Schahram Dustdar — accepted AI-conference papers

2026

On-Device Realistic Test-Time Adaptation via Bias-Resistant Statistical Alignment

Haojie Bai, Aiguo Chen, Ruiting Dai, Yijia Rong, Zirui Wang, Jiaxin Liu +2

IJCAI 2026

Test-Time Adaptation (TTA) aims to adapt pretrained models to unseen test data, which is crucial for resource-constrained edge devices that must handle distribution shifts on the fly without human supervision. However, conventional TTA methods often fail in realistic scenarios characterized by conti

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2022

Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting

Shizhan Liu, Hang Yu, Cong Liao, Jianguo Li, Weiyao Lin, Alex X. Liu +1

ICLR 2022oral

Accurate prediction of the future given the past based on time series data is of paramount importance, since it opens the door for decision making and risk management ahead of time. In practice, the challenge is to build a flexible but parsimonious model that can capture a wide range of temporal dep…

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