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Word cloudA word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
Anbil Parthipan, S. C., Khetarpal, K., Rajendran, J., & Riemer, M. (2024, December). Balancing Context Length and Mixing Times for Reinforcement Learning at Scale [Paper]. 38th Conference on Neural Information Processing Systems (NeurIPS 2024), Vancouver, BC, Canada. External link
Bouchoucha, R., Haj Yahmed, A., Patil, D., Rajendran, J., Nikanjam, A., Anbil Parthipan, S. C., & Khomh, F. (2024, October). Toward Debugging Deep Reinforcement Learning Programs with RLExplorer [Paper]. IEEE International Conference on Software Maintenance and Evolution (ICSME 2024), Flagstaff, AZ, USA. External link
Govindarajan, P., Miret, S., Rector-Brooks, J., Phielipp, M., Rajendran, J., & Chandar, S. (2024). Learning conditional policies for crystal design using offline reinforcement learning. Digital Discovery, 3(4), 769-785. Available
Nekoei, H., Badrinaaraayanan, A., Sinha, A., Amini, M., Rajendran, J., Mahajan, A., & Anbil Parthipan, S. C. (2023, August). Dealing with non-stationarity in decentralized cooperative multi-agent deep reinforcement learning via multi-timescale learning [Paper]. 2nd Conference on Lifelong Learning Agents (CoLLAs 2023), Montreal, Qc. Canada. Unavailable
Nekoei, H., Zhao, X. T., Rajendran, J., Liu, M. A., & Anbil Parthipan, S. C. (2023, August). Towards few-shot coordination : revisiting ad-hoc teamplay challenge in the game of Hanabi [Paper]. 2nd Conference on Lifelong Learning Agents (CoLLAs 2023), Montreal, Qc, Canada. Unavailable
Patil, D., Rajendran, J., Berseth, G., & Anbil Parthipan, S. C. (2024, May). Intelligent Switching for Reset-Free RL [Paper]. 12th International Conference on Learning Representations (ICLR 2024), Vienna, Austria. External link
Rahimi-Kalahroudi, A., Rajendran, J., Momennejad, I., van Seijen, H., & Anbil Parthipan, S. C. (2023, August). Replay buffer with local forgetting for adapting to local environment changes in deep model-based reinforcement learning [Paper]. 2nd Conference on Lifelong Learning Agents (CoLLAs 2023), Montreal, Qc, Canada. External link
Rajendran, J., Khapra, M. M., Anbil Parthipan, S. C., & Ravindran, B. (2016, June). Bridge Correlational Neural Networks for Multilingual Multimodal Representation Learning [Paper]. Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego, California. External link
Samsami, M. R., Zholus, A., Rajendran, J., & Anbil Parthipan, S. C. (2024, May). Mastering Memory Tasks with World Models [Paper]. 12th International Conference on Learning Representations (ICLR 2024), Vienna, Austria. External link
Saha, A., Khapra, M. M., Anbil Parthipan, S. C., Rajendran, J., & Cho, K. (2016, December). A Correlational Encoder Decoder Architecture for Pivot Based Sequence Generation [Paper]. 26th International Conference on Computational Linguistics (COLING 2016), Osaka, Japan. External link
Vaithilingam Sudhakar, A., Nekoei, H., Reymond, M., Rajendran, J., Liu, M., & Anbil Parthipan, S. C. (2025, April). A generalist hanabi agent [Paper]. 13th International Conference on Learning Representations (ICLR 2025), Singapore, Singapore. External link
Wan, Y., Rahimi-Kalahroudi, A., Rajendran, J., Momennejad, I., Anbil Parthipan, S. C., & van Seijen, H. (2022, July). Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods [Paper]. 39th International Conference on Machine Learning (ICML 2022), Baltimore, MD. External link
Zhao, X., Pan, Y., Xiao, C., Anbil Parthipan, S. C., & Rajendran, J. (2023, July). Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement Learning [Paper]. 39th Conference on Uncertainty in Artificial Intelligence (UAI 2023), Pittsburgh, PA, USA. External link
