A preliminary benchmark of four saliency algorithms on comic art. - Khimya Khetarpal; Eakta Jain (ICME Workshops, 2016)

Creating Segments and Effects on Comics by Clustering Gaze Data. - Ishwarya Thirunarayanan; Khimya Khetarpal; Sanjeev J. Koppal; Olivier Le Meur; John M. Shea; Eakta Jain (ACM Trans. Multim. Comput. Commun. Appl., 2017)

Attend Before you Act: Leveraging human visual attention for continual learning. - Khimya Khetarpal; Doina Precup (CoRR, 2018)

Environments for Lifelong Reinforcement Learning. - Khimya Khetarpal; Shagun Sodhani; Sarath Chandar; Doina Precup (CoRR, 2018)

Learning Generalized Temporal Abstractions across Both Action and Perception. - Khimya Khetarpal (AAAI, 2019)

Learning Options with Interest Functions. - Khimya Khetarpal; Doina Precup (AAAI, 2019)

Options of Interest: Temporal Abstraction with Interest Functions. - Khimya Khetarpal; Martin Klissarov; Maxime Chevalier-Boisvert; Pierre-Luc Bacon; Doina Precup (AAAI, 2020)

Value Preserving State-Action Abstractions. - David Abel; Nate Umbanhowar; Khimya Khetarpal; Dilip Arumugam; Doina Precup; Michael L. Littman (AISTATS, 2020)

What can I do here? A Theory of Affordances in Reinforcement Learning. - Khimya Khetarpal; Zafarali Ahmed; Gheorghe Comanici; David Abel; Doina Precup (ICML, 2020)

Multi-Task Reinforcement Learning as a Hidden-Parameter Block MDP. - Amy Zhang 001; Shagun Sodhani; Khimya Khetarpal; Joelle Pineau (CoRR, 2020)

Variance Penalized On-Policy and Off-Policy Actor-Critic. - Arushi Jain; Gandharv Patil; Ayush Jain; Khimya Khetarpal; Doina Precup (AAAI, 2021)

Self-Supervised Attention-Aware Reinforcement Learning. - Haiping Wu; Khimya Khetarpal; Doina Precup (AAAI, 2021)

Learning Robust State Abstractions for Hidden-Parameter Block MDPs. - Amy Zhang 001; Shagun Sodhani; Khimya Khetarpal; Joelle Pineau (ICLR, 2021)

Temporally Abstract Partial Models. - Khimya Khetarpal; Zafarali Ahmed; Gheorghe Comanici; Doina Precup (NeurIPS, 2021)

Sequoia: A Software Framework to Unify Continual Learning Research. - Fabrice Normandin; Florian Golemo; Oleksiy Ostapenko; Pau Rodríguez; Matthew D. Riemer; Julio Hurtado; Khimya Khetarpal; Dominic Zhao; Ryan Lindeborg; Timothée Lesort; Laurent Charlin; Irina Rish; et al. (CoRR, 2021)

Safe option-critic: learning safety in the option-critic architecture. - Arushi Jain; Khimya Khetarpal; Doina Precup (Knowl. Eng. Rev., 2021)

The Paradox of Choice: Using Attention in Hierarchical Reinforcement Learning. - Andrei Cristian Nica; Khimya Khetarpal; Doina Precup (CoRR, 2022)

Towards Continual Reinforcement Learning: A Review and Perspectives. - Khimya Khetarpal; Matthew Riemer; Irina Rish; Doina Precup (J. Artif. Intell. Res., 2022)

Discovering Object-Centric Generalized Value Functions From Pixels. - Somjit Nath; Gopeshh Raaj Subbaraj; Khimya Khetarpal; Samira Ebrahimi Kahou (ICML, 2023)

Forecaster: Towards Temporally Abstract Tree-Search Planning from Pixels. - Thomas Jiralerspong; Flemming Kondrup; Doina Precup; Khimya Khetarpal (CoRR, 2023)

POMRL: No-Regret Learning-to-Plan with Increasing Horizons. - Khimya Khetarpal; Claire Vernade; Brendan O'Donoghue; Satinder Singh 001; Tom Zahavy (Trans. Mach. Learn. Res., 2023)

Disentangling the Causes of Plasticity Loss in Neural Networks. - Clare Lyle; Zeyu Zheng; Khimya Khetarpal; Hado van Hasselt; Razvan Pascanu; James Martens; Will Dabney (CoLLAs, 2024)

Normalization and effective learning rates in reinforcement learning. - Clare Lyle; Zeyu Zheng; Khimya Khetarpal; James Martens; Hado Philip van Hasselt; Razvan Pascanu; Will Dabney (NeurIPS, 2024)

Balancing Context Length and Mixing Times for Reinforcement Learning at Scale. - Matthew Riemer; Khimya Khetarpal; Janarthanan Rajendran; Sarath Chandar (NeurIPS, 2024)

Toward Human-AI Alignment in Large-Scale Multi-Player Games. - Sugandha Sharma; Guy Davidson; Khimya Khetarpal; Anssi Kanervisto; Udit Arora; Katja Hofmann; Ida Momennejad (CoRR, 2024)

A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning. - Khimya Khetarpal; Zhaohan Daniel Guo; Bernardo Ávila Pires; Yunhao Tang; Clare Lyle; Mark Rowland 001; Nicolas Heess; Diana L. Borsa; Arthur Guez; Will Dabney (AISTATS, 2025)

Optimizing Return Distributions with Distributional Dynamic Programming. - Bernardo Ávila Pires; Mark Rowland 001; Diana Borsa; Zhaohan Daniel Guo; Khimya Khetarpal; André Barreto 001; David Abel; Rémi Munos; Will Dabney (CoRR, 2025)

Agency Is Frame-Dependent. - David Abel; André Barreto 001; Michael Bowling; Will Dabney; Shi Dong; Steven Hansen 001; Anna Harutyunyan; Khimya Khetarpal; Clare Lyle; Razvan Pascanu; Georgios Piliouras; Doina Precup; et al. (CoRR, 2025)

Long Range Navigator (LRN): Extending robot planning horizons beyond metric maps. - Matt Schmittle; Rohan Baijal; Nathan Hatch; Rosario Scalise; Mateo Guaman Castro; Sidharth Talia; Khimya Khetarpal; Byron Boots; Siddhartha S. Srinivasa (CoRR, 2025)

Representation Learning via Non-Contrastive Mutual Information. - Zhaohan Daniel Guo; Bernardo Ávila Pires; Khimya Khetarpal; Dale Schuurmans; Bo Dai 001 (CoRR, 2025)

Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning. - Lynn Cherif; Flemming Kondrup; David Venuto; Ankit Anand; Doina Precup; Khimya Khetarpal (CoRR, 2025)

Plasticity as the Mirror of Empowerment. - David Abel; Michael Bowling; André Barreto 001; Will Dabney; Shi Dong; Steven Hansen 001; Anna Harutyunyan; Khimya Khetarpal; Clare Lyle; Razvan Pascanu; Georgios Piliouras; Doina Precup; et al. (CoRR, 2025)

Self-Predictive Representations for Combinatorial Generalization in Behavioral Cloning. - Daniel Lawson; Adriana Hugessen; Charlotte Cloutier; Glen Berseth; Khimya Khetarpal (CoRR, 2025)