Publications

[Preprints] [Journals] [Conferences] [Workshops] [Thesis]

The list of publications are also available on Google scholar and Semantic scholar.

Preprints

H. Kasai, P. Jawanpuria, and B. Mishra. Adaptive stochastic gradient algorithms on Riemannian manifolds. Technical report, arXiv preprint, arXiv:1902.01144, 2019.
[arXiv:1902.01144].

M. Meghawanshi, P. Jawanpuria, A. Kunchukuttan, H. Kasai, and B. Mishra. McTorch, a manifold optimization library for deep learning. Technical report, arXiv preprint, arXiv:1810.01811, 2018. Accepted in Machine Learning Open Source Software (MLOSS) workshop, NeurIPS 2018.
[arXiv:1810.01811][Project page].

Journal articles

B. Mishra, H. Kasai, P. Jawanpuria and A. Saroop. A Riemannian gossip approach to decentralized subspace learning on Grassmann manifold. Machine Learning Journal (MLJ), 2019.
[Local copy][Online html version][Matrix completion code][Multitask code].

P. Jawanpuria, A. Balgovind, A. Kunchukuttan, and B. Mishra. Learning multilingual word embeddings in a latent metric space: a geometric approach. Transactions of the Association for Computational Linguistics (TACL), 2019.
[arXiv:1808.08773].

P. Jawanpuria, J. Saketha Nath and Ganesh Ramakrishnan. Generalized hierarchical kernel learning. Journal of Machine Learning Research, 16(Mar):617−652, 2015.
[Local copy][Publisher’s copy][Matlab codes].

Conference proceedings

M. Nimishakavi, P. Jawanpuria and B. Mishra. A dual framework for low-rank tensor completion. In Conference on Neural Information Processing Systems, 2018 (formerly called Advances in Neural Information Processing Systems, 2018).
[Local copy][Publisher’s copy][Codes@Github].
Longer version: [Local copy][arXiv:1712.01193]

P. Jawanpuria and B. Mishra. A unified framework for structured low-rank matrix learning. In International Conference on Machine Learning, 2018.
[Local copy][Publisher’s copy][Matlab codes][Slides][Poster][Talk given in IIT Madras].
Longer version: [Local copy][arXiv:1704.07352].

P. Jawanpuria, Maksim Lapin, Matthias Hein and Bernt Schiele. Efficient output kernel learning for multiple tasks. In Advances in Neural Information Processing Systems, 2015.
[Local copy][Publisher’s copy][Long version][Matlab codes].

P. Jawanpuria, Manik Varma and J. Saketha Nath. On p-norm path following in multiple kernel learning for non-linear feature selection. In International Conference on Machine Learning, 2014.
[Local copy][Publisher’s copy].

P. Jawanpuria and J. Saketha Nath. A convex feature learning formulation for latent task structure discovery. In International Conference on Machine Learning, 2012.
[Local copy][Publisher’s copy][Supplementary material][Matlab codes].

P. Jawanpuria, J Saketha Nath and Ganesh Ramakrishnan. Efficient rule ensemble learning using hierarchical kernels. In International Conference on Machine Learning, 2011.
[Local copy][Publisher’s copy][Technical report].

P. Jawanpuria and J. Saketha Nath. Multi-task multiple kernel learning. In SIAM International Conference on Data Mining, 2011.
[Local copy][Matlab codes].

Workshop papers

M. Bhutani, P. Jawanpuria, H. Kasai, and B. Mishra. Low-rank geometric mean metric learning. In ICML workshop on Geometry in Machine Learning (GiMLi), 2018.
[Local copy][arXiv:1806.05454][Poster].

M. Nimishakavi, P. Jawanpuria, and B. Mishra. A dual framework for low rank tensor completion. In NIPS workshop on Synergies in Geometric Data Analysis, 2017.
[Local copy][Slides][Poster]. An extended version published in NeurIPS’18.

P. Jawanpuria and B. Mishra. A unified framework for structured low-rank matrix learning. In NIPS workshop on Optimization for Machine Learning, 2017.
[Local copy][Poster]. An extended version published in ICML’18.

Ph.D. Thesis

P. Jawanpuria. Learning kernels for multiple predictive tasks.
Ph.D. thesis, IIT Bombay, 2015 [Local copy]. The jury included Kilian Q. Weinberger, Harish Karnick, Anirban Dasgupta, Ganesh Ramakrishnan, Sunita Sarawagi, Pushpak Bhattacharya, and Saketh Nath.