrent Comparators. In Proceedings of the International Conference on Machine Learning (ICML), 2017. [bibtex][arxiv] [code] [blog]
D. Ghoshdastidar and A. Dukkipati. Consistency of Spectral Hypergraph Partitioning under Planted Partition Model . The Annals of Statistics, 45(1): 289-315, 2017. [bibtex][arxiv]
G. Pandey and A. Dukkipati. Variational methods for conditional multimodal deep learning. In Proceedings of the International Joint Conference on Neural Networks (IJCNN): 2017. [arxiv] [ DEEPImagine ]
G. Pandey and A. Dukkipati. On Collapsed representations of hierarchical Completely Random Measures. In Proceedings of the 33rd International Conference on Machine Learning (ICML): 1605-1613, 2016.
A. Dukkipati, D. Ghoshdastidar, and J. Krishnan. Mixture Modelling with Compact Support Distributions for Unsupervised Learning. In Proceedings of the International Joint Conference on Neural Networks (IJCNN): 2706-2713, 2016.
D. Ghoshdastidar, A. Adsul and A. Dukkipati. Learning with Jensen-Tsallis kernels. IEEE Transactions on Neural Networks and Learning Systems 27(10): 2108-2119, 2016.
D. Ghoshdastidar and A. Dukkipati. A Provable Generalized Tensor Spectral Method for Uniform Hypergraph Partitioning.In Proceedings of the 32nd International Conference on Machine Learning (ICML): 400-409, 2015. (Acceptance Rate: 26%)
D. Ghoshdastidar and A. Dukkipati. Spectral Clustering using Multilinear SVD: Analysis, Approximations and Applications. In Proceedings of 29th AAAI Conference on Artificial Intelligence: 2610-2616, 2015. (Acceptance Rate (Oral): 11.95%)
D. Ghoshdastidar and A. Dukkipati. Consistency of Spectral Partitioning of Uniform Hypergraphs under Planted Partition Model. In Advances in Neural Information Processing Systems (NIPS), pp.397-405, 2014. [code] (Acceptance Rate: 24.67%)
D. Ghoshdastidar, A. Dukkipati, and S. Bhatnagar. Newton based stochastic optimization using q-Gaussian smoothed functional algorithms. Automatica, 50(10):2606–2614, 2014. [arxiv] [code]
D. Ghoshdastidar, A. Dukkipati, and S. Bhatnagar. Smoothed functional algorithms for stochastic optimization using q-Gaussian distributions. ACM Transactions on Modeling and Computer Simulation, 24 (3), Article 17, 2014. [arxiv] [code]
G. Pandey and A. Dukkipati. Learning by Stretching Deep Networks. In Proceedings of the 31st International Conference on Machine Learning (ICML), JMLR Workshops and Conference Proceedings: Pages II-1719-II-1727, 2014. (Acceptance Rate: 25%) [code]
D. Ghoshdastidar, A. Dukkipati, A. P. Adsul and A. S. Vijayan. Spectral Clustering with Jensen-type kernels and their multi-point extensions. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1472-1477, IEEE Press, 2014. (Acceptance Rate: 29.88%) [arxiv]
G. Pandey and A. Dukkipati. To go deep or wide in learning? In Proceedings of Seventeenth International Conference on Artificial Intelligence and Statistics (AISTATS'2014): 724-732, 2014. (Acceptance Rate: 36.12%)
A. Dukkipati, G. Pandey, D. Ghoshdastidar, P. Koley, and D. M. V. Satya Sriram. Generative maximum entropy learning for multiclass classification. In Proceedings of IEEE International Conference on Data Mining (ICDM'13), pp. 141-150, IEEE press, 2013. (Acceptance Rate (Regular): 11.62%) [arxiv]
G. Pandey and A. Dukkipati. Minimum description length Principle for maximum entropy model selection. In Proceedings of IEEE International Symposium on Information Theory (ISIT'13), pp. 1521-1525, IEEE press, 2013. [arxiv]
D. Ghoshdastidar and A. Dukkipati. On power law kernels, corresponding reproducing kernel Hilbert space and applications. In Proceedings of 27th AAAI Conference on Artificial Intelligence (AAAI'13): 365-371, 2013. (Acceptance Rate: 29%) [arxiv]
D. Ghoshdastidar, A. Dukkipati and S. Bhatnagar. q-Gaussian based smoothed functional algorithm for stochastic optimization. In Proceedings of IEEE International Symposium on Information Theory (ISIT'12), pp. 1059-1063, IEEE press, 2012. [arxiv]
A. D. Kumar and A. Dukkipati. A two stage selective averaging LDPC decoding. In Proceedings of IEEE International Symposium on Information Theory (ISIT'12), pp. 2866-2870, IEEE press, 2012. [arxiv]
A. Dukkipati. On Kolmogorov-Nagumo averages and nonextensive entropy. In Proceedings of International Symposium on Information Theory and its Applications (ISITA'10), pp. 446-451, IEEE press, 2010.
A. Dukkipati, A. K. Yadav and M. N. Murty. Maximum entropy model based classification with feature selection. In Proceedings of IEEE International Conference on Pattern Recognition (ICPR'10), pp. 565-568, IEEE press, 2010.

Preprints

D. Ghoshdastidar and A. Dukkipati. Uniform Hypergraph Partitioning: Provable Tensor Methods and Sampling Techniques. arXiv preprint (arXiv:1602.06516), 2016.
[code]
D. Ghoshdastidar and A. Dukkipati. Consistency of Spectral Hypergraph Partitioning under Planted Partition Model. arXiv preprint (arXiv:1505.01582), 2015.
D. Ghoshdastidar and A. Dukkipati. Coloring Random Non-Uniform Bipartite Hypergraphs . arXiv preprint (arXiv:1507.00763), 2015.
D. Ghoshdastidar, A. P. Adsul and A. Dukkipati. Learning with Jensen-Tsallis kernels. 2015.
[pdf] [code]

Presentations

Learning beyond Datasets: Knowledge Graph Augmented Neural Networks for Natural Language Processing: Annervaz K M1, Somnath Basu