About me

I am a Ph.D. student in Computer science at UMass Amherst, advised by Professor Andrew Lan. Previously, I was a Software Engineer at Microsoft, Bangalore; before that, I completed my Master in Electrical Engineering at the Indian Institute of Science, Bangalore (IISc), advised by Professor P.S. Sastry.

I have a broad interest in Machine/Deep learning, Graphical models, and Reinforcement learning. My Ph.D. research mainly focuses on learning sequential models from large-scale (unlabeled) data. Besides, I am also interested in more theoretical questions from a learning perspective, such as robust learning under label noise.

Research Interest

• Sequential Generative Models, Variational Inference, Inverse Reinforcement Learning
• AI for Social Goods, Computational Advertising

Updates

Publications

• Do We Really Need Gold Samples for Sample Weighting under Label Noise?

  Aritra Ghosh, and Andrew Lan.

  Proceedings of the 2021 IEEE Winter Conference on Applications of Computer Vision, 2021 (WACV).

  Code

• Skill-based Career Path Modeling and Recommendation

  Aritra Ghosh, Beverly Woolf, Shlomo Zilberstein, and Andrew Lan.

  Proceedings of the 2020 IEEE International Conference on Big Data (Big Data), 2020 (IEEE BigData).

  Best Student Paper Award Code

• Context-Aware Attentive Knowledge Tracing

  Aritra Ghosh, Neil Heffernan, and Andrew S Lan.

  Proceedings of the 2020 ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020 (KDD).

  Code

• Optimal Bidding Strategy without Exploration in Real-time Bidding

  Aritra Ghosh, Saayan Mitra, Somdeb Sarkhel, and Viswanathan Swaminathan.

  Proceedings of the 2020 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2020 (SIAM SDM).

• Scalable Bid Landscape Forecasting in Real-time Bidding

  Aritra Ghosh, Saayan Mitra, Somdeb Sarkhel, Jason Xie, Gang Wu, and Viswanathan Swaminathan.

  Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, Cham, 2019 (ECML-PKDD).

• Generative Sequential Stochastic Model for Marked Point Processes

  Abhishek Sharma, Aritra Ghosh, and Madalina Fiterau.

  ICML Time Series Workshop. 2019.

• Robust Loss Functions for Deep Neural Networks

  Aritra Ghosh, Himanshu Kumar, and P. S. Sastry.

  Thirty-First AAAI Conference on Artificial Intelligence. 2017 (AAAI).

• On the Robustness of Decision Tree Learning Under Label Noise

  Aritra Ghosh, Naresh Manwani, and P. S. Sastry.

  Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, Cham, 2017 (PAKDD).

• A Preference Approach to Reputation in Sponsored Search

  Aritra Ghosh, Dinesh Gaurav, and Rahul Agrawal.

  Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. ACM, 2016 (CIKM).

• Making Risk Minimization Tolerant to Label Noise

  Aritra Ghosh, Naresh Manwani, and P. S. Sastry.

  Neurocomputing 160 (2015): 93-107.