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2020 – today
- 2023
[c48]Aditya Grover:
Generative Decision Making Under Uncertainty. AAAI 2023: 15440- [c47]Baiting Zhu, Meihua Dang, Aditya Grover:
Scaling Pareto-Efficient Decision Making via Offline Multi-Objective RL. ICLR 2023 - [c46]Siddarth Krishnamoorthy, Satvik Mehul Mashkaria, Aditya Grover:
Diffusion Models for Black-Box Optimization. ICML 2023: 17842-17857 - [c45]Satvik Mehul Mashkaria, Siddarth Krishnamoorthy, Aditya Grover:
Generative Pretraining for Black-Box Optimization. ICML 2023: 24173-24197 - [c44]Tung Nguyen, Johannes Brandstetter, Ashish Kapoor, Jayesh K. Gupta, Aditya Grover:
ClimaX: A foundation model for weather and climate. ICML 2023: 25904-25938 - [c43]Qinqing Zheng, Mikael Henaff, Brandon Amos, Aditya Grover:
Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories. ICML 2023: 42339-42362 - [i47]Tung Nguyen, Johannes Brandstetter, Ashish Kapoor, Jayesh K. Gupta, Aditya Grover:
ClimaX: A foundation model for weather and climate. CoRR abs/2301.10343 (2023) - [i46]Hritik Bansal, Aditya Grover:
Leaving Reality to Imagination: Robust Classification via Generated Datasets. CoRR abs/2302.02503 (2023) - [i45]Hritik Bansal, Nishad Singhi, Yu Yang, Fan Yin, Aditya Grover, Kai-Wei Chang:
CleanCLIP: Mitigating Data Poisoning Attacks in Multimodal Contrastive Learning. CoRR abs/2303.03323 (2023) - [i44]Baiting Zhu, Meihua Dang, Aditya Grover:
Scaling Pareto-Efficient Decision Making Via Offline Multi-Objective RL. CoRR abs/2305.00567 (2023) - [i43]Siyan Zhao, Aditya Grover:
Decision Stacks: Flexible Reinforcement Learning via Modular Generative Models. CoRR abs/2306.06253 (2023) - [i42]Siddarth Krishnamoorthy, Satvik Mehul Mashkaria, Aditya Grover:
Diffusion Models for Black-Box Optimization. CoRR abs/2306.07180 (2023) - [i41]Tung Nguyen, Jason Jewik, Hritik Bansal, Prakhar Sharma, Aditya Grover:
ClimateLearn: Benchmarking Machine Learning for Weather and Climate Modeling. CoRR abs/2307.01909 (2023) - [i40]Hritik Bansal, John Dang, Aditya Grover:
Peering Through Preferences: Unraveling Feedback Acquisition for Aligning Large Language Models. CoRR abs/2308.15812 (2023) - 2022
- [j2]Joey Bose, Ricardo Pio Monti, Aditya Grover:
Controllable Generative Modeling via Causal Reasoning. Trans. Mach. Learn. Res. 2022 (2022) - [c42]Kevin Lu, Aditya Grover, Pieter Abbeel, Igor Mordatch:
Frozen Pretrained Transformers as Universal Computation Engines. AAAI 2022: 7628-7636 - [c41]Wenshuo Guo, Kumar Krishna Agrawal, Aditya Grover, Vidya K. Muthukumar, Ashwin Pananjady:
Learning from an Exploring Demonstrator: Optimal Reward Estimation for Bandits. AISTATS 2022: 6357-6386 - [c40]Yuqing Du, Pieter Abbeel, Aditya Grover:
It Takes Four to Tango: Multiagent Self Play for Automatic Curriculum Generation. ICLR 2022 - [c39]Omri Puny, Matan Atzmon, Edward J. Smith, Ishan Misra, Aditya Grover, Heli Ben-Hamu, Yaron Lipman:
Frame Averaging for Invariant and Equivariant Network Design. ICLR 2022 - [c38]Heli Ben-Hamu, Samuel Cohen, Joey Bose, Brandon Amos, Maximilian Nickel, Aditya Grover, Ricky T. Q. Chen, Yaron Lipman:
Matching Normalizing Flows and Probability Paths on Manifolds. ICML 2022: 1749-1763 - [c37]Tung Nguyen, Aditya Grover:
Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling. ICML 2022: 16569-16594 - [c36]Qinqing Zheng, Amy Zhang, Aditya Grover:
Online Decision Transformer. ICML 2022: 27042-27059 - [c35]Shashank Goel, Hritik Bansal, Sumit Bhatia, Ryan A. Rossi, Vishwa Vinay, Aditya Grover:
CyCLIP: Cyclic Contrastive Language-Image Pretraining. NeurIPS 2022 - [c34]Fangchen Liu, Hao Liu, Aditya Grover, Pieter Abbeel:
Masked Autoencoding for Scalable and Generalizable Decision Making. NeurIPS 2022 - [i39]Nimit Sharad Sohoni, Maziar Sanjabi, Nicolas Ballas, Aditya Grover, Shaoliang Nie, Hamed Firooz, Christopher Ré:
BARACK: Partially Supervised Group Robustness With Guarantees. CoRR abs/2201.00072 (2022) - [i38]Qinqing Zheng, Amy Zhang, Aditya Grover:
Online Decision Transformer. CoRR abs/2202.05607 (2022) - [i37]Yuqing Du, Pieter Abbeel, Aditya Grover:
It Takes Four to Tango: Multiagent Selfplay for Automatic Curriculum Generation. CoRR abs/2202.10608 (2022) - [i36]Carl Qi, Pieter Abbeel, Aditya Grover:
Imitating, Fast and Slow: Robust learning from demonstrations via decision-time planning. CoRR abs/2204.03597 (2022) - [i35]Shashank Goel, Hritik Bansal, Sumit Bhatia, Ryan A. Rossi, Vishwa Vinay, Aditya Grover:
CyCLIP: Cyclic Contrastive Language-Image Pretraining. CoRR abs/2205.14459 (2022) - [i34]Siddarth Krishnamoorthy, Satvik Mehul Mashkaria, Aditya Grover:
Generative Pretraining for Black-Box Optimization. CoRR abs/2206.10786 (2022) - [i33]Tung Nguyen, Aditya Grover:
Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling. CoRR abs/2207.04179 (2022) - [i32]Heli Ben-Hamu, Samuel Cohen, Joey Bose, Brandon Amos, Aditya Grover, Maximilian Nickel, Ricky T. Q. Chen, Yaron Lipman:
Matching Normalizing Flows and Probability Paths on Manifolds. CoRR abs/2207.04711 (2022) - [i31]Tung Nguyen, Qinqing Zheng, Aditya Grover:
ConserWeightive Behavioral Cloning for Reliable Offline Reinforcement Learning. CoRR abs/2210.05158 (2022) - [i30]Qinqing Zheng, Mikael Henaff, Brandon Amos, Aditya Grover:
Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories. CoRR abs/2210.06518 (2022) - [i29]Fangchen Liu, Hao Liu, Aditya Grover, Pieter Abbeel:
Masked Autoencoding for Scalable and Generalizable Decision Making. CoRR abs/2211.12740 (2022) - 2021
- [c33]Kevin Lu, Aditya Grover, Pieter Abbeel, Igor Mordatch:
Reset-Free Lifelong Learning with Skill-Space Planning. ICLR 2021 - [c32]Yilun Xu, Yang Song, Sahaj Garg, Linyuan Gong, Rui Shu, Aditya Grover, Stefano Ermon:
Anytime Sampling for Autoregressive Models via Ordered Autoencoding. ICLR 2021 - [c31]Abhishek Das, Muhammed Shuaibi, Aini Palizhati, Siddharth Goyal, Aditya Grover, Adeesh Kolluru, Janice Lan, Ammar Rizvi, Anuroop Sriram, Brandon M. Wood, Devi Parikh, Zachary W. Ulissi, C. Lawrence Zitnick, Guolin Ke, Shuxin Zheng, Yu Shi, Di He, Tie-Yan Liu, Chengxuan Ying, Jiacheng You, Yihan He, Rostislav Grigoriev, Ruslan Lukin, Adel Yarullin, Max Faleev:
The Open Catalyst Challenge 2021: Competition Report. NeurIPS (Competition and Demos) 2021: 29-40 - [c30]Chris Cundy, Aditya Grover, Stefano Ermon:
BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery. NeurIPS 2021: 7095-7110 - [c29]Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch:
Decision Transformer: Reinforcement Learning via Sequence Modeling. NeurIPS 2021: 15084-15097 - [c28]Noam Rozen, Aditya Grover, Maximilian Nickel, Yaron Lipman:
Moser Flow: Divergence-based Generative Modeling on Manifolds. NeurIPS 2021: 17669-17680 - [c27]Robin M. E. Swezey, Aditya Grover, Bruno Charron, Stefano Ermon:
PiRank: Scalable Learning To Rank via Differentiable Sorting. NeurIPS 2021: 21644-21654 - [i28]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) - [i27]Kevin Lu, Aditya Grover, Pieter Abbeel, Igor Mordatch:
Pretrained Transformers as Universal Computation Engines. CoRR abs/2103.05247 (2021) - [i26]Kourosh Hakhamaneshi, Pieter Abbeel, Vladimir Stojanovic, Aditya Grover:
JUMBO: Scalable Multi-task Bayesian Optimization using Offline Data. CoRR abs/2106.00942 (2021) - [i25]Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch:
Decision Transformer: Reinforcement Learning via Sequence Modeling. CoRR abs/2106.01345 (2021) - [i24]Muhammed Shuaibi, Adeesh Kolluru, Abhishek Das, Aditya Grover, Anuroop Sriram, Zachary W. Ulissi, C. Lawrence Zitnick:
Rotation Invariant Graph Neural Networks using Spin Convolutions. CoRR abs/2106.09575 (2021) - [i23]Wenshuo Guo, Kumar Krishna Agrawal, Aditya Grover, Vidya Muthukumar, Ashwin Pananjady:
Learning from an Exploring Demonstrator: Optimal Reward Estimation for Bandits. CoRR abs/2106.14866 (2021) - [i22]Noam Rozen, Aditya Grover, Maximilian Nickel, Yaron Lipman:
Moser Flow: Divergence-based Generative Modeling on Manifolds. CoRR abs/2108.08052 (2021) - [i21]Omri Puny, Matan Atzmon, Heli Ben-Hamu, Edward J. Smith, Ishan Misra, Aditya Grover, Yaron Lipman:
Frame Averaging for Invariant and Equivariant Network Design. CoRR abs/2110.03336 (2021) - [i20]Chris Cundy, Aditya Grover, Stefano Ermon:
BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery. CoRR abs/2112.02761 (2021) - 2020
- [b1]Aditya Grover:
Learning to represent and reason under limited supervision. Stanford University, USA, 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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last updated on 2023-09-27 22:49 CEST by the dblp team
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