Aditya Grover

I am an assistant professor of computer science at UCLA. My research is centered around machine learning with limited supervision, and am currently focussing on probabilistic 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

Journal and Conference Publications

• CyCLIP: Cyclic Contrastive Language-Image Pretraining
  Shashank Goel*, Hritik Bansal*, Sumit Bhatia, Ryan A. Rossi, Vishwa Vinay, Aditya Grover
  Advances in Neural Information Processing Systems (NeurIPS), 2022.
  [paper][code]


• Masked Autoencoding for Scalable and Generalizable Decision Making
  Fangchen Liu, Hao Liu, Aditya Grover, Pieter Abbeel
  Advances in Neural Information Processing Systems (NeurIPS), 2022.
  [paper and code coming soon]


• Online Decision Transformer
  Qinqing Zheng, Amy Zhang, Aditya Grover
  International Conference on Machine Learning (ICML), 2022.
  [paper][code]
  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][code]


• 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]
  


• 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]

Teaching

• Fall 2022: Instructor for CS 269: AI and Climate Change at UCLA
  
• Spring 2022: Instructor for CS M146: Introduction to Machine Learning at UCLA
  
• Winter 2022: Instructor for CS 269: Deep Generative Models at UCLA
  
• Fall 2019: Instructor for CS 236: Deep Generative Models at Stanford
  
• Fall 2018: Instructor for CS 236: Deep Generative Models at Stanford
  
• Winter 2017: Head Teaching Assistant for CS 228: Probabilistic Graphical Models at Stanford
  
• Spring 2015: Teaching Assistant for CSL 333: Artificial Intelligence at IIT Delhi