Influence of learners' motivation on responses to Facebook promotions of online courses. - Rahul Ramesh; Deepak Gupta 003 (ICACCI, 2015)

Learning to Factor Policies and Action-Value Functions: Factored Action Space Representations for Deep Reinforcement learning. - Sahil Sharma 003; Aravind Suresh; Rahul Ramesh; Balaraman Ravindran (CoRR, 2017)

AUPCR Maximizing Matchings : Towards a Pragmatic Notion of Optimality for One-Sided Preference Matchings. - Girish Raguvir J; Rahul Ramesh; Sachin Sridhar; Vignesh Manoharan (CoRR, 2017)

A Question-Answering framework for plots using Deep learning. - Revanth Reddy; Rahul Ramesh; Ameet Deshpande; Mitesh M. Khapra (CoRR, 2018)

Successor Options: An Option Discovery Framework for Reinforcement Learning. - Rahul Ramesh; Manan Tomar; Balaraman Ravindran (IJCAI, 2019)

FigureNet : A Deep Learning model for Question-Answering on Scientific Plots. - Revanth Reddy; Rahul Ramesh; Ameet Deshpande; Mitesh M. Khapra (IJCNN, 2019)

Option Encoder: A Framework for Discovering a Policy Basis in Reinforcement Learning. - Arjun Manoharan; Rahul Ramesh; Balaraman Ravindran (ECML/PKDD (2), 2020)

Boosting a Model Zoo for Multi-Task and Continual Learning. - Rahul Ramesh; Pratik Chaudhari (CoRR, 2021)

Model Zoo: A Growing Brain That Learns Continually. - Rahul Ramesh; Pratik Chaudhari (ICLR, 2022)

Deep Reference Priors: What is the best way to pretrain a model? - Yansong Gao; Rahul Ramesh; Pratik Chaudhari (ICML, 2022)

Prospective Learning: Principled Extrapolation to the Future. - Ashwin De Silva; Rahul Ramesh; Lyle H. Ungar; Marshall G. Hussain Shuler; Noah J. Cowan; Michael L. Platt; Chen Li; Leyla Isik; Seung-Eon Roh; Adam S. Charles; Archana Venkataraman; Brian Caffo; et al. (CoLLAs, 2023)

A Picture of the Space of Typical Learnable Tasks. - Rahul Ramesh; Jialin Mao; Itay Griniasty; Rubing Yang; Han Kheng Teoh; Mark K. Transtrum; James P. Sethna; Pratik Chaudhari (ICML, 2023)

The Value of Out-of-Distribution Data. - Ashwin De Silva; Rahul Ramesh; Carey E. Priebe; Pratik Chaudhari; Joshua T. Vogelstein (ICML, 2023)

The Training Process of Many Deep Networks Explores the Same Low-Dimensional Manifold. - Jialin Mao; Itay Griniasty; Han Kheng Teoh; Rahul Ramesh; Rubing Yang; Mark K. Transtrum; James P. Sethna; Pratik Chaudhari (CoRR, 2023)

How Capable Can a Transformer Become? A Study on Synthetic, Interpretable Tasks. - Rahul Ramesh; Mikail Khona; Robert P. Dick; Hidenori Tanaka; Ekdeep Singh Lubana (CoRR, 2023)

Towards an Understanding of Stepwise Inference in Transformers: A Synthetic Graph Navigation Model. - Mikail Khona; Maya Okawa; Jan Hula; Rahul Ramesh; Kento Nishi; Robert P. Dick; Ekdeep Singh Lubana; Hidenori Tanaka (ICML, 2024)

Compositional Capabilities of Autoregressive Transformers: A Study on Synthetic, Interpretable Tasks. - Rahul Ramesh; Ekdeep Singh Lubana; Mikail Khona; Robert P. Dick; Hidenori Tanaka (ICML, 2024)

Prospective Learning: Learning for a Dynamic Future. - Ashwin De Silva; Rahul Ramesh; Rubing Yang; Siyu Yu; Joshua T. Vogelstein; Pratik Chaudhari (NeurIPS, 2024)

Many Perception Tasks are Highly Redundant Functions of their Input Data. - Rahul Ramesh; Anthony Bisulco; Ronald W. Ditullio; Linran Wei; Vijay Balasubramanian; Kostas Daniilidis; Pratik Chaudhari (CoRR, 2024)

Representation Shattering in Transformers: A Synthetic Study with Knowledge Editing. - Kento Nishi; Maya Okawa; Rahul Ramesh; Mikail Khona; Ekdeep Singh Lubana; Hidenori Tanaka (CoRR, 2024)

From Linearity to Non-Linearity: How Masked Autoencoders Capture Spatial Correlations. - Anthony Bisulco; Rahul Ramesh; Randall Balestriero; Pratik Chaudhari (CoRR, 2025)