More Like this

1. Causal Markov Blanket Representation Learning for Out-of-distribution Generalization

   Yin, Naiyu; Wang, Hanjing; Gao, Tia; Dhurandhar, Amit; Ji, Qiang (December 2023, NeurIPS Workshop: Causal Representation Learning, 2023)

   The pursuit of generalizable representations in the realm of machine learning and computer vision is a dynamic field of research. Typically, current methods aim to secure invariant representations by either harnessing domain expertise or leveraging data from multiple domains. In this paper, we introduce a novel approach that involves acquiring Causal Markov Blanket (CMB) representations to improve prediction performance in the face of distribution shifts. Causal Markov Blanket representations comprise the direct causes and effects of the target variable, rendering them invariant across diverse domains. To elaborate, our approach commences with the introduction of a novel structural causal model (SCM) equipped with latent representations, designed to capture the underlying causal mechanisms governing the data generation process. Subsequently, we propose a CMB representation learning framework that derives representations conforming to the proposed SCM. In comparison to state-of-the-art domain generalization methods, our approach exhibits robustness and adaptability under distribution shifts 

   more » « less
2. Ensemble Pruning for Out-of-distribution Generalization

   Qiao, Fengchun; Peng, Xi (September 2024, International Conference on Machine Learning)
3. Time-Series Forecasting for Out-of-Distribution Generalization Using Invariant Learning

   Liu, H; Kamarthi, H; Kong, L; Zhao, Z; Zhang, C; Prakash, B A (July 2024, International Conference on Machine Learning)
4. Time-Series Forecasting for Out-of-Distribution Generalization Using Invariant Learning

   Liu, Haoxin; Kamarthi, Harshavardhan; Kong, Lingkai; Zhao, Zhiyuan; Zhang, Chao; Prakash, B Aditya (July 2024, PMLR (Proceedings of ICML))
5. Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization

   https://doi.org/10.1109/ICDM59182.2024.00108  

   Wang, Song; Yang, Xiaodong; Islam, Rashidul; Chen, Huiyuan; Xu, Minghua; Li, Jundong; Cai, Yiwei (December 2024, IEEE)

   To deal with distribution shifts in graph data, various graph out-of-distribution (OOD) generalization techniques have been recently proposed. These methods often employ a two-step strategy that first creates augmented environments and subsequently identifies invariant subgraphs to improve generalizability. Nevertheless, this approach could be suboptimal from the perspective of consistency. First, the process of augmenting environments by altering the graphs while preserving labels may lead to graphs that are not realistic or meaningfully related to the origin distribution, thus lacking distribution consistency. Second, the extracted subgraphs are obtained from directly modifying graphs, and may not necessarily maintain a consistent predictive relationship with their labels, thereby impacting label consistency. In response to these challenges, we introduce an innovative approach that aims to enhance these two types of consistency for graph OOD generalization. We propose a modifier to obtain both augmented and invariant graphs in a unified manner. With the augmented graphs, we enrich the training data without compromising the integrity of label-graph relationships. The label consistency enhancement in our framework further preserves the supervision information in the invariant graph. We conduct extensive experiments on real-world datasets to demonstrate the superiority of our framework over other state-of-the-art baselines. 

   more » « less