Aditya Grover
I am an assistant professor of computer science at UCLA. My research is centered around probabilistic approaches for machine learning with limited supervision, with a current focus on generative modeling and sequential decision making under uncertainty. I am also an affiliate faculty in the Institute of the Environment and Sustainability at UCLA, where I ground my foundations research in applications at the intersection of physical sciences and climate change.
Before joining UCLA, I was a research scientist in the Core ML team at FAIR in Meta. Previously, I completed my postdoctoral training at UC Berkeley (2021; advisor: Pieter Abbeel), PhD at Stanford University (2020; advisor: Stefano Ermon) and bachelors at IIT Delhi (2015; co-advisors: Mausam, Parag Singla), all in computer science. During my PhD, I spent wonderful summers interning at Google Brain, Microsoft Research, and OpenAI.
Contact: adityag at cs dot ucla dot edu
Selected Awards
- Outstanding Paper Award at NeurIPS (2021) [press]
- Adobe Data Science Research Award (2021) [press]
- ACM SIGKDD Doctoral Dissertation Award (2021) [press 1, 2]
Given to 1 dissertation globally for outstanding work in data science and machine learning.
- Gores Award (2020) [press 1, 2]
Stanford's highest award for teaching excellence. Given to 1 student and 2 faculty in the entire university.
- Google-Simons Research Fellowship (2020) [press]
- Centennial Award (2020) [press]
- Lieberman Fellowship (2019-20) [press]
- Stanford Data Science Scholarship (2018-20) [press]
- Microsoft Research Ph.D. Fellowship (2017-19) [press]
- Best Paper Award at the International Workshop on Statistical Relational AI held at IJCAI (2016) [press]
- Best Undergraduate Thesis Award at IIT Delhi (2015)
Ph.D. Thesis
Learning to Represent and Reason under Limited Supervision
Advisor: Stefano Ermon
Committee: Stephen Boyd (chair), Moses Charikar, Eric Horvitz, Jure Leskovec, Percy Liang
[thesis][press 1, 2]
ACM SIGKDD Doctoral Dissertation Award, 2021.
Journal and Conference Publications
- Online Decision Transformer
Qinqing Zheng, Amy Zhang, Aditya Grover
International Conference on Machine Learning (ICML), 2022.
[paper]
Long Oral Presentation
- Transformer Neural Process: Uncertainty-Aware Meta Learning Via Sequence Modeling
Tung Nguyen, Aditya Grover
International Conference on Machine Learning (ICML), 2022.
[paper coming soon]
- Matching Normalizing Flows and Probability Paths on Manifolds
Heli Ben-Hamu, Samuel Cohen, Joey Bose, Brandon Amos, Aditya Grover, Maximilian Nickel, Ricky Chen, Yaron Lipman
International Conference on Machine Learning (ICML), 2022.
[paper coming soon]
- It Takes Four to Tango: Multiagent Self Play for Automatic Curriculum Generation
Yuqing Du, Pieter Abbeel, Aditya Grover
International Conference on Learning Representations (ICLR), 2022.
[paper] [code]
- Frame Averaging for Invariant and Equivariant Network Design
Omri Puny, Matan Atzmon, Heli Ben-Hamu, Edward J. Smith, Ishan Misra, Aditya Grover, Yaron Lipman
International Conference on Learning Representations (ICLR), 2022.
[paper]
Oral Presentation
- Pretrained Transformers as Universal Computation Engines
Kevin Lu, Aditya Grover, Pieter Abbeel, Igor Mordatch
AAAI Conference on Artificial Intelligence (AAAI), 2022.
[paper][code][blog][press][video (courtesy: Yannic Kilcher)]
- Learning from an Exploring Demonstrator: Optimal Reward Estimation for Bandits
Wenshuo Guo, Kumar Krishna Agrawal, Aditya Grover, Vidya Muthukumar, Ashwin Pananjady
International Conference on Artificial Intelligence and Statistics (AISTATS), 2022.
[paper][code]
- Moser Flow: Divergence-based Generative Modeling on Manifolds
Noam Rozen, Aditya Grover, Maximilian Nickel, Yaron Lipman
Advances in Neural Information Processing Systems (NeurIPS), 2021.
[paper][press]
Outstanding Paper Award
- Decision Transformer: Reinforcement Learning via Sequence Modeling
Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch
Advances in Neural Information Processing Systems (NeurIPS), 2021.
[paper][code][video (courtesy: Yannic Kilcher)]
- BCD Nets: Scalable Variational Approaches to Bayesian Causal Discovery
Chris Cundy, Aditya Grover, Stefano Ermon
Advances in Neural Information Processing Systems (NeurIPS), 2021.
[paper][code]
- PiRank: Learning To Rank via Differentiable Sorting
Robin Swezey, Aditya Grover, Bruno Charron, Stefano Ermon
Advances in Neural Information Processing Systems (NeurIPS), 2021.
[paper][code]
- Bayesian learning for rapid prediction of lithium-ion battery-cycling protocols
Benben Jiang, William Gent, Fabian Mohr, Supratim Das, Marc Berliner, Michael Forsuelo, Hongbo Zhao, Peter Attia, Aditya Grover , Patrick Herring, Martin Bazant, Stephen Harris, Stefano Ermon, William Chueh, Richard Braatz
Joule, Cell press, 2021.
[paper]
- Reset-Free Lifelong Learning with Skill-Space Planning
Kevin Lu, Aditya Grover, Pieter Abbeel, Igor Mordatch
International Conference on Learning Representations (ICLR), 2021.
[paper][code][website]
- Anytime Sampling for Autoregressive Models via Ordered Autoencoding
Yilun Xu, Yang Song, Sahaj Garg, Linyuan Gong, Rui Shu, Aditya Grover, Stefano Ermon
International Conference on Learning Representations (ICLR), 2021.
[paper][code]
- Closed-loop optimization of fast-charging protocols for batteries with machine learning
Peter Attia*, Aditya Grover*, Norman Jin, Kristen Severson, Todor Markov, Yang-Hung Liao, Michael Chen, Bryan Cheong, Nicholas Perkins, Zi Yang, Patrick Herring, Muratahan Aykol, Stephen Harris, Richard Braatz, Stefano Ermon, William Chueh
Nature, 2020.
[paper][code][press release][selected media]
- Fair Generative Modeling via Weak Supervision
Kristy Choi*, Aditya Grover*, Trisha Singh, Rui Shu, Stefano Ermon
International Conference on Machine Learning (ICML), 2020.
[paper][code][press]
- Permutation Invariant Graph Generation via Score-Based Generative Modeling
Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, Stefano Ermon
International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.
[paper][code]
- 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]
- 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][blog]
- Graphite: Iterative Generative Modeling of Graphs
Aditya Grover, Aaron Zweig, 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][blog]
- Neural Joint Source-Channel Coding
Kristy Choi, Kedar Tatwawadi, Aditya Grover, Tsachy Weissman, Stefano Ermon
International Conference on Machine Learning (ICML), 2019.
[paper][code]
Full Oral Presentation
- Streamlining Variational Inference for Constraint Satisfaction Problems
Aditya Grover, Tudor Achim, Stefano Ermon
Advances in Neural Information Processing Systems (NeurIPS), 2018.
Extended version appears as invited paper in the special issue of Journal of Statistical Mechanics: Theory and Experiment on Machine Learning, 2019.
[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]
Preprints
- JUMBO: Scalable Multi-task Bayesian Optimization using Offline Data
Kourosh Hakhamaneshi, Pieter Abbeel, Vladimir Stojanovic, Aditya Grover
[paper][code]
- Rotation Invariant Graph Neural Networks using Spin Convolutions
Muhammed Shuaibi, Adeesh Kolluru, Abhishek Das, Aditya Grover, Anuroop Sriram, Zachary Ulissi, C. Lawrence Zitnick
[paper]
[* denotes equal contribution]
Teaching
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