- Dec 2020: honored to get the Best Student Paper Award at IEEE Big Data 2020.
- Nov 2020: won the personalized question selection task in NeurIPS 2020 Education Challenge. Implementation is publicly available.
- Nov 2020: paper on robust sample reweighting strategy without gold samples to appear at WACV 2021. Paper/codes will be released soon.
- Nov 2020: paper on optimal career trajectory modeling to appear at IEEE Big Data 2020. Paper/codes will be released soon.
- Sep 2020: will be serving as Program committee member for AAAI 2021 and WACV 2021 .
- Aug 2020: selected for KDD 2020 Student Award!
- May 2020: paper on knowledge tracing to appear at KDD 2020. Paper/codes/data will be released soon.
- Mar 2020: selected for SIAM SDM Student Travel Award.
- Dec 2019: paper on optimal bidding strategy to appear at SIAM SDM 2020.
- Jun 2019: paper on bid landscape forecasting to appear at ECML-PKDD 2019.
- May 2019: paper on point processes to appear at ICML Time series workshop 2019.
- May 2018: will be joining Adobe Research, San Jose for internship (again).
- Sep 2018: had a great summer at Adobe Research, San Jose.
- Aug 2017: last day at Microsoft!
- Jul 2017: will be joining UMass Amherst for PhD in Computer Science.
- Feb 2017: paper on robust loss functions for deep networks to appear at AAAI 2017.
- Jan 2017: paper on robustness of decision trees to appear at PAKDD 2017.
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 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.