Publications

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

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

Preprints

P. Jawanpuria, M. Meghwanshi, and B. Mishra. Low-rank approximations of hyperbolic embeddings. Technical report, arXiv preprint, arXiv:1903.07307, 2019.
[arXiv:1903.07307][Matlab codes].

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), 7:107-120, 2019.
[Local copy][Publisher’s copy][Codes@Github].

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

H. Kasai, P. Jawanpuria, and B. Mishra. Riemannian adaptive stochastic gradient algorithms on matrix manifolds. In International Conference on Machine Learning, 2019.
[arXiv:1902.01144][Matlab codes].

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].
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.

Presentations

Talk at IIIT Hyderabad (2019) on low-rank matrix learning from machine learning a optimization perspective.

In ICML 2018, presented our work “A unified framework for structured low-rank matrix learning” [Slides].

Talk at IIT Madras and IIT Bombay (2018) on large-scale structured low-rank matrix learning [IITM slides][IITB slides].

In ICML 2014, presented our work “On p-norm path following in multiple kernel learning for non-linear feature selection” [Slides].

In ICML 2012, presented our work “A convex feature learning formulation for latent task structure discovery” [Slides].