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 OpenAI (2017) and Microsoft Research, Redmond (2018). Before joining Stanford, I obtained my bachelors in Computer Science and Engineering from IIT Delhi (2015).

I am a recipient of the Microsoft Research PhD Fellowship in machine learning, the Stanford Data Science Scholarship, and the Stanford Teaching Fellowship. As a Stanford Teaching Fellow, I recently taught a new class on Deep Generative Models in 2018 with an enrollment of 150+ students.

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, stochastic optimization algorithms, and representation frameworks for probabilistic reasoning (ICLR 2019, AISTATS 2019a, 2018a,b, AAAI 2018a,b). These endeavors have often led to algorithms that bridge theory and practice for applications across machine learning, e.g., fair representation learning, constraint satisfaction problems, compressed sensing, and multiagent reinforcement learning (AISTATS 2019b, NeurIPS 2018, ICML 2018a,b).

Recent

Publications

  1. Stochastic Optimization of Sorting Networks via Continuous Relaxations
    Aditya Grover*, Eric Wang*, Aaron Zweig, Stefano Ermon
    International Conference on Learning Representations (ICLR), 2019. To appear
    [pre-camera ready][code coming soon]

  2. Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization
    Aditya Grover, Stefano Ermon
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2019. To appear
    [pre-camera ready][code coming soon]

  3. Learning Controllable Fair Representations
    Jiaming Song, Pratyusha Kalluri, Aditya Grover, Shengjia Zhao, Stefano Ermon
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2019. To appear
    [pre-camera ready][code coming soon]

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

  5. 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%)]

  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%)]

  7. 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]

  8. 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]

  9. 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]

  10. 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%)]

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

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

  13. 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%)]

  14. 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.

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

  16. 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]

  17. 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]