Optimization for and by Machine Learning

Pritish Mohapatra


In machine learning, tasks like making predictions using a model and learning model parameters can often be formulated as optimization problems. The feasibility of using a machine learning model de- pends on the efficiency with which the corresponding optimization problems can be solved. As such, the area of machine learning throws up many challenges and interesting problems for research in the field of optimization. While in some cases, it is possible to directly apply off-the-shelf optimization methods for problems in machine learning, in many other cases, it becomes necessary to develop optimization algo- rithms that are tailor-made for specific problems. On the other hand, developing optimization algorithms for specific problem domains can itself be helped by machine learning techniques. Learning optimiza- tion algorithms from data can help relieve tedious effort required to develop optimization methods for new problem domains. The challenge here is to appropriately parameterize the space of algorithms for different optimization problems. In this context, we explore the interplay between the areas of optimiza- tion and machine learning and make contributions in specific problems of interest that lie in the overlap of these fields.


Year of completion:  December 2021
 Advisor : C. V. Jawahar

Related Publications

  • Pritish Mohapatra, C. V. Jawahar and M. Pawan Kumar -  Learning to Round for Discrete Labeling Problems, Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), 2018, 09 - 11 April 2018, Playa Blanca, Lanzarote.[PDF]

  • Pritish Mohapatra, Michal Rolı́nek, C. V. Jawahar, Vladimir Kolmogorov and M. Pawan Kumar -  Efficient Optimization for Rank-based Loss Functions, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, 18 - 22 June 2018,Salt Lake City, Utah.[PDF]

  • Pritish Mohapatra, Puneet Kumar Dokania, C.V Jawahar and M. Pawan Kumar - Partial Linearization based Optimization for Multi-class SVM, Proceedings of European Conference on Computer Vision, (ECCV) – Amsterdam, The Netherlands, 2016. [PDF]

  • Aseem Behl, Pritish Mohapatra, C. V. Jawahar, M. Pawan Kumar - Optimizing Average Precision using Weakly Supervised Data IEEE Transations on Pattern Analysis and Machine Intelligence (TPAMI 2015). [PDF]

  • Mohak Sukhwani, Suriya Singh, Anirudh Goyal, Aseem Behl, Pritish Mohapatra, Brijendra Kumar Bharti, C.V. Jawahar - Monocular Vision based Road Marking Recognition for Driver Assistance and Safety Proceedings of the IEEE Conference on Vehicular Electronics and Safety,16-17 Dec 2014, Hyderabad, India. [PDF]

  • Pritish Mohapatra, C.V. Jawahar and M. Pawan Kumar - Efficient Optimization for Average Precision SVM Proceedings of the Neural Information Processing Systems Foundation,08-13 Dec 2014, Qubec, Canada. [PDF]