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ORCIDAditya Grover
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2020 – today
- 2021
[i20]Yilun Xu, Yang Song, Sahaj Garg, Linyuan Gong, Rui Shu, Aditya Grover, Stefano Ermon:
Anytime Sampling for Autoregressive Models via Ordered Autoencoding. CoRR abs/2102.11495 (2021)- 2020
- [j1]Peter M. Attia
, Aditya Grover, Norman Jin, Kristen A. Severson, Todor M. Markov, Yang-Hung Liao, Michael H. Chen, Bryan Cheong, Nicholas Perkins, Zi Yang, Patrick K. Herring, Muratahan Aykol, Stephen J. Harris, Richard D. Braatz, Stefano Ermon, William C. Chueh:
Closed-loop optimization of fast-charging protocols for batteries with machine learning. Nat. 578(7795): 397-402 (2020) - [c26]Aditya Grover, Christopher Chute, Rui Shu, Zhangjie Cao, Stefano Ermon:
AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows. AAAI 2020: 4028-4035 - [c25]Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, Stefano Ermon:
Permutation Invariant Graph Generation via Score-Based Generative Modeling. AISTATS 2020: 4474-4484 - [c24]Kristy Choi, Aditya Grover, Trisha Singh, Rui Shu, Stefano Ermon:
Fair Generative Modeling via Weak Supervision. ICML 2020: 1887-1898 - [i19]Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, Stefano Ermon:
Permutation Invariant Graph Generation via Score-Based Generative Modeling. CoRR abs/2003.00638 (2020) - [i18]Kevin Lu, Aditya Grover, Pieter Abbeel, Igor Mordatch:
Reset-Free Lifelong Learning with Skill-Space Planning. CoRR abs/2012.03548 (2020) - [i17]Robin M. E. Swezey, Aditya Grover, Bruno Charron, Stefano Ermon:
PiRank: Learning To Rank via Differentiable Sorting. CoRR abs/2012.06731 (2020)
2010 – 2019
- 2019
- [c23]Jiaming Song, Pratyusha Kalluri, Aditya Grover, Shengjia Zhao, Stefano Ermon:
Learning Controllable Fair Representations. AISTATS 2019: 2164-2173 - [c22]Aditya Grover, Stefano Ermon:
Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization. AISTATS 2019: 2514-2524 - [c21]Aditya Grover, Christopher Chute, Rui Shu, Zhangjie Cao, Stefano Ermon:
AlignFlow: Learning from multiple domains via normalizing flows. DGS@ICLR 2019 - [c20]Aditya Grover, Jiaming Song, Ashish Kapoor, Kenneth Tran, Alekh Agarwal, Eric Horvitz, Stefano Ermon:
Bias Correction of Learned Generative Models via Likelihood-free Importance Weighting. DGS@ICLR 2019 - [c19]Aditya Grover, Eric Wang, Aaron Zweig, Stefano Ermon:
Stochastic Optimization of Sorting Networks via Continuous Relaxations. ICLR (Poster) 2019 - [c18]Kristy Choi, Kedar Tatwawadi, Aditya Grover, Tsachy Weissman, Stefano Ermon:
Neural Joint Source-Channel Coding. ICML 2019: 1182-1192 - [c17]Aditya Grover, Aaron Zweig, Stefano Ermon:
Graphite: Iterative Generative Modeling of Graphs. ICML 2019: 2434-2444 - [c16]Aditya Grover, Jiaming Song, Ashish Kapoor, Kenneth Tran, Alekh Agarwal, Eric Horvitz, Stefano Ermon:
Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting. NeurIPS 2019: 11056-11068 - [i16]Aditya Grover, Eric Wang, Aaron Zweig, Stefano Ermon:
Stochastic Optimization of Sorting Networks via Continuous Relaxations. CoRR abs/1903.08850 (2019) - [i15]Aditya Grover, Christopher Chute, Rui Shu, Zhangjie Cao, Stefano Ermon:
AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows. CoRR abs/1905.12892 (2019) - [i14]Aditya Grover, Jiaming Song, Alekh Agarwal, Kenneth Tran, Ashish Kapoor, Eric Horvitz, Stefano Ermon:
Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting. CoRR abs/1906.09531 (2019) - [i13]Aditya Grover, Kristy Choi, Rui Shu, Stefano Ermon:
Fair Generative Modeling via Weak Supervision. CoRR abs/1910.12008 (2019) - 2018
- [c15]Aditya Grover, Manik Dhar, Stefano Ermon:
Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models. AAAI 2018: 3069-3076 - [c14]Aditya Grover, Stefano Ermon:
Boosted Generative Models. AAAI 2018: 3077-3084 - [c13]Aditya Grover, Ramki Gummadi, Miguel Lázaro-Gredilla, Dale Schuurmans, Stefano Ermon:
Variational Rejection Sampling. AISTATS 2018: 823-832 - [c12]Aditya Grover, Todor M. Markov, Peter M. Attia, Norman Jin, Nicolas Perkins, Bryan Cheong, Michael H. Chen, Zi Yang, Stephen J. Harris, William C. Chueh, Stefano Ermon:
Best arm identification in multi-armed bandits with delayed feedback. AISTATS 2018: 833-842 - [c11]Aditya Grover, Maruan Al-Shedivat, Jayesh K. Gupta, Yuri Burda, Harrison Edwards:
Evaluating Generalization in Multiagent Systems using Agent-Interaction Graphs. AAMAS 2018: 1944-1946 - [c10]Manik Dhar, Aditya Grover, Stefano Ermon:
Modeling Sparse Deviations for Compressed Sensing using Generative Models. ICML 2018: 1222-1231 - [c9]Aditya Grover, Maruan Al-Shedivat, Jayesh K. Gupta, Yuri Burda, Harrison Edwards:
Learning Policy Representations in Multiagent Systems. ICML 2018: 1797-1806 - [c8]Shen Wang, Aditya Grover, Brian Mac Namee, Philip Plantholt, Javier Lopez-Leones, Pablo Sanchez-Escalonilla:
ROGER: An On-Line Flight Efficiency Monitoring System Using ADS-B Data. MDM 2018: 233-238 - [c7]Aditya Grover, Tudor Achim, Stefano Ermon:
Streamlining Variational Inference for Constraint Satisfaction Problems. NeurIPS 2018: 10579-10589 - [i12]Aditya Grover, Aaron Zweig, Stefano Ermon:
Graphite: Iterative Generative Modeling of Graphs. CoRR abs/1803.10459 (2018) - [i11]Aditya Grover, Todor M. Markov, Peter M. Attia, Norman Jin, Nicholas Perkins, Bryan Cheong, Michael H. Chen, Zi Yang, Stephen J. Harris, William C. Chueh, Stefano Ermon:
Best arm identification in multi-armed bandits with delayed feedback. CoRR abs/1803.10937 (2018) - [i10]Aditya Grover, Ramki Gummadi, Miguel Lázaro-Gredilla, Dale Schuurmans, Stefano Ermon:
Variational Rejection Sampling. CoRR abs/1804.01712 (2018) - [i9]Aditya Grover, Maruan Al-Shedivat, Jayesh K. Gupta, Yura Burda, Harrison Edwards:
Learning Policy Representations in Multiagent Systems. CoRR abs/1806.06464 (2018) - [i8]Manik Dhar, Aditya Grover, Stefano Ermon:
Modeling Sparse Deviations for Compressed Sensing using Generative Models. CoRR abs/1807.01442 (2018) - [i7]Aditya Grover, Tudor Achim, Stefano Ermon:
Streamlining Variational Inference for Constraint Satisfaction Problems. CoRR abs/1811.09813 (2018) - [i6]Jiaming Song, Pratyusha Kalluri, Aditya Grover, Shengjia Zhao, Stefano Ermon:
Learning Controllable Fair Representations. CoRR abs/1812.04218 (2018) - [i5]Aditya Grover, Stefano Ermon:
Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization. CoRR abs/1812.10539 (2018) - 2017
- [i4]Aditya Grover, Stefano Ermon:
Boosted Generative Models. CoRR abs/1702.08484 (2017) - [i3]Aditya Grover, Manik Dhar, Stefano Ermon:
Flow-GAN: Bridging implicit and prescribed learning in generative models. CoRR abs/1705.08868 (2017) - 2016
- [c6]Ankit Anand, Aditya Grover, Mausam, Parag Singla:
Contextual Symmetries in Probabilistic Graphical Models. IJCAI 2016: 3560-3568 - [c5]Aditya Grover, Jure Leskovec:
node2vec: Scalable Feature Learning for Networks. KDD 2016: 855-864 - [c4]Aditya Grover, Stefano Ermon:
Variational Bayes on Monte Carlo Steroids. NIPS 2016: 3018-3026 - [i2]Ankit Anand, Aditya Grover, Mausam, Parag Singla:
Contextual Symmetries in Probabilistic Graphical Models. CoRR abs/1606.09594 (2016) - [i1]Aditya Grover, Jure Leskovec:
node2vec: Scalable Feature Learning for Networks. CoRR abs/1607.00653 (2016) - 2015
- [c3]Ankit Anand, Aditya Grover, Mausam, Parag Singla:
A Novel Abstraction Framework for Online Planning: Extended Abstract. AAMAS 2015: 1901-1902 - [c2]Ankit Anand, Aditya Grover, Mausam, Parag Singla:
ASAP-UCT: Abstraction of State-Action Pairs in UCT. IJCAI 2015: 1509-1515 - [c1]Aditya Grover, Ashish Kapoor, Eric Horvitz:
A Deep Hybrid Model for Weather Forecasting. KDD 2015: 379-386
Coauthor Index
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