Imitation learning (IL) is a popular paradigm for training policies in robotic systems when specifying the reward function is difficult. However, despite the success of IL algorithms, they impose the somewhat unrealistic requirement that the expert demonstrations must come from the same domain in which a new imitator policy is to be learned. We consider a practical setting, where (i) state-only expert demonstrations from the real (deployment) environment are given to the learner, (ii) the imitation learner policy is trained in a simulation (training) environment whose transition dynamics is slightly different from the real environment, and (iii) the learner does not have any access to the real environment during the training phase beyond the batch of demonstrations given. Most of the current IL methods, such as generative adversarial imitation learning and its state-only variants, fail to imitate the optimal expert behavior under the above setting. By leveraging insights from the Robust reinforcement learning (RL) literature and building on recent adversarial imitation approaches, we propose a robust IL algorithm to learn policies that can effectively transfer to the real environment without fine-tuning. Furthermore, we empirically demonstrate on continuous-control benchmarks that our method outperforms the state-of-the-art state-only IL method in terms of the zero-shot transfer performance in the real environment and robust performance under different testing conditions.
Misspecification-robust Sequential Neural Likelihood for Simulation-based Inference
School of Mathematical Sciences（数学科学学院）；Centre for Data Science（数据科学中心）；Queensland University of Technology（昆士兰科技大学）；Institute of Operations Research and Analytics（运营研究与分析研究所）；National University of Singapore（新加坡国立大学）；Department of Econometrics and Business Statistics（计量经济学与商务统计系）；Monash University（莫纳什大学）
Robust Predictive Uncertainty and Double Descent in Contaminated Bayesian Random Features
The University of Manchester, UK（英国曼彻斯特大学）；University of Pisa, Italy（意大利比萨大学）；Nanyang Technological University, Singapore（新加坡南洋理工大学）；Max Planck Institute for Mathematics in the Sciences, Germany（德国马克斯·普朗克数学研究所）
Misspecification-robust amortised simulation-based inference using variational methods
Institute of Astronomy, University of Cambridge（天文研究所，剑桥大学）；Statistical Laboratory, University of Cambridge（统计实验室，剑桥大学）；Kavli Institute for Cosmology, University of Cambridge（宇宙学研究所，剑桥大学）；Institute of Astrophysics, FORTH（天体物理学研究所，福蒂斯研究所）
Fast and Robust Rank Aggregation against Model Misspecification
Center for Frontier AI Research（前沿人工智能研究中心）；Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局）；Australian Artificial Intelligence Institute（澳大利亚人工智能研究所）；University of Technology Sydney（悉尼科技大学）；Zhijiang College（之江学院）；Zhejiang University of Technology（浙江工业大学）；Center for Advanced Intelligence Project（高级智能项目中心）；RIKEN（理化学研究所）；Graduate School of Frontier Sciences（前沿科学研究生院）；University of Tokyo（东京大学）
Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Framework
University of Warwick, Department of Computer Science, Coventry, United Kingdom（沃里克大学计算机科学系）；University of Warwick, Department of Statistics, Coventry, United Kingdom（沃里克大学统计系）
Dynamic treatment effects: high-dimensional inference under model misspecification
Institute of Statistics and Big Data, Renmin University of China（中国人民大学统计与大数据研究院）；School of Statistics and Management, Shanghai University of Finance and Economics（上海财经大学统计与管理学院）；University of California, San Diego（加利福尼亚大学圣迭戈分校）
Simulation-based Bayesian inference under model misspecification
School of Mathematical Sciences, Queensland University of Technology（昆士兰科技大学数学科学学院）；Centre for Data Science, Queensland University of Technology（昆士兰科技大学数据科学中心）；Department of Econometrics and Business Statistics, Monash University（莫纳什大学计量经济学与商业统计系）；Department of Statistics and Data Science, National University of Singapore（新加坡国立大学统计与数据科学系）；Institute for Adaptive & Neural Computation, University of Edinburgh（爱丁堡大学自适应与神经计算研究所）；Centre of Excellence for the Mathematical Analysis of Cellular Systems, Queensland University of Technology（昆士兰科技大学细胞系统数学分析卓越中心）
Department of Statistics（统计系）；LSE London, UK（伦敦大学学院）；Department of Mathematics（数学系）；Tsinghua University（清华大学）；School of Design LCC, UAL London, UK（伦敦艺术大学设计学院）；Department of Engineering Science（工程科学系）；University of Oxford（牛津大学）
