"Towards Effective AI Support for Developers: A Survey of Desires and Concerns," co-authored with Mansi Khemka, explores developers' perspectives on AI integration in their workflows. This study prioritizes developers' voices, revealing their top desires for AI assistance and major concerns. By conducting a comprehensive survey…
Microsoft is proud to sponsor the 18th USENIX Symposium on Operating Systems Design and Implementation (opens in new tab). OSDI brings together professionals from academic and industrial backgrounds in what has become a premier forum for discussing the design, implementation, and implications of systems software.…
Learning-to-rank (LTR) models rank items based on specific features, aiming to maximize ranking utility by prioritizing highly relevant items. However, optimizing only for ranking utility can lead to representational harm and may fail to address implicit bias in relevance scores. Prior studies introduced algorithms to…
Recent research on time-series self-supervised models shows great promise in learning semantic representations. However, it has been limited to small-scale datasets, e.g., thousands of temporal sequences. In this work, we make key technical contributions that are tailored to the numerical properties of time-series data and…
Learning-to-rank (LTR) models rank items based on specific features, aiming to maximize ranking utility by prioritizing highly relevant items. However, optimizing only for ranking utility can lead to representational harm and may fail to address implicit bias in relevance scores. Prior studies introduced algorithms to…
Learning-to-rank (LTR) models rank items based on specific features, aiming to maximize ranking utility by prioritizing highly relevant items. However, optimizing only for ranking utility can lead to representational harm and may fail to address implicit bias in relevance scores. Prior studies introduced algorithms to…
In large-scale cloud service systems, monitoring metric data and conducting anomaly detection is an important way to maintain reliability and stability. However, great disparity exists between academic approaches and industrial practice to anomaly detection. Industry predominantly uses simple, efficient methods due to better interpretability and…
Serving large language models (LLMs) in production can incur substantial costs, which has prompted recent advances in inference system optimizations. Today, these systems are evaluated against conventional latency and throughput metrics (eg. TTFT, TBT, Normalised Latency and TPOT). However, these metrics fail to fully capture…
In-context learning for vision data has been underexplored compared with that in natural language. Previous works studied image in-context learning, urging models to generate a single image guided by demonstrations. In this paper, we propose and study video in-context learning, where the model starts from…
Controlling the evolution of complex physical systems is a fundamental task across science and engineering. Classical techniques suffer from limited applicability or huge computational costs. On the other hand, recent deep learning and reinforcement learning-based approaches often struggle to optimize long-term control sequences under the…
