🧑‍🔬 Aditya Grover

🏛️ Affiliations

🎓University of California, Los AngelesLos Angeles · United States2020–2026 · 45 paperscurrent🎓Stanford UniversityStanford · United States2016–2021 · 27 papers🏢MetaMenlo Park · United States2020–2022 · 6 papers🎓University of California, BerkeleyBerkeley · United States2020–2022 · 4 papers🎓Indian Institute of Technology DelhiNew Delhi · India2015–2016 · 4 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models (2026 · 97 citations)
   Reasoning-oriented post-training of LLMs usually relies on RLVR (e.g., GRPO), SFT, or distillation from a stronger teacher, and each has a weakness.
2. When To Solve, When To Verify: Compute-Optimal Problem Solving and Generative Verification for LLM Reasoning (2025 · 12 citations)
   Large language models can spend extra test-time compute on reasoning in two main ways. The first is Self-Consistency (SC), which samples many candidate solutions and takes a majority vote. The second samples fewer solutions and ranks them with a Generative Reward Model (GenRM), which writes several verification…

All works

27 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

1. node2vec ↗ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · no summary yetAditya Grover, Jure Leskovec2016 · 10,750 citations
2. Closed-loop optimization of fast-charging protocols for batteries with machine learning ↗Nature · no summary yetPeter M. Attia et al.2020 · 1,155 citations
3. node2vec: Scalable Feature Learning for Networks ↗PubMed · no summary yetAditya Grover, Jure Leskovec2016 · 484 citations
4. Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models ↗Proceedings of the AAAI Conference on Artificial Intelligence · no summary yetAditya Grover, Manik Dhar, Stefano Ermon2018 · 204 citations
5. Bayesian learning for rapid prediction of lithium-ion battery-cycling protocols ↗Joule · no summary yetBenben Jiang et al.2021 · 128 citations
6. Frozen Pretrained Transformers as Universal Computation Engines ↗Proceedings of the AAAI Conference on Artificial Intelligence · no summary yetKevin Lu et al.2022 · 123 citations
7. Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language ModelsarXiv:2601.18734Siyan Zhao, Zhihui Xie, Mengchen Liu, et al.2026 · 97 citations
8. Stochastic Optimization of Sorting Networks via Continuous Relaxations ↗arXiv (Cornell University) · no summary yetAditya Grover et al.2019 · 89 citations
9. Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting ↗arXiv (Cornell University) · no summary yetAditya Grover et al.2019 · 80 citations
10. Learning Controllable Fair Representations ↗arXiv (Cornell University) · no summary yetJiaming Song et al.2018 · 70 citations
11. Graphite: Iterative Generative Modeling of Graphs ↗arXiv (Cornell University) · no summary yetAditya Grover, Aaron Zweig, Stefano Ermon2018 · 48 citations
12. Modeling Sparse Deviations for Compressed Sensing using Generative Models ↗arXiv (Cornell University) · no summary yetManik Dhar, Aditya Grover, Stefano Ermon2018 · 43 citations
13. Boosted Generative Models ↗Proceedings of the AAAI Conference on Artificial Intelligence · no summary yetAditya Grover, Stefano Ermon2018 · 42 citations
14. Mercury: Ultra-Fast Language Models Based on Diffusion ↗arXiv:2506.17298 · no summary yetInception Labs et al.2025 · 36 citations
15. Permutation Invariant Graph Generation via Score-Based Generative Modeling ↗arXiv (Cornell University) · no summary yetChenhao Niu et al.2020 · 34 citations
16. Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization ↗arXiv (Cornell University) · no summary yetAditya Grover, Stefano Ermon2018 · 31 citations
17. Best arm identification in multi-armed bandits with delayed feedback ↗arXiv (Cornell University) · no summary yetAditya Grover et al.2018 · 27 citations
18. Fair Generative Modeling via Weak Supervision ↗arXiv (Cornell University) · no summary yetKristy Choi et al.2019 · 15 citations
19. When To Solve, When To Verify: Compute-Optimal Problem Solving and Generative Verification for LLM ReasoningarXiv:2504.01005Nishad Singhi, Hritik Bansal, Arian Hosseini, et al.2025 · 12 citations
20. PiRank: Scalable Learning To Rank via Differentiable Sorting ↗arXiv (Cornell University) · no summary yetRobin M. E. Swezey et al.2020 · 9 citations
21. Anytime Sampling for Autoregressive Models via Ordered Autoencoding ↗arXiv (Cornell University) · no summary yetYilun Xu et al.2021 · 8 citations
22. Streamlining variational inference for constraint satisfaction problems* ↗Journal of Statistical Mechanics Theory and Experiment · no summary yetAditya Grover, Tudor Achim, Stefano Ermon2019 · 6 citations
23. Variational Rejection Sampling ↗arXiv (Cornell University) · no summary yetAditya Grover et al.2018 · 6 citations
24. BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery ↗arXiv (Cornell University) · no summary yetChris Cundy, Aditya Grover, Stefano Ermon2021 · 4 citations
25. Variational Bayes on Monte Carlo Steroids ↗neural information processing systems · no summary yetAditya Grover, Stefano Ermon2016 · 4 citations

See all 27 works by Aditya Grover →

🤝 Co-authors here

7 co-authors with a page, the most papers together first.

• Anna Rohrbach
• Arian Hosseini
• Hritik Bansal
• Jing Huang
• Kai-Wei Chang
• Marcus Rohrbach
• Zhihui Xie

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Proceedings of the AAAI Conference on Artificial Intelligence ×3
• ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)
• International Conference on Learning Representations
• Joule
• Journal of Statistical Mechanics Theory and Experiment
• Nature
• PubMed
• neural information processing systems

🔗 Cited by

Papers of our library cite their works 663 times (of the 149,439 papers whose bibliographies we read); 63 citing papers with a page here →.

Updated 2 November 2026

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