7 citations · 14 across the 9 of their papers we have counts for
9 papers · 1 filter
ReSFlow: Rectifying Categorical Flows on Statistical Manifolds for One-Step Generation
Xiaohuan Jia, Renzhe Xu, Xiao Wang +2
Generative modeling for discrete data has been significantly advanced by learning continuous vector fields on the statistical manifolds to transport simple priors to target discret…
Momentum Streams for Optimizer-Inspired Transformers
Jingchu Gai, Nai-Chieh Huang, Jiayun Wu
The residual update of a pre-norm Transformer layer admits an interpretation as one step of a first-order optimizer acting on a surrogate token energy, wherein the attention and ML…
Mitigating LLM Hallucination via Behaviorally Calibrated Reinforcement Learning
Jiayun Wu, Jiashuo Liu, Zhiyuan Zeng +3
LLM deployment in critical domains is currently impeded by persistent hallucinations--generating plausible but factually incorrect assertions. While scaling laws drove significant…
LLM Swiss Round: Aggregating Multi-Benchmark Performance via Competitive Swiss-System Dynamics
Jiashuo Liu, Jiayun Wu, Chunjie Wu +5
The rapid proliferation of Large Language Models (LLMs) and diverse specialized benchmarks necessitates a shift from fragmented, task-specific metrics to a holistic, competitive ra…
LimiX: Unleashing Structured-Data Modeling Capability for Generalist Intelligence
Xingxuan Zhang, Gang Ren, Han Yu +35
We argue that progress toward general intelligence requires complementary foundation models grounded in language, the physical world, and structured data. This report presents Limi…
Topology-Aware Dynamic Reweighting for Distribution Shifts on Graph
Weihuang Zheng, Jiashuo Liu, Jiaxing Li +3
Graph Neural Networks (GNNs) are widely used for node classification tasks but often fail to generalize when training and test nodes come from different distributions, limiting the…
Bridging Multicalibration and Out-of-distribution Generalization Beyond Covariate Shift
Jiayun Wu, Jiashuo Liu, Peng Cui +1
We establish a new model-agnostic optimization framework for out-of-distribution generalization via multicalibration, a criterion that ensures a predictor is calibrated across a fa…
A Survey on Evaluation of Out-of-Distribution Generalization
Han Yu, Jiashuo Liu, Xingxuan Zhang +2
Machine learning models, while progressively advanced, rely heavily on the IID assumption, which is often unfulfilled in practice due to inevitable distribution shifts. This render…
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…