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Langevin Monte Carlo for Contextual Bandits.

P. Xu, H. Zheng, E. Mazumdar, K. Azizzadenesheli, and A. Anandkumar. ICML, volume 162 of Proceedings of Machine Learning Research, page 24830-24850. PMLR, (2022)

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Efficient approaches for escaping higher order saddle points in non-convex optimization.A. Anandkumar, and R. Ge. COLT, volume 49 of JMLR Workshop and Conference Proceedings, page 81-102. JMLR.org, (2016)Multi-task learning for electronic structure to predict and explore molecular potential energy surfaces.Z. Qiao, F. Ding, M. Welborn, P. Bygrave, D. Smith, A. Anandkumar, F. Manby, and T. III. CoRR, (2020)Learning Linear Bayesian Networks with Latent VariablesA. Anandkumar, D. Hsu, A. Javanmard, and S. Kakade. CoRR, (2012)Emergent Hand Morphology and Control from Optimizing Robust Grasps of Diverse Objects.X. Pan, A. Garg, A. Anandkumar, and Y. Zhu. CoRR, (2020)Min-min times in peer-to-peer file sharing networks.G. Ezovski, A. Anandkumar, A. Tang, and L. Andrew. Allerton, page 1487-1494. IEEE, (2008)Diffusion Models for Adversarial Purification.W. Nie, B. Guo, Y. Huang, C. Xiao, A. Vahdat, and A. Anandkumar. ICML, volume 162 of Proceedings of Machine Learning Research, page 16805-16827. PMLR, (2022)Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning.A. Mahajan, M. Samvelyan, L. Mao, V. Makoviychuk, A. Garg, J. Kossaifi, S. Whiteson, Y. Zhu, and A. Anandkumar. ICML, volume 139 of Proceedings of Machine Learning Research, page 7301-7312. PMLR, (2021)Detection error exponent for spatially dependent samples in random networks.A. Anandkumar, A. Willsky, and L. Tong. ISIT, page 2882-2886. IEEE, (2009)Cost-performance tradeoff in multi-hop aggregation for statistical inference.A. Anandkumar, L. Tong, A. Swami, and A. Ephremides. ISIT, page 662-666. IEEE, (2008)Distributed Statistical Inference using Type Based Random Access over Multi-access Fading Channels.A. Anandkumar, and L. Tong. CISS, page 38-43. IEEE, (2006)

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Langevin Monte Carlo for Contextual Bandits.

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Disambiguation of "Anandkumar, Animashree"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Langevin Monte Carlo for Contextual Bandits.

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Langevin Monte Carlo for Contextual Bandits.