publications

see Google Scholar for the most updated information.

2022

  1. Controllable Generative Modeling via Causal Reasoning
    Joey Bose, Ricardo Pio Monti, and Aditya Grover
    Transactions of Machine Learning Research, 2022
    PDF
  2. Masked Autoencoding for Scalable and Generalizable Decision Making
    Fangchen Liu, Hao Liu, Aditya Grover, and Pieter Abbeel
    In Advances in Neural Information Processing Systems (NeurIPS), 2022
    PDF
  3. CyCLIP: Cyclic Contrastive Language-Image Pretraining
    Shashank Goel, Hritik Bansal, Sumit Bhatia, Ryan A Rossi, Vishwa Vinay, and Aditya Grover
    In Advances in Neural Information Processing Systems (NeurIPS), 2022
    PDF
  4. Transformer neural processes: Uncertainty-aware meta learning via sequence modeling
    Tung Nguyen, and Aditya Grover
    In International Conference on Machine Learning (ICML), 2022
    PDF
  5. Matching normalizing flows and probability paths on manifolds
    Heli Ben-Hamu, Samuel Cohen, Joey Bose, Brandon Amos, Aditya Grover, Maximilian Nickel, Ricky Chen, and Yaron Lipman
    In International Conference on Machine Learning (ICML), 2022
    PDF
  6. Pretrained transformers as universal computation engines
    Kevin Lu, Aditya Grover, Pieter Abbeel, and Igor Mordatch
    In AAAI Conference on Artificial Intelligence, 2022
    PDF
  7. It Takes Four to Tango: Multiagent Selfplay for Automatic Curriculum Generation
    Yuqing Du, Pieter Abbeel, and Aditya Grover
    In International Conference on Learning Representations (ICLR), 2022
    PDF
  8. Frame averaging for invariant and equivariant network design
    Omri Puny, Matan Atzmon, Heli Ben-Hamu, Edward J Smith, Ishan Misra, Aditya Grover, and Yaron Lipman
    In International Conference on Learning Representations (ICLR), 2022
    PDF

2021

  1. Frame averaging for invariant and equivariant network design
    Omri Puny, Matan Atzmon, Heli Ben-Hamu, Edward J Smith, Ishan Misra, Aditya Grover, and Yaron Lipman
    In International Conference on Learning Representations (ICLR), 2021
    PDF
  2. Bayesian learning for rapid prediction of lithium-ion battery-cycling protocols
    Benben Jiang, William E Gent, Fabian Mohr, Supratim Das, Marc D Berliner, Michael Forsuelo, Hongbo Zhao, Peter M Attia, Aditya Grover, Patrick K Herring, and  others
    Joule, 2021
    PDF
  3. BCD nets: Scalable variational approaches for bayesian causal discovery
    Chris Cundy, Aditya Grover, and Stefano Ermon
    Advances in Neural Information Processing Systems (NeurIPS), 2021
    PDF
  4. Pirank: Scalable learning to rank via differentiable sorting
    Robin Swezey, Aditya Grover, Bruno Charron, and Stefano Ermon
    Advances in Neural Information Processing Systems (NeurIPS), 2021
    PDF
  5. Decision transformer: Reinforcement learning via sequence modeling
    Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Misha Laskin, Pieter Abbeel, Aravind Srinivas, and Igor Mordatch
    Advances in Neural Information Processing Systems (NeurIPS), 2021
    PDF
  6. Moser flow: Divergence-based generative modeling on manifolds
    Noam Rozen, Aditya Grover, Maximilian Nickel, and Yaron Lipman
    Advances in Neural Information Processing Systems (NeurIPS), 2021
    PDF
  7. Learning from an Exploring Demonstrator: Optimal Reward Estimation for Bandits
    Wenshuo Guo, Kumar Krishna Agrawal, Aditya Grover, Vidya Muthukumar, and Ashwin Pananjady
    In International Conference on Artificial Intelligence and Statistics (AISTATS), 2021
    PDF
  8. Anytime sampling for autoregressive models via ordered autoencoding
    Yilun Xu, Yang Song, Sahaj Garg, Linyuan Gong, Rui Shu, Aditya Grover, and Stefano Ermon
    2021
    PDF
  9. Reset-free lifelong learning with skill-space planning
    Kevin Lu, Aditya Grover, Pieter Abbeel, and Igor Mordatch
    2021
    PDF

2020

  1. Fair Generative Modeling via Weak Supervision
    Kristy Choi, Aditya Grover, Trisha Singh, Rui Shu, and Stefano Ermon
    In International Conference on Machine Learning (ICML), 2020
    PDF
  2. Closed-loop optimization of extreme fast charging for batteries using machine learning
    Peter Attia, Aditya Grover, Norman Jin, Kristen Severson, Bryan Cheong, Jerry Liao, Michael H Chen, Nicholas Perkins, Zi Yang, Patrick Herring, Muratahan Aykol, Stephen Harris, Richard Braatz, Stefano Ermon, and William Chueh
    Nature, 2020
    PDF
  3. AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows
    Aditya Grover, Christopher Chute, Rui Shu, Zhangjie Cao, and Stefano Ermon
    In AAAI Conference on Artificial Intelligence, 2020
    PDF
  4. Permutation Invariant Graph Generation via Score-Based Generative Modeling
    Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, and Stefano Ermon
    In International Conference on Artificial Intelligence and Statistics (AISTATS), 2020
    PDF

2019

  1. Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting
    Aditya Grover, Jiaming Song, Alekh Agarwal, Kenneth Tran, Ashish Kapoor, Eric Horvitz, and Stefano Ermon
    In Advances in Neural Information Processing Systems (NeurIPS), 2019
  2. Graphite: Iterative generative modeling of graphs
    Aditya Grover, Aaron Zweig, and Stefano Ermon
    In International Conference on Machine Learning (ICML), 2019
  3. Neural Joint Source-Channel Coding
    Kristy Choi, Kedar Tatwawadi, Aditya Grover, Tsachy Weissman, and Stefano Ermon
    In International Conference on Machine Learning (ICML), 2019
  4. Stochastic Optimization of Sorting Networks via Continuous Relaxations
    Aditya Grover, Eric Wang, Aaron Zweig, and Stefano Ermon
    In International Conference on Learning Representations (ICLR), 2019
  5. Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization
    Aditya Grover, and Stefano Ermon
    In International Conference on Artificial Intelligence and Statistics (AISTATS), 2019
  6. Learning Controllable Fair Representations
    Jiaming Song, Pratyusha Kalluri, Aditya Grover, Shengjia Zhao, and Stefano Ermon
    In International Conference on Artificial Intelligence and Statistics (AISTATS), 2019

2018

  1. Streamlining variational inference for constraint satisfaction problems
    Aditya Grover, Tudor Achim, and Stefano Ermon
    In Advances in Neural Information Processing Systems (NeurIPS), 2018
  2. Learning Policy Representations in Multiagent Systems
    Aditya Grover, Maruan Al-Shedivat, Jayesh K Gupta, Yura Burda, and Harrison Edwards
    In International Conference on Machine Learning (ICML), 2018
  3. Modeling sparse deviations for compressed sensing using generative models
    Manik Dhar, Aditya Grover, and Stefano Ermon
    In International Conference on Machine Learning (ICML), 2018
  4. Variational Rejection Sampling
    Aditya Grover, Ramki Gummadi, Miguel Lazaro-Gredilla, Dale Schuurmans, and Stefano Ermon
    In International Conference on Artificial Intelligence and Statistics (AISTATS), 2018
  5. 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, and Stefano Ermon
    In International Conference on Artificial Intelligence and Statistics (AISTATS), 2018
  6. Boosted generative models
    Aditya Grover, and Stefano Ermon
    In AAAI Conference on Artificial Intelligence, 2018
  7. Flow-GAN: Combining maximum likelihood and adversarial learning in generative models
    Aditya Grover, Manik Dhar, and Stefano Ermon
    In AAAI Conference on Artificial Intelligence, 2018
  8. Evaluating Generalization in Multiagent Systems using Agent-Interaction Graphs
    Aditya Grover, Maruan Al-Shedivat, Jayesh K Gupta, Yuri Burda, and Harrison Edwards
    In International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2018

2016

  1. Variational Bayes on Monte Carlo Steroids
    Aditya Grover, and Stefano Ermon
    In Advances in Neural Information Processing Systems (NeurIPS), 2016
  2. node2vec: Scalable Feature Learning for Networks
    Aditya Grover, and Jure Leskovec
    In International Conference on Knowledge Discovery and Data Mining (KDD), 2016
  3. Contextual Symmetries in Probabilistic Graphical Models
    Ankit Anand, Aditya Grover,  Mausam, and Parag Singla
    In International Joint Conference on Artificial Intelligence (IJCAI), 2016

2015

  1. A deep hybrid model for weather forecasting
    Aditya Grover, Ashish Kapoor, and Eric Horvitz
    In International Conference on Knowledge Discovery and Data Mining (KDD), 2015
  2. ASAP-UCT: abstraction of state-action pairs in UCT
    Ankit Anand, Aditya Grover,  Mausam, and Parag Singla
    In International Joint Conference on Artificial Intelligence (IJCAI), 2015
  3. A Novel Abstraction Framework for Online Planning
    Ankit Anand, Aditya Grover,  Mausam, and Parag Singla
    In International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2015