Publications & Competitions

1. “On the Separability of Classes with the Cross-Entropy Loss Function” - Rudrajit Das and Subhasis Chaudhuri.

   Pre-print. Download paper here.

2. “Extremal Eigenvalue Analysis of the Hessian and a Learning Rate Choice for Stochastic Gradient Descent” - Rudrajit Das and Subhasis Chaudhuri.

   Pre-print. Manuscript available on request.

3. “On the Convergence of a Biased Version of Stochastic Gradient Descent” - Rudrajit Das, Jiong Zhang and Inderjit Dhillon.

   Accepted for poster presentation in “Beyond First Order Methods in ML” workshop in NeurIPS 2019.

4. “Nonlinear Blind Compressed Sensing under Signal-Dependent Noise” - Rudrajit Das and Ajit Rajwade.

   Accepted for presentation in IEEE International Conference on Image Processing (ICIP) 2019. Download paper here.

5. “Sparse Kernel PCA for Outlier Detection” - Rudrajit Das, Aditya Golatkar and Suyash Awate.

   Accepted for oral presentation in IEEE International Conference on Machine Learning and Applications (ICMLA) 2018. Download paper here.

6. iFood Challenge, FGVC Workshop, CVPR 2018 - Parth Kothari^, Arka Sadhu^, Aditya Golatkar^, Rudrajit Das^ (^ denotes equal contribution).

   Finished $2^{nd}$ in the public leaderboard and $3^{rd}$ in the private leaderboard (Team name : Invincibles). Leaderboard Link. Invited to present our method at CVPR 2018 (slides can be found here).

7. “Some Probabilistically Provable Theoretical Aspects of Neural Networks and Algorithmic Aspects of Large-Scale Optimization” - Bachelor’s & Master’s Thesis. Awarded the Undergraduate Research Award (URA-03) for exceptional work in thesis. Download here.