5 papers
Revisiting Lossy Verification in Speculative Decoding: Mechanisms, Trade-offs, and Failure Modes
Tianyu Wang, Yuxuan Zhou, Heng Li +4
Speculative Decoding (SD) accelerates large language model inference by allowing a lightweight draft model to propose tokens that are subsequently verified in parallel by a larger…
Overcoming Joint Intractability with Lossless Hierarchical Speculative Decoding
Yuxuan Zhou, Fei Huang, Heng Li +5
Verification is a key bottleneck in improving inference speed while maintaining distribution fidelity in Speculative Decoding. Recent work has shown that sequence-level verificatio…
OneTrans: Unified Feature Interaction and Sequence Modeling with One Transformer in Industrial Recommender
Zhaoqi Zhang, Haolei Pei, Jun Guo +5
In recommendation systems, scaling up feature-interaction modules (e.g., Wukong, RankMixer) or user-behavior sequence modules (e.g., LONGER) has achieved notable success. However,…
DRO: A Python Library for Distributionally Robust Optimization in Machine Learning
Jiashuo Liu, Tianyu Wang, Henry Lam +2
We introduce dro, an open-source Python library for distributionally robust optimization (DRO) for regression and classification problems. The library implements 14 DRO formulation…
Geometry-Calibrated DRO: Combating Over-Pessimism with Free Energy Implications
Jiashuo Liu, Jiayun Wu, Tianyu Wang +3
Machine learning algorithms minimizing average risk are susceptible to distributional shifts. Distributionally Robust Optimization (DRO) addresses this issue by optimizing the wors…