4 citations · 4 across the 9 of their papers we have counts for
10 papers
Difficulty-Estimated Policy Optimization
Yu Zhao, Fan Jiang, Tianle Liu +4
Recent advancements in Large Reasoning Models (LRMs), exemplified by DeepSeek-R1, have underscored the potential of scaling inference-time compute through Group Relative Policy Opt…
A Looming of phantoms
Kimoi Kemboi, Daniel Krashen, Tianle Liu +6
Following Krah's method, we construct new examples of phantom categories as semiorthogonal components of the derived categories of two types of rational surfaces: the blowup of the…
LADY: Linear Attention for Autonomous Driving Efficiency without Transformers
Jihao Huang, Xi Xia, Zhiyuan Li +4
End-to-end autonomous driving has emerged as a promising paradigm. However, state-of-the-art methods rely heavily on Transformer architectures. The inherent quadratic complexity of…
Generative AI for subgrid turbulence in large-eddy simulations
Yu Cheng, Tianle Liu
Turbulence governs the transport of momentum, energy, and scalars in many geophysical and engineering flows. In large-eddy simulations (LES), parameterizing subgrid-scale (SGS) str…
Test-Time Discovery via Hashing Memory
Fan Lyu, Tianle Liu, Zhang Zhang +2
We introduce Test-Time Discovery (TTD) as a novel task that addresses class shifts during testing, requiring models to simultaneously identify emerging categories while preserving…
Poisson-Process Topic Model for Integrating Knowledge from Pre-trained Language Models
Morgane Austern, Yuanchuan Guo, Zheng Tracy Ke +1
Topic modeling is traditionally applied to word counts without accounting for the context in which words appear. Recent advancements in large language models (LLMs) offer contextua…
A Heavily Right Strategy for Statistical Inference with Dependent Studies in Arbitrary Dimensions
Tianle Liu, Xiao-Li Meng, Natesh S. Pillai
We leverage recent advances in heavy-tail approximations for global hypothesis testing with dependent studies to construct approximate confidence regions without modeling or estima…
Measuring and Controlling Instruction (In)Stability in Language Model Dialogs
Kenneth Li, Tianle Liu, Naomi Bashkansky +4
System-prompting is a standard tool for customizing language-model chatbots, enabling them to follow a specific instruction. An implicit assumption in the use of system prompts is…
Wasserstein-p Bounds via Cumulant-Based Edgeworth Expansion for -Mixing Random Fields
Tianle Liu, Morgane Austern
We establish normal-approximation bounds in the Wasserstein- distance for -mixing random fields, extending results previously available mainly for independent or locally depe…
Wasserstein-p Bounds in the Central Limit Theorem Under Weak Dependence
Tianle Liu, Morgane Austern
The central limit theorem is one of the most fundamental results in probability and has been successfully extended to locally dependent data and strongly-mixing random fields. In t…