publications

publications by categories in reversed chronological order.

2022

    2021

    1. Amortized Rejection Sampling in Universal Probabilistic Programming
      Naderiparizi, Saeid, Scibior, Adam, Munk, Andreas, Ghadiri, Mehrdad, Baydin, Atilim Gunes, Gram-Hansen, Bradley, Witt, Christian Schroeder, Zinkov, Robert, Torr, Philip H. S., Rainforth, Tom, Teh, Yee Whye, and Wood, Frank
      AISTATS 2021 2021
    2. Revealing Robust Oil and Gas Company Macro-Strategies Using Deep Multi-Agent Reinforcement Learning
      Radovic, Dylan, Kruitwagen, Lucas, Witt, Christian, Caldecott, Ben, Tomlinson, Shane, and Workman, Mark
      SSRN 2021
    3. Fixed Points in Cyber Space: Rethinking Optimal Evasion Attacks in the Age of AI-NIDS
      Witt, Christian Schroeder, Huang, Yongchao, Torr, Philip H. S., and Strohmeier, Martin
      arXiv:2111.12197 [cs] Nov 2021
    4. Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning
      Iqbal, Shariq, Witt, Christian A. Schroeder De, Peng, Bei, Boehmer, Wendelin, Whiteson, Shimon, and Sha, Fei
      In Proceedings of the 38th International Conference on Machine Learning Jul 2021

    2020

    1. Simulation-Based Inference for Global Health Decisions
      Witt, Christian, Gram-Hansen, Bradley, Nardelli, Nantas, Gambardella, Andrew, Zinkov, Rob, Dokania, Puneet, Siddharth, N., Espinosa-Gonzalez, Ana Belen, Darzi, Ara, Torr, Philip, and Baydin, Atilim Gunes
      ML for Global Health Workshop at ICML 2002 Jul 2020
    2. FACMAC: Factored Multi-Agent Centralised Policy Gradients
      Peng, Bei, Rashid, Tabish, Witt, C. S. D., Kamienny, Pierre-Alexandre, Torr, Philip H. S., Bohmer, Wendelin, and Whiteson, S.
      In Advances in Neural Information Processing Systems Jul 2020
    3. Deep Multi-Agent Reinforcement Learning for Decentralized Continuous Cooperative Control
      Witt, Christian, Peng, Bei, Kamienny, Pierre-Alexandre, Torr, Philip, Bohmer, Wendelin, and Whiteson, Shimon
      arXiv:2003.06709v4 [cs.LG] Mar 2020
    4. Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?
      Witt, Christian Schroeder, Gupta, Tarun, Makoviichuk, Denys, Makoviychuk, Viktor, Torr, Philip H. S., Sun, Mingfei, and Whiteson, Shimon
      arXiv:2011.09533 [cs] Nov 2020
    5. Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
      Rashid, Tabish, Samvelyan, Mikayel, Schroeder de Witt, Christian, , Farquhar, Gregory, Foerster, Jakob, and Whiteson, Shimon
      Journal of Machine Learning Research Nov 2020

    2019

    1. Effective Approximate Inference for Nested Simulators
      Gram-Hansen, Bradley, Golinski, Adam, Witt, Christian, Naderiparizi, Saeid, Scibior, Adam, Munk, Andreas, Wood, Frank, Torr, Philip, Teh, Yee Whye, Baydin, Atilim Gunes, and Rainforth, Tom
      AABI 2019 Nov 2019
    2. Hijacking Malaria Simulators with Probabilistic Programming
      Gram-Hansen, Bradley, Witt, Christian, Rainforth, Tom, Torr, Philip, Teh, Yee Whye, and Baydin, Atilim Gunes
      AI for Social Good Workshop at ICML 2019 Nov 2019
    3. The StarCraft Multi-Agent Challenge
      Samvelyan, Mikayel, Rashid, Tabish, Witt, Christian Schroeder, Farquhar, Gregory, Nardelli, Nantas, Rudner, Tim G. J., Hung, Chia-Man, Torr, Philip H. S., Foerster, Jakob, and Whiteson, Shimon
      arXiv:1902.04043 [cs, stat] Dec 2019
    4. Multi-Agent Common Knowledge Reinforcement Learning
      Schroeder de Witt*, Christian, Foerster*, Jakob, Farquhar, Gregory, Torr, Philip, Boehmer, Wendelin, and Whiteson, Shimon
      In Advances in Neural Information Processing Systems Dec 2019

    2018

      2017

        2016

          2015

            2014