Publications

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

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

Preprints

B. Mishra, H. Kasai, and P. Jawanpuria. Riemannian optimization on the simplex of positive definite matrices. Technical report, arXiv preprint, arXiv:1906.10436, 2019.
[arXiv:1906.10436].

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

P. Jawanpuria, M. Meghwanshi, and B. Mishra. Low-rank approximations of hyperbolic embeddings. Accepted to IEEE Conference on Decision and Control, 2019.
[arXiv:1903.07307][Matlab codes].

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

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 DiDi AI Lab in Los Angeles (2019) on optimization on manifolds and its application to learning multilingual word embeddings.

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].

Talks 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].