We study a theoretical and algorithmic framework for structured prediction in
the online learning setting. The problem of structured prediction, i.e.
estimating function where the output space lacks a vectorial structure, is well
studied in the literature of supervised statistical learning. We show that our
algorithm is a generalisation of optimal algorithms from the supervised
learning setting, and achieves the same excess risk upper bound also when data
are not i.i.d. Moreover, we consider a second algorithm designed especially for
non-stationary data distributions, including adversarial data. We bound its
stochastic regret in function of the variation of the data distributions.

在线学习环境下的结构化预测问题的理论和算法性框架的研究。通过研究，我们发现我们的算法能够推广到监督学习环境中的优化算法，并且在数据非独立同分布的情况下也能达到相同的风险上界。此外，我们还考虑了一种特别设计用于非平稳数据分布（包括对抗性数据）的第二个算法，并以数据分布的变化为函数界限其随机遗憾。

在线学习中的结构化预测

Structured Prediction in Online Learning

Self-supervised learning (SSL) aims to eliminate one of the major bottlenecks
in representation learning - the need for human annotations. As a result, SSL
holds the promise to learn representations from data in-the-wild, i.e., without
the need for finite and static datasets. Instead, true SSL algorithms should be
able to exploit the continuous stream of data being generated on the internet
or by agents exploring their environments. But do traditional self-supervised
learning approaches work in this setup? In this work, we investigate this
question by conducting experiments on the continuous self-supervised learning
problem. While learning in the wild, we expect to see a continuous (infinite)
non-IID data stream that follows a non-stationary distribution of visual
concepts. The goal is to learn a representation that can be robust, adaptive
yet not forgetful of concepts seen in the past. We show that a direct
application of current methods to such continuous setup is 1) inefficient both
computationally and in the amount of data required, 2) leads to inferior
representations due to temporal correlations (non-IID data) in some sources of
streaming data and 3) exhibits signs of catastrophic forgetting when trained on
sources with non-stationary data distributions. We propose the use of replay
buffers as an approach to alleviate the issues of inefficiency and temporal
correlations. We further propose a novel method to enhance the replay buffer by
maintaining the least redundant samples. Minimum redundancy (MinRed) buffers
allow us to learn effective representations even in the most challenging
streaming scenarios composed of sequential visual data obtained from a single
embodied agent, and alleviates the problem of catastrophic forgetting when
learning from data with non-stationary semantic distributions.

本文研究了自我监督学习在连续流数据中的应用及其效率，提出了重放缓存与最小冗余样本的方法来增强学习。实验结果表明，这些方法可以有效提高表示学习的精度和鲁棒性，在非平稳的语义分布下也不容易出现灾难性遗忘。