Kumar, Ramana

Reward tampering problems and solutions in reinforcement learning: a causal influence diagram perspective

Everitt, T., Hutter, M., Kumar, R., & Krakovna, V. (2021) · Synthese

Reinforcement Learning in RoboticsEthics and Social Impacts of AI

Cited by 80

Penalizing side effects using stepwise relative reachability

Krakovna, V., Orseau, L., Kumar, R., Martic, M., & Legg, S. (2018) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 26

Solving math word problems with process- and outcome-based feedback

Uesato, J., Kushman, N., Kumar, R., Song, F., Siegel, N., Wang, L., Creswell, A., Irving, G., & Higgins, I. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 25

Discovering agents

Kenton, Z., Kumar, R., Farquhar, S., Richens, J., MacDermott, M., & Everitt, T. (2023) · Artificial Intelligence

Bayesian Modeling and Causal InferenceLogic, Reasoning, and Knowledge

Cited by 17

Goal Misgeneralization: Why Correct Specifications Aren't Enough For Correct Goals

Shah, R., Varma, V., Kumar, R., Phuong, M., Krakovna, V., Uesato, J., & Kenton, Z. (2022) · arXiv

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 16

Evaluating Frontier Models for Dangerous Capabilities

Phuong, M., Aitchison, M., Catt, E., Cogan, S., Kaskasoli, A., Krakovna, V., Lindner, D., Rahtz, M., Assael, Y., Hodkinson, S., et al. (2024) · arXiv

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 11

Discovering Agents

Kenton, Z., Kumar, R., Farquhar, S., Richens, J., MacDermott, M., & Everitt, T. (2022) · arXiv

Bayesian Modeling and Causal InferenceLogic, Reasoning, and Knowledge

Cited by 7