28 citations · 43 across the 15 of their papers we have counts for
16 papers
APE: Agentic Prompt Enhancer for Image Generation and Editing
Zijian Huang, Jay Zhangjie Wu, Zian Wang +5
Natural language has become a powerful interface for image generation and editing, yet text-guided visual systems remain highly sensitive to prompt formulation. Semantically simila…
Beyond Trajectory Rewards: Step-level Credit Assignment for Agentic Search via Graph Modeling
Yuchen Liu, Yingjie Feng, Lixiong Qin +5
In Agentic Search, trajectory-level outcome rewards fail to quantify the behavioral contributions of individual steps, while existing step-level reward methods typically rely on co…
VSPO: Vector-Steered Policy Optimization for Behavioral Control
Xuechen Zhang, Zijian Huang, Kai Yang +3
Modern language models often need to optimize a primary accuracy objective while also accommodating secondary behavioral preferences, such as verbosity, agreeableness, or the level…
OpenG2G: A Simulation Platform for AI Datacenter-Grid Runtime Coordination
Jae-Won Chung, Zhirui Liang, Yanyong Mao +3
AI's growing compute demand and new datacenter buildouts present major capacity and reliability challenges for the electricity grid, leading to multi-year interconnection delays fo…
SmartChunk Retrieval: Query-Aware Chunk Compression with Planning for Efficient Document RAG
Xuechen Zhang, Koustava Goswami, Samet Oymak +2
Retrieval-augmented generation (RAG) has strong potential for producing accurate and factual outputs by combining language models (LMs) with evidence retrieved from large text corp…
GPU-to-Grid: Voltage Regulation via GPU Utilization Control
Zhirui Liang, Jae-Won Chung, Mosharaf Chowdhury +2
While the rapid expansion of data centers poses challenges for power grids, it also offers new opportunities as flexible loads. Existing power system research often abstracts data…
BREAD: Branched Rollouts from Expert Anchors Bridge SFT & RL for Reasoning
Xuechen Zhang, Zijian Huang, Yingcong Li +3
Small language models (SLMs) struggle to learn complex reasoning behaviors, especially when high-quality traces are scarce or difficult to learn from. The standard training approac…
Making Small Language Models Efficient Reasoners: Intervention, Supervision, Reinforcement
Xuechen Zhang, Zijian Huang, Chenshun Ni +3
Recent research enhances language model reasoning by scaling test-time compute via longer chain-of-thought traces. This often improves accuracy but also introduces redundancy and h…
Selective Attention: Enhancing Transformer through Principled Context Control
Xuechen Zhang, Xiangyu Chang, Mingchen Li +3
The attention mechanism within the transformer architecture enables the model to weigh and combine tokens based on their relevance to the query. While self-attention has enjoyed ma…
Efficient Contextual LLM Cascades through Budget-Constrained Policy Learning
Xuechen Zhang, Zijian Huang, Ege Onur Taga +3
Recent successes in natural language processing have led to the proliferation of large language models (LLMs) by multiple providers. Each LLM offering has different inference accur…
Class-attribute Priors: Adapting Optimization to Heterogeneity and Fairness Objective
Xuechen Zhang, Mingchen Li, Jiasi Chen +2
Modern classification problems exhibit heterogeneities across individual classes: Each class may have unique attributes, such as sample size, label quality, or predictability (easy…
FedYolo: Augmenting Federated Learning with Pretrained Transformers
Xuechen Zhang, Mingchen Li, Xiangyu Chang +4
The growth and diversity of machine learning applications motivate a rethinking of learning with mobile and edge devices. How can we address diverse client goals and learn with sca…
AutoBalance: Optimized Loss Functions for Imbalanced Data
Mingchen Li, Xuechen Zhang, Christos Thrampoulidis +2
Imbalanced datasets are commonplace in modern machine learning problems. The presence of under-represented classes or groups with sensitive attributes results in concerns about gen…
Post-hoc Models for Performance Estimation of Machine Learning Inference
Xuechen Zhang, Samet Oymak, Jiasi Chen
Estimating how well a machine learning model performs during inference is critical in a variety of scenarios (for example, to quantify uncertainty, or to choose from a library of a…
On the Role of Dataset Quality and Heterogeneity in Model Confidence
Yuan Zhao, Jiasi Chen, Samet Oymak
Safety-critical applications require machine learning models that output accurate and calibrated probabilities. While uncalibrated deep networks are known to make over-confident pr…
Learning Feature Nonlinearities with Non-Convex Regularized Binned Regression
Samet Oymak, Mehrdad Mahdavi, Jiasi Chen
For various applications, the relations between the dependent and independent variables are highly nonlinear. Consequently, for large scale complex problems, neural networks and re…