Aditya Grover

About

Aditya Grover is an Assistant Professor of Computer Science at UCLA Samueli School of Engineering. His research focuses on probabilistic machine learning for unsupervised representation learning and sequential decision making. He has contributed to advancements in neural information processing, including divergence-based generative modeling on manifolds. Grover has received numerous awards for his work, including the Forbes 30 Under 30 in Science (2023), the AI2050 Early Career Fellowship (2024), and the Samsung AI Researcher of the Year Award (2022). His work has been recognized in the media and he has been acknowledged for his outstanding contributions to the field of machine learning and AI.

Research topics

• Computer Science
• Machine Learning
• Artificial Intelligence
• Engineering
• Meteorology
• Ecology
• Electrical engineering
• Geography
• Mathematics
• Reliability engineering

Selected publications

• Decision Transformer: Reinforcement Learning via Sequence Modeling

  arXiv (Cornell University) · 2021 · 465 citations

  We introduce a framework that abstracts Reinforcement Learning (RL) as a sequence modeling problem. This allows us to draw upon the simplicity and scalability of the Transformer architecture, and associated advances in language modeling such as GPT-x and BERT. In particular, we present Decision Transformer, an architecture that casts the problem of RL as conditional sequence modeling. Unlike prior approaches to RL that fit value functions or compute policy gradients, Decision Transformer simply…

  DOI

• ClimaX: A foundation model for weather and climate

  arXiv (Cornell University) · 2023 · 168 citations

  Senior authorCorresponding

  Most state-of-the-art approaches for weather and climate modeling are based on physics-informed numerical models of the atmosphere. These approaches aim to model the non-linear dynamics and complex interactions between multiple variables, which are challenging to approximate. Additionally, many such numerical models are computationally intensive, especially when modeling the atmospheric phenomenon at a fine-grained spatial and temporal resolution. Recent data-driven approaches based on machine l…

  DOI

• Mamba-ND: Selective State Space Modeling for Multi-dimensional Data

  Lecture notes in computer science · 2024-10-24 · 55 citations

  book-chapterSenior author
  PublisherDOI

• Comparing Bad Apples to Good Oranges Aligning Large Language Models via Joint Preference Optimization

  2025-01-01 · 1 citations

  articleOpen accessSenior author

  A common technique for aligning large language models (LLMs) relies on acquiring human preferences by comparing multiple generations conditioned on a fixed context.This method, however, relies solely on pairwise comparisons, where the generations are evaluated within an identical context.While effective to such conditional preferences often fail to encompass the nuanced and multidimensional nature of human preferences.In this work, we revisit the traditional paradigm of preference acquisition an…

  PublisherOA PDFDOI

• InstructAny2Pix: Image Editing with Multi-Modal Prompts

  2025-01-01 · 1 citations

  articleOpen accessSenior author
  PublisherOA PDFDOI

Frequent coauthors

• Stefano Ermon

  41 shared

• Hritik Bansal

  13 shared

• Pieter Abbeel

  University of California, Berkeley

  12 shared

• Tung Thanh Nguyen

  9 shared

• Stefano Ermon

  Stanford University

  9 shared

• Rui Shu

  Northeastern University

  7 shared

• Yaron Lipman

  7 shared

• Kai-Wei Chang

  7 shared

Awards & honors

• AI2050 Early Career Fellowship (2024)
• Forbes 30 Under 30 (2023)
• Samsung AI Researcher of the Year Award (2022)
• NeurIPS Outstanding Paper Award (2021)
• ACM SIGKDD Doctoral Dissertation Award (2021)

Similar researchers at University of California, Los Angeles

• Demetri Terzopoulosh-93
• Wei Wangh-91
• Eleazar Eskinh-83