Yarats, Denis

Convolutional Sequence to Sequence Learning

Gehring, J., Auli, M., Grangier, D., Yarats, D., & Dauphin, Y. N. (2017) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 2,850

Deal or No Deal? End-to-End Learning of Negotiation Dialogues

Lewis, M., Yarats, D., Dauphin, Y., Parikh, D., & Batra, D. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Speech and dialogue systemsMulti-Agent Systems and Negotiation

Cited by 347

Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels

Kostrikov, I., Yarats, D., & Fergus, R. (2020) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 267

Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning

Yarats, D., Fergus, R., Lazaric, A., & Pinto, L. (2021) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 89

DRACO: a Cross-Domain Benchmark for Deep Research Accuracy, Completeness, and Objectivity

Zhong, J., Zhang, H., Southern, C., Yang, J., Wang, T., Jung, K., Zhang, S., Yarats, D., Ho, J., & Ma, J. (2026) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 0

WANDR: A Benchmark for Wide and Deep Research

Polshkov, V., Pitera, M., Yang, J., Priemko, K., Gaiduk, M., Nikolenko, A., Bykov, D., Southern, C., Yarats, D., & Ma, J. (2026) · arXiv

Web Data Mining and AnalysisInformation Retrieval and Search Behavior

Cited by 0