Towards event analysis in time-series data: Asynchronous probabilistic models and learning from partial labels

In this thesis, we contribute in two main directions: modeling asynchronous time-series data and learning from partial labelled data. We first propose novel probabilistic frameworks to improve flexibility and expressiveness of current approaches in modeling complex real-world asynchronous event sequence data. Second, we present a scalable approach to end-to-end learn a deep multi-label classifier with partial labels. To evaluate the effectiveness of our proposed frameworks, we focus on visual recognition application, however, our proposed frameworks are generic and can be used in modeling general settings of learning event sequences, and learning multi-label classifiers from partial labels. Visual recognition is a fundamental piece for achieving machine intelligence, and has a wide range of applications such as human activity analysis, autonomous driving, surveillance and security, health-care monitoring, etc. With a wide range of experiments, we show that our proposed approaches help to build more powerful and effective visual recognition frameworks

A Survey on Deep Semi-supervised Learning

Deep semi-supervised learning is a fast-growing field with a range of practical applications. This paper provides a comprehensive survey on both fundamentals and recent advances in deep semi-supervised learning methods from model design perspectives and unsupervised loss functions. We first present a taxonomy for deep semi-supervised learning that categorizes existing methods, including deep generative methods, consistency regularization methods, graph-based methods, pseudo-labeling methods, and hybrid methods. Then we offer a detailed comparison of these methods in terms of the type of losses, contributions, and architecture differences. In addition to the past few years' progress, we further discuss some shortcomings of existing methods and provide some tentative heuristic solutions for solving these open problems.Comment: 24 pages, 6 figure

Learning Calibratable Policies using Programmatic Style-Consistency

We study the important and challenging problem of controllable generation of long-term sequential behaviors. Solutions to this problem would impact many applications, such as calibrating behaviors of AI agents in games or predicting player trajectories in sports. In contrast to the well-studied areas of controllable generation of images, text, and speech, there are significant challenges that are unique to or exacerbated by generating long-term behaviors: how should we specify the factors of variation to control, and how can we ensure that the generated temporal behavior faithfully demonstrates diverse styles? In this paper, we leverage large amounts of raw behavioral data to learn policies that can be calibrated to generate a diverse range of behavior styles (e.g., aggressive versus passive play in sports). Inspired by recent work on leveraging programmatic labeling functions, we present a novel framework that combines imitation learning with data programming to learn style-calibratable policies. Our primary technical contribution is a formal notion of style-consistency as a learning objective, and its integration with conventional imitation learning approaches. We evaluate our framework using demonstrations from professional basketball players and agents in the MuJoCo physics environment, and show that our learned policies can be accurately calibrated to generate interesting behavior styles in both domains