Bachrach, Yoram

Personality and patterns of Facebook usage

Bachrach, Y., Kosinski, M., Graepel, T., Kohli, P., & Stillwell, D. (2012) · Proceedings of the 4th Annual ACM Web Science Conference

Personality Traits and PsychologyPrivacy, Security, and Data Protection

Cited by 380

Cooperative AI: machines must learn to find common ground

Dafoe, A., Bachrach, Y., Hadfield, G., Horvitz, E., Larson, K., & Graepel, T. (2021) · Nature

Ethics and Social Impacts of AIMulti-Agent Systems and Negotiation

Cited by 232

Studying user income through language, behaviour and affect in social media

Preoţiuc-Pietro, D., Volkova, S., Lampos, V., Bachrach, Y., & Aletras, N. (2015) · PLOS ONE

Authorship Attribution and ProfilingSentiment Analysis and Opinion Mining

Cited by 219

Open Problems in Cooperative AI

Dafoe, A., Hughes, E., Bachrach, Y., Collins, T., McKee, K. R., Leibo, J. Z., Larson, K., & Graepel, T. (2020) · arXiv

Multi-Agent Systems and NegotiationGame Theory and Voting Systems

Cited by 93

Open-ended Learning in Symmetric Zero-sum Games

Balduzzi, D., Garnelo, M., Bachrach, Y., Czarnecki, W. M., Perolat, J., Jaderberg, M., & Graepel, T. (2019) · arXiv

Artificial Intelligence in GamesEvolutionary Algorithms and Applications

Cited by 88

Biases for Emergent Communication in Multi-agent Reinforcement Learning

Eccles, T., Bachrach, Y., Lever, G., Lazaridou, A., & Graepel, T. (2019) · arXiv

Reinforcement Learning in RoboticsLanguage and cultural evolution

Cited by 42

Embodied AI Agents: Modeling the World

Fung, P., Bachrach, Y., Celikyilmaz, A., Chaudhuri, K., Chen, D., Chung, W., Dupoux, E., Gong, H., Jégou, H., Lazaric, A., et al. (2025) · arXiv

Social Robot Interaction and HRIAI-based Problem Solving and Planning

Cited by 29

MLGym: A New Framework and Benchmark for Advancing AI Research Agents

Nathani, D., Madaan, L., Roberts, N., Bashlykov, N., Menon, A., Moens, V., Budhiraja, A., Magka, D., Vorotilov, V., Chaurasia, G., et al. (2025) · arXiv

Artificial Intelligence ApplicationsReinforcement Learning in Robotics

Cited by 15

AIRA_2: Overcoming Bottlenecks in AI Research Agents

Hambardzumyan, K., Baldwin, N., Toledo, E., Hazra, R., Kuchnik, M., Omari, B. A., Foster, T. S., Protopopov, A., Gagnon-Audet, J.-C., Mediratta, I., et al. (2026) · arXiv

Artificial Intelligence ApplicationsScientific Computing and Data Management

Cited by 1

AI Research Agents for Machine Learning: Search, Exploration, and Generalization in MLE-bench

Toledo, E., Hambardzumyan, K., Josifoski, M., Hazra, R., Baldwin, N., Audran-Reiss, A., Kuchnik, M., Magka, D., Jiang, M., Lupidi, A. M., et al. (2025) · arXiv

Machine Learning and Data ClassificationEvolutionary Algorithms and Applications

Cited by 0

APRES: An Agentic Paper Revision and Evaluation System

Zhao, B., Zhang, J., Whitehouse, C., Jiang, M., Shvartsman, M., Charnalia, A., Magka, D., Shavrina, T., Dunfield, D., Mac Aodha, O., et al. (2026) · arXiv

Academic Writing and Publishingscientometrics and bibliometrics research

Cited by 0