Aditya Grover
Ph.D. Candidate, Computer Science
Stanford Artificial Intelligence Laboratory
Statistical Machine Learning Group
Email: adityag at cs.stanford.edu
I am on the job market this academic year (2019-20).
I am a final-year Ph.D. candidate in Computer Science at Stanford University advised by Stefano Ermon. My research focusses broadly on probabilistic machine learning, including topics in generative modeling, approximate inference, and deep learning. I am particularly excited in grounding my research via applications relating to scientific discovery and sustainable development.
My research is supported by a Microsoft Research Ph.D. Fellowship, a Lieberman Fellowship, and a Data Science Scholarship. I am also a Teaching Fellow at Stanford since 2018, where I co-designed and teach a new class on Deep Generative Models. Before joining Stanford, I obtained my bachelors in Computer Science and Engineering from IIT Delhi (2015).
During my Ph.D., I have also spent wonderful summers interning at Google Brain (2019), Microsoft Research (2018), and OpenAI (2017).
I grew up in various parts of India before moving to the US. In my free time, I like to play tennis, social dance, hike, and practice card tricks.
Recent
- Fall 2019: Looking forward to again co-teaching the second offering of CS 236: Deep Generative Models. Stay tuned for updated course materials covering recent advances in the field!
- May 2019: Honored to have received the Lieberman Fellowship, recognizing "students who have demonstrated broad potential for leadership in academia through their research accomplishments, teaching and university service"!
Preprints
- Fair Generative Modeling via Weak Supervision
Aditya Grover*, Kristy Choi*, Rui Shu, Stefano Ermon
NeurIPS Workshop on Human-Centric Machine Learning, 2019.
[paper]
Publications
- AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows
Aditya Grover*, Christopher Chute*, Rui Shu, Zhangjie Cao, Stefano Ermon
AAAI Conference on Artificial Intelligence (AAAI), 2020.
[paper][code coming soon]
- Bias Correction of Learned Generative Models using Likelihood-free Importance Weighting
Aditya Grover, Jiaming Song, Alekh Agarwal, Kenneth Tran, Ashish Kapoor, Eric Horvitz, Stefano Ermon
Advances in Neural Information Processing Systems (NeurIPS), 2019.
[paper][code coming soon]
- Graphite: Iterative Generative Modeling of Graphs
Aditya Grover, Aaron Zweig, Stefano Ermon
International Conference on Machine Learning (ICML), 2019.
[paper][code]
- Neural Joint Source-Channel Coding
Kristy Choi, Kedar Tatwawadi, Aditya Grover, Tsachy Weissman, Stefano Ermon
International Conference on Machine Learning (ICML), 2019.
[paper][code]
- Stochastic Optimization of Sorting Networks via Continuous Relaxations
Aditya Grover*, Eric Wang*, Aaron Zweig, Stefano Ermon
International Conference on Learning Representations (ICLR), 2019.
[paper][code]
- Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization
Aditya Grover, Stefano Ermon
International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.
[paper][code][blog]
- Learning Controllable Fair Representations
Jiaming Song, Pratyusha Kalluri, Aditya Grover, Shengjia Zhao, Stefano Ermon
International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.
[paper][ code ]
- Streamlining Variational Inference for Constraint Satisfaction Problems
Aditya Grover, Tudor Achim, Stefano Ermon
Advances in Neural Information Processing Systems (NeurIPS), 2018.
[paper][code]
- Learning Policy Representations in Multiagent Systems
Aditya Grover, Maruan Al-Shedivat, Jayesh K. Gupta, Yura Burda, Harrison Edwards
International Conference on Machine Learning (ICML), 2018.
[paper]
Full Oral Presentation [acceptance rate: 212/2473 (8.6%)]
- Modeling Sparse Deviations for Compressed Sensing using Generative Models
Manik Dhar, Aditya Grover, Stefano Ermon
International Conference on Machine Learning (ICML), 2018.
[paper] [code]
Full Oral Presentation [acceptance rate: 212/2473 (8.6%)]
- Evaluating Generalization in Multiagent Systems using Agent-Interaction Graphs (short)
Aditya Grover, Maruan Al-Shedivat, Jayesh K. Gupta, Yura Burda, Harrison Edwards
International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2018.
[paper]
- Variational Rejection Sampling
Aditya Grover*, Ramki Gummadi*, Miguel Lazaro-Gredilla, Dale Schuurmans, Stefano Ermon
International Conference on Artificial Intelligence and Statistics (AISTATS), 2018.
[paper][blog]
- Best arm identification in multi-armed bandits with delayed feedback
Aditya Grover, Todor Markov, Peter Attia, Norman Jin, Nicholas Perkins, Bryan Cheong, Michael Chen, Zi Yang, Stephen Harris, William Chueh, Stefano Ermon
International Conference on Artificial Intelligence and Statistics (AISTATS), 2018.
[paper][code]
- Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models
Aditya Grover, Manik Dhar, Stefano Ermon
AAAI Conference on Artificial Intelligence (AAAI), 2018.
[paper][code][blog]
Full Oral Presentation [acceptance rate: 417/3800 (10.9%)]
- Boosted Generative Models
Aditya Grover, Stefano Ermon
AAAI Conference on Artificial Intelligence (AAAI), 2018.
[paper][code]
- Variational Bayes on Monte Carlo Steroids
Aditya Grover, Stefano Ermon
Advances in Neural Information Processing Systems (NIPS), 2016.
[paper][spotlight video]
- node2vec: Scalable Feature Learning for Networks
Aditya Grover, Jure Leskovec
Knowledge Discovery and Data Mining (KDD), 2016.
[paper][code]
Oral Plenary Presentation [acceptance rate: 70/784 (8.9%)]
- Contextual Symmetries in Probabilistic Graphical Models
Ankit Anand, Aditya Grover, Mausam, Parag Singla
International Joint Conference on Artificial Intelligence (IJCAI), 2016.
[paper][code]
Best Paper Award at the International Workshop on Statistical Relational AI (StarAI), 2016.
- A Deep Hybrid Model for Weather Forecasting
Aditya Grover, Ashish Kapoor, Eric Horvitz
Knowledge Discovery and Data Mining (KDD), 2015.
[paper][press Microsoft , Gizmodo]
- ASAP-UCT: Abstraction of State-Action Pairs in UCT
Ankit Anand, Aditya Grover, Mausam, Parag Singla
International Joint Conference on Artificial Intelligence (IJCAI), 2015.
[paper][code]
- A Novel Abstraction Framework for Online Planning (short)
Ankit Anand, Aditya Grover, Mausam, Parag Singla
International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2015.
[paper]
[* denotes equal contribution]
Teaching
Service
- Journal Reviewer: Nature, Journal of Machine Learning Research, Machine Learning Journal, Transactions on Knowledge Discovery from Data, Transactions on Networking
- Conference Program Committee: AAAI (2020, 2019), KDD (2019), UAI (2019)
- Conference Reviewer: AISTATS (2020), ICML (2019), ICLR (2020, 2019), NeurIPS (2019, 2018, 2016)
- Student Representative in the Committee on Research within the Stanford University Academic Council (2016-17)
Outreach