Aditya Grover
Ph.D. Candidate, Computer Science
Stanford Artificial Intelligence Laboratory
Statistical Machine Learning Group
Email: adityag at cs.stanford.edu



Bio

I am a fourth-year Ph.D. candidate in Computer Science at Stanford University, where I am advised by Stefano Ermon and affiliated with the Stanford Artificial Intelligence Laboratory and the Statistical Machine Learning Group. My research focusses on various aspects of machine learning, including probabilistic modeling, stochastic optimization, and deep learning. Previously, I spent two summers interning at Microsoft Research, Redmond (2018) and OpenAI (2017). Before joining Stanford, I obtained my bachelors in Computer Science and Engineering from IIT Delhi (2015).

Research

I am interested in developing algorithms for efficient learning and inference in probabilistic models. A large part of my research in this direction entails the design and analysis of suitable learning objectives and stochastic optimization algorithms at the intersection of probabilistic reasoning and representation learning. These endeavors have often led to algorithms that bridge theory and practice for applications across machine learning, e.g., fairness, constraint satisfaction problems, compressed sensing, and multiagent reinforcement learning.

My research is supported by a Microsoft Research PhD Fellowship in machine learning, a Stanford Data Science Scholarship, and a Lieberman Fellowship. I am also Teaching Fellow at Stanford, where I designed and taught a new class on Deep Generative Models in Fall 2018.

Recent

Preprints

  1. 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
    [paper]

  2. AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows
    Aditya Grover*, Christopher Chute*, Rui Shu, Zhangjie Cao, Stefano Ermon
    [paper]

Publications

  1. Graphite: Iterative Generative Modeling of Graphs
    Aditya Grover, Aaron Zweig, Stefano Ermon
    International Conference on Machine Learning (ICML), 2019.
    [paper][code]

  2. Neural Joint Source-Channel Coding
    Kristy Choi, Kedar Tatwawadi, Aditya Grover, Tsachy Weissman, Stefano Ermon
    International Conference on Machine Learning (ICML), 2019.
    [paper][code]

  3. 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]

  4. 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]

  5. 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 ]

  6. Streamlining Variational Inference for Constraint Satisfaction Problems
    Aditya Grover, Tudor Achim, Stefano Ermon
    Neural Information Processing Systems (NeurIPS), 2018.
    [paper][code]

  7. 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%)]

  8. 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%)]

  9. 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]

  10. 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]

  11. 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]

  12. 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%)]

  13. Boosted Generative Models
    Aditya Grover, Stefano Ermon
    AAAI Conference on Artificial Intelligence (AAAI), 2018.
    [paper][code]

  14. Variational Bayes on Monte Carlo Steroids
    Aditya Grover, Stefano Ermon
    Neural Information Processing Systems (NIPS), 2016.
    [paper][spotlight video]

  15. 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%)]

  16. 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.

  17. A Deep Hybrid Model for Weather Forecasting
    Aditya Grover, Ashish Kapoor, Eric Horvitz
    Knowledge Discovery and Data Mining (KDD), 2015.
    [paper][press Microsoft , Gizmodo]

  18. 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]

  19. 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]