Decisions made by machine learning systems have increasing influence on the world, yet it is common for machine learning algorithms to assume that no such influence exists. An example is the use of the i.i.d. assumption in content recommendation. In fact, the (choice of) content displayed can change users' perceptions and preferences, or even drive them away, causing a shift in the distribution of users. We introduce the term auto-induced distributional shift (ADS) to describe the phenomenon of an algorithm causing a change in the distribution of its own inputs. Our goal is to ensure that machine learning systems do not leverage ADS to increase performance when doing so could be undesirable. We demonstrate that changes to the learning algorithm, such as the introduction of meta-learning, can cause hidden incentives for auto-induced distributional shift (HI-ADS) to be revealed. To address this issue, we introduce `unit tests' and a mitigation strategy for HI-ADS, as well as a toy environment for modelling real-world issues with HI-ADS in content recommendation, where we demonstrate that strong meta-learners achieve gains in performance via ADS. We show meta-learning and Q-learning both sometimes fail unit tests, but pass when using our mitigation strategy.
Distributional Soft Actor-Critic with Harmonic Gradient for Safe and Efficient Autonomous Driving in Multi-lane Scenarios
School of Vehicle and Mobility, Tsinghua University（清华大学车辆与移动系统学院）；School of Mechanical Engineering, University of Science and Technology Beijing（北京科技大学机械工程学院）；College of Artificial Intelligence, Tsinghua University（清华大学人工智能学院）
Distributionally Robust Imitation Learning: Layered Control Architecture for Certifiable Autonomy
Department of Mechanical Science and Engineering, University of Illinois Urbana-Champaign（机械科学与工程系，伊利诺伊大学厄巴纳-香槟分校）；Department of Electrical and Systems Engineering, University of Pennsylvania（电气与系统工程系，宾夕法尼亚大学）；Mechanical and Civil Engineering, Control and Dynamical Systems, California Institute of Technology（机械与土木工程、控制与动态系统，加州理工学院）；Massachusetts Institute of Technology（麻省理工学院）；Advanced Technology Labs, Lockheed Martin（洛克希德·马丁高级技术实验室）
Wasserstein multivariate auto-regressive models for modeling distributional time series
Université Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK（格勒诺布尔阿尔卑斯大学、国家科学研究中心、法国国家信息与自动化研究所、格勒诺布尔INP、LJK研究中心）；Institut de Mathématiques de Bordeaux, Université de Bordeaux（波尔多数学研究所、波尔多大学）
