We present methods for online linear optimization that take advantage of benign (as opposed to worst-case) sequences. Specifically if the sequence encountered by the learner is described well by a known "predictable process", the algorithms presented enjoy tighter bounds as compared to the typical worst case bounds. Additionally, the methods achieve the usual worst-case regret bounds if the sequence is not benign. Our approach can be seen as a way of adding prior knowledge about the sequence within the paradigm of online learning. The setting is shown to encompass partial and side information. Variance and path-length bounds can be seen as particular examples of online learning with simple predictable sequences.
We further extend our methods and results to include competing with a set of possible predictable processes (models), that is "learning" the predictable process itself concurrently with using it to obtain better regret guarantees. We show that such model selection is possible under various assumptions on the available feedback. Our results suggest a promising direction of further research with potential applications to stock market and time series prediction.
Online monotone density estimation and log-optimal calibration
Department of Statistics and Data Science, Carnegie Mellon University, USA（统计与数据科学系，卡内基梅隆大学，美国）；Department of Statistics and Actuarial Science, University of Waterloo, Canada（统计与精算科学系，滑铁卢大学，加拿大）
Optimizing the Preconditioner: A Black-box Online-to-Nonconvex Conversion with Static Regret Minimization Oracles
Center for Computational Science and Engineering, MIT（麻省理工学院计算科学与工程中心）；Department of Civil and Environmental Engineering, MIT（麻省理工学院土木与环境工程系）；Institute for Data, Systems, and Society, MIT（麻省理工学院数据、系统与社会研究所）
Regret Lower Bound for Online Learning of Quantum States with Logarithmic Loss, with Applications to Quantum State Tomography and Confidence Sequences for Matrix Means
National Taiwan University（台湾大学）；Center for Quantum Science and Engineering, National Taiwan University（台湾大学量子科学与工程中心）
Improving the Performance of Online Neural Transducer Models
Google, Inc.（谷歌公司）
拥有一个可以在线方式操作的序列到序列模型对于语音搜索等流媒体应用非常重要。神经传感器是一种流式序列到序列模型，但与非流式模型（如Listen，Attend and Spell（LAS））相比，其性能显着下降。在本文中，我们提出了各种改进NT。具体来说，我们着眼于增加NT计算注意力的窗口，主要是通过回顾时间，使模型仍然保持在线。此外，我们探索从LAS训练的模型初始化NT模型，以便更好地对齐。最后，我们探索包括更强的语言模型，如使用词段模型，并在束搜索期间应用外部LM。在语音搜索任务中，我们发现通过这些改进，我们可以让NT匹配LAS的性能。
