COPSRO: An Offline Empirical Game Theoretic Method With Conservative Critic.

This article studies how to learn approximate Nash equilibrium (NE) from static historical datasets by empirical game-theoretic analysis (EGTA), which provides a simulation-based framework to model complex multiagent interactions. Generally, EGTA requires plentiful interactions with the environment or simulator to estimate a cogent and tractable game model approximating the underlying game. However, these exploratory interactions often suffer from low data utilization efficiency and may not be feasible in risk-sensitive applications.

Hierarchical Augmentation and Distillation for Class Incremental Audio-Visual Video Recognition.

Audio-visual video recognition (AVVR) integrates audio and visual cues to accurately categorize videos. While current methods using provided datasets achieve satisfactory results, they face challenges in retaining historical class knowledge when new classes appear in real-world situations. There are no dedicated methods to address this issue, prompting this paper to explore Class Incremental Audio-Visual Video Recognition (CIAVVR).

Label Information Guided Graph Construction for Semi-Supervised Learning.

In the literature, most existing graph-based semi-supervised learning methods only use the label information of observed samples in the label propagation stage, while ignoring such valuable information when learning the graph. In this paper, we argue that it is beneficial to consider the label information in the graph learning stage. Specifically, by enforcing the weight of edges between labeled samples of different classes to be zero, we explicitly incorporate the label information into the state-of-the-art graph learning methods, such as the low-rank representation (LRR), and propose a novel semi-supervised graph learning method called semi-supervised low-rank representation.

Constructing a Nonnegative Low-Rank and Sparse Graph With Data-Adaptive Features.

This paper aims at constructing a good graph to discover the intrinsic data structures under a semisupervised learning setting. First, we propose to build a nonnegative low-rank and sparse (referred to as NNLRS) graph for the given data representation. In particular, the weights of edges in the graph are obtained by seeking a nonnegative low-rank and sparse reconstruction coefficients matrix that represents each data sample as a linear combination of others.