Aditya Grover

ORCID

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 118

Top Venues

Publications

Shufan Li, Harkanwar Singh, Aditya Grover
PopAlign: Population-Level Alignment for Fair Text-to-Image Generation. CoRR (2024)
Juan Nathaniel, Yongquan Qu, Tung Nguyen, Sungduk Yu, Julius Busecke, Aditya Grover, Pierre Gentine
ChaosBench: A Multi-Channel, Physics-Based Benchmark for Subseasonal-to-Seasonal Climate Prediction. CoRR (2024)
Hritik Bansal, Ashima Suvarna, Gantavya Bhatt, Nanyun Peng, Kai-Wei Chang, Aditya Grover
Comparing Bad Apples to Good Oranges: Aligning Large Language Models via Joint Preference Optimization. CoRR (2024)
Siyan Zhao, John Dang, Aditya Grover
Group Preference Optimization: Few-Shot Alignment of Large Language Models. ICLR (2024)
Valay Bundele, Mahesh Bhupati, Biplab Banerjee, Aditya Grover
Scaling Vision-and-Language Navigation With Offline RL. CoRR (2024)
Hritik Bansal, Zongyu Lin, Tianyi Xie, Zeshun Zong, Michal Yarom, Yonatan Bitton, Chenfanfu Jiang, Yizhou Sun, Kai-Wei Chang, Aditya Grover
VideoPhy: Evaluating Physical Commonsense for Video Generation. CoRR (2024)
Valay Bundele, Mahesh Bhupati, Biplab Banerjee, Aditya Grover
Scaling Vision-and-Language Navigation With Offline RL. Trans. Mach. Learn. Res. 2024 (2024)
Siyan Zhao, Daniel Israel, Guy Van den Broeck, Aditya Grover
Prepacking: A Simple Method for Fast Prefilling and Increased Throughput in Large Language Models. CoRR (2024)
Hritik Bansal, Yonatan Bitton, Michal Yarom, Idan Szpektor, Aditya Grover, Kai-Wei Chang
TALC: Time-Aligned Captions for Multi-Scene Text-to-Video Generation. CoRR (2024)
Shufan Li, Harkanwar Singh, Aditya Grover
Mamba-ND: Selective State Space Modeling for Multi-Dimensional Data. CoRR (2024)
Hritik Bansal, John Dang, Aditya Grover
Peering Through Preferences: Unraveling Feedback Acquisition for Aligning Large Language Models. ICLR (2024)
Tung Nguyen, Aditya Grover
LICO: Large Language Models for In-Context Molecular Optimization. CoRR (2024)
Siyan Zhao, Tung Nguyen, Aditya Grover
Probing the Decision Boundaries of In-context Learning in Large Language Models. CoRR (2024)
Aditya Grover
Generative Decision Making Under Uncertainty. AAAI (2023)
Siyan Zhao, John Dang, Aditya Grover
Group Preference Optimization: Few-Shot Alignment of Large Language Models. CoRR (2023)
Tung Nguyen, Johannes Brandstetter, Ashish Kapoor, Jayesh K. Gupta, Aditya Grover
ClimaX: A foundation model for weather and climate. CoRR (2023)
Hritik Bansal, Nishad Singhi, Yu Yang, Fan Yin, Aditya Grover, Kai-Wei Chang
CleanCLIP: Mitigating Data Poisoning Attacks in Multimodal Contrastive Learning. CoRR (2023)
Tung Nguyen, Johannes Brandstetter, Ashish Kapoor, Jayesh K. Gupta, Aditya Grover
ClimaX: A foundation model for weather and climate. ICML (2023)
Tung Nguyen, Jason Jewik, Hritik Bansal, Prakhar Sharma, Aditya Grover
ClimateLearn: Benchmarking Machine Learning for Weather and Climate Modeling. NeurIPS (2023)
Daniel Israel, Aditya Grover, Guy Van den Broeck
High Dimensional Causal Inference with Variational Backdoor Adjustment. CoRR (2023)
Tung Nguyen, Sudhanshu Agrawal, Aditya Grover
ExPT: Synthetic Pretraining for Few-Shot Experimental Design. NeurIPS (2023)
Siyan Zhao, Aditya Grover
Decision Stacks: Flexible Reinforcement Learning via Modular Generative Models. CoRR (2023)
Siyan Zhao, Aditya Grover
Decision Stacks: Flexible Reinforcement Learning via Modular Generative Models. NeurIPS (2023)
Tung Nguyen, Jason Jewik, Hritik Bansal, Prakhar Sharma, Aditya Grover
ClimateLearn: Benchmarking Machine Learning for Weather and Climate Modeling. CoRR (2023)
Shufan Li, Harkanwar Singh, Aditya Grover
InstructAny2Pix: Flexible Visual Editing via Multimodal Instruction Following. CoRR (2023)
Siddarth Krishnamoorthy, Satvik Mehul Mashkaria, Aditya Grover
Diffusion Models for Black-Box Optimization. CoRR (2023)
Tung Nguyen, Rohan Shah, Hritik Bansal, Troy Arcomano, Sandeep Madireddy, Romit Maulik, Veerabhadra Kotamarthi, Ian T. Foster, Aditya Grover
Scaling transformer neural networks for skillful and reliable medium-range weather forecasting. CoRR (2023)
Hritik Bansal, Yonatan Bitton, Idan Szpektor, Kai-Wei Chang, Aditya Grover
VideoCon: Robust Video-Language Alignment via Contrast Captions. CoRR (2023)
Hritik Bansal, John Dang, Aditya Grover
Peering Through Preferences: Unraveling Feedback Acquisition for Aligning Large Language Models. CoRR (2023)
Qinqing Zheng, Matt Le, Neta Shaul, Yaron Lipman, Aditya Grover, Ricky T. Q. Chen
Guided Flows for Generative Modeling and Decision Making. CoRR (2023)
Hritik Bansal, Fan Yin, Nishad Singhi, Aditya Grover, Yu Yang, Kai-Wei Chang
CleanCLIP: Mitigating Data Poisoning Attacks in Multimodal Contrastive Learning. ICCV (2023)
Baiting Zhu, Meihua Dang, Aditya Grover
Scaling Pareto-Efficient Decision Making via Offline Multi-Objective RL. ICLR (2023)
Hritik Bansal, Aditya Grover
Leaving Reality to Imagination: Robust Classification via Generated Datasets. CoRR (2023)
Siddarth Krishnamoorthy, Satvik Mehul Mashkaria, Aditya Grover
Diffusion Models for Black-Box Optimization. ICML (2023)
Tung Nguyen, Sudhanshu Agrawal, Aditya Grover
ExPT: Synthetic Pretraining for Few-Shot Experimental Design. CoRR (2023)
Baiting Zhu, Meihua Dang, Aditya Grover
Scaling Pareto-Efficient Decision Making Via Offline Multi-Objective RL. CoRR (2023)
Satvik Mehul Mashkaria, Siddarth Krishnamoorthy, Aditya Grover
Generative Pretraining for Black-Box Optimization. ICML (2023)
Qinqing Zheng, Mikael Henaff, Brandon Amos, Aditya Grover
Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories. ICML (2023)
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 (2022)
Shashank Goel, Hritik Bansal, Sumit Bhatia, Ryan A. Rossi, Vishwa Vinay, Aditya Grover
CyCLIP: Cyclic Contrastive Language-Image Pretraining. CoRR (2022)
Carl Qi, Pieter Abbeel, Aditya Grover
Imitating, Fast and Slow: Robust learning from demonstrations via decision-time planning. CoRR (2022)
Wenshuo Guo, Kumar Krishna Agrawal, Aditya Grover, Vidya K. Muthukumar, Ashwin Pananjady
Learning from an Exploring Demonstrator: Optimal Reward Estimation for Bandits. AISTATS (2022)
Qinqing Zheng, Amy Zhang, Aditya Grover
Online Decision Transformer. ICML (2022)
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)
Shashank Goel, Hritik Bansal, Sumit Bhatia, Ryan A. Rossi, Vishwa Vinay, Aditya Grover
CyCLIP: Cyclic Contrastive Language-Image Pretraining. NeurIPS (2022)
Tung Nguyen, Aditya Grover
Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling. CoRR (2022)
Fangchen Liu, Hao Liu, Aditya Grover, Pieter Abbeel
Masked Autoencoding for Scalable and Generalizable Decision Making. NeurIPS (2022)
Tung Nguyen, Aditya Grover
Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling. ICML (2022)
Siddarth Krishnamoorthy, Satvik Mehul Mashkaria, Aditya Grover
Generative Pretraining for Black-Box Optimization. CoRR (2022)
Kevin Lu, Aditya Grover, Pieter Abbeel, Igor Mordatch
Frozen Pretrained Transformers as Universal Computation Engines. AAAI (2022)
Nimit Sharad Sohoni, Maziar Sanjabi, Nicolas Ballas, Aditya Grover, Shaoliang Nie, Hamed Firooz, Christopher Ré
BARACK: Partially Supervised Group Robustness With Guarantees. CoRR (2022)
Yuqing Du, Pieter Abbeel, Aditya Grover
It Takes Four to Tango: Multiagent Self Play for Automatic Curriculum Generation. ICLR (2022)
Tung Nguyen, Qinqing Zheng, Aditya Grover
ConserWeightive Behavioral Cloning for Reliable Offline Reinforcement Learning. CoRR (2022)
Fangchen Liu, Hao Liu, Aditya Grover, Pieter Abbeel
Masked Autoencoding for Scalable and Generalizable Decision Making. CoRR (2022)
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)
Yuqing Du, Pieter Abbeel, Aditya Grover
It Takes Four to Tango: Multiagent Selfplay for Automatic Curriculum Generation. CoRR (2022)
Qinqing Zheng, Mikael Henaff, Brandon Amos, Aditya Grover
Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories. CoRR (2022)
Qinqing Zheng, Amy Zhang, Aditya Grover
Online Decision Transformer. CoRR (2022)
Joey Bose, Ricardo Pio Monti, Aditya Grover
Controllable Generative Modeling via Causal Reasoning. Trans. Mach. Learn. Res. 2022 (2022)
Robin M. E. Swezey, Aditya Grover, Bruno Charron, Stefano Ermon
PiRank: Scalable Learning To Rank via Differentiable Sorting. NeurIPS (2021)
Kourosh Hakhamaneshi, Pieter Abbeel, Vladimir Stojanovic, Aditya Grover
JUMBO: Scalable Multi-task Bayesian Optimization using Offline Data. CoRR (2021)
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 (2021)
Noam Rozen, Aditya Grover, Maximilian Nickel, Yaron Lipman
Moser Flow: Divergence-based Generative Modeling on Manifolds. CoRR (2021)
Wenshuo Guo, Kumar Krishna Agrawal, Aditya Grover, Vidya Muthukumar, Ashwin Pananjady
Learning from an Exploring Demonstrator: Optimal Reward Estimation for Bandits. CoRR (2021)
Yilun Xu, Yang Song, Sahaj Garg, Linyuan Gong, Rui Shu, Aditya Grover, Stefano Ermon
Anytime Sampling for Autoregressive Models via Ordered Autoencoding. CoRR (2021)
Noam Rozen, Aditya Grover, Maximilian Nickel, Yaron Lipman
Moser Flow: Divergence-based Generative Modeling on Manifolds. NeurIPS (2021)
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)
Kevin Lu, Aditya Grover, Pieter Abbeel, Igor Mordatch
Pretrained Transformers as Universal Computation Engines. CoRR (2021)
Chris Cundy, Aditya Grover, Stefano Ermon
BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery. NeurIPS (2021)
Yilun Xu, Yang Song, Sahaj Garg, Linyuan Gong, Rui Shu, Aditya Grover, Stefano Ermon
Anytime Sampling for Autoregressive Models via Ordered Autoencoding. ICLR (2021)
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)
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 (2021)
Chris Cundy, Aditya Grover, Stefano Ermon
BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery. CoRR (2021)
Kevin Lu, Aditya Grover, Pieter Abbeel, Igor Mordatch
Reset-Free Lifelong Learning with Skill-Space Planning. ICLR (2021)
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 (2021)
Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, Stefano Ermon
Permutation Invariant Graph Generation via Score-Based Generative Modeling. CoRR (2020)
Aditya Grover, Christopher Chute, Rui Shu, Zhangjie Cao, Stefano Ermon
AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows. AAAI (2020)
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) (2020)
Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, Stefano Ermon
Permutation Invariant Graph Generation via Score-Based Generative Modeling. AISTATS (2020)
Kevin Lu, Aditya Grover, Pieter Abbeel, Igor Mordatch
Reset-Free Lifelong Learning with Skill-Space Planning. CoRR (2020)
Kristy Choi, Aditya Grover, Trisha Singh, Rui Shu, Stefano Ermon
Fair Generative Modeling via Weak Supervision. ICML (2020)
Robin M. E. Swezey, Aditya Grover, Bruno Charron, Stefano Ermon
PiRank: Learning To Rank via Differentiable Sorting. CoRR (2020)
Aditya Grover, Eric Wang, Aaron Zweig, Stefano Ermon
Stochastic Optimization of Sorting Networks via Continuous Relaxations. CoRR (2019)
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)
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 (2019)
Aditya Grover, Aaron Zweig, Stefano Ermon
Graphite: Iterative Generative Modeling of Graphs. ICML (2019)
Aditya Grover, Christopher Chute, Rui Shu, Zhangjie Cao, Stefano Ermon
AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows. CoRR (2019)
Kristy Choi, Kedar Tatwawadi, Aditya Grover, Tsachy Weissman, Stefano Ermon
Neural Joint Source-Channel Coding. ICML (2019)
Aditya Grover, Christopher Chute, Rui Shu, Zhangjie Cao, Stefano Ermon
AlignFlow: Learning from multiple domains via normalizing flows. DGS@ICLR (2019)
Aditya Grover, Kristy Choi, Rui Shu, Stefano Ermon
Fair Generative Modeling via Weak Supervision. CoRR (2019)
Aditya Grover, Eric Wang, Aaron Zweig, Stefano Ermon
Stochastic Optimization of Sorting Networks via Continuous Relaxations. ICLR (Poster) (2019)
Aditya Grover, Stefano Ermon
Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization. AISTATS (2019)
Jiaming Song, Pratyusha Kalluri, Aditya Grover, Shengjia Zhao, Stefano Ermon
Learning Controllable Fair Representations. AISTATS (2019)
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)
Aditya Grover, Maruan Al-Shedivat, Jayesh K. Gupta, Yura Burda, Harrison Edwards
Learning Policy Representations in Multiagent Systems. CoRR (2018)
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 (2018)
Aditya Grover, Aaron Zweig, Stefano Ermon
Graphite: Iterative Generative Modeling of Graphs. CoRR (2018)
Manik Dhar, Aditya Grover, Stefano Ermon
Modeling Sparse Deviations for Compressed Sensing using Generative Models. ICML (2018)
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)
Aditya Grover, Manik Dhar, Stefano Ermon
Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models. AAAI (2018)

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Publication Activity (10 Years)

Top Topics

Top Venues

Aditya Grover