28 citations · 37 across the 16 of their papers we have counts for
17 papers
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…
Enabling Intrinsic Reasoning over Dense Geospatial Embeddings with DFR-Gemma
Xuechen Zhang, Aviv Slobodkin, Joydeep Paul +4
Representation learning for geospatial and spatio-temporal data plays a critical role in enabling general-purpose geospatial intelligence. Recent geospatial foundation models, such…
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…
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…
Continuous Chain of Thought Enables Parallel Exploration and Reasoning
Halil Alperen Gozeten, M. Emrullah Ildiz, Xuechen Zhang +3
Modern language models generate chain-of-thought traces by autoregressively sampling tokens from a finite vocabulary. While this discrete sampling has achieved remarkable success,…
L3GS: Layered 3D Gaussian Splats for Efficient 3D Scene Delivery
Yi-Zhen Tsai, Xuechen Zhang, Zheng Li +1
Traditional 3D content representations include dense point clouds that consume large amounts of data and hence network bandwidth, while newer representations such as neural radianc…
Test-Time Training Provably Improves Transformers as In-context Learners
Halil Alperen Gozeten, M. Emrullah Ildiz, Xuechen Zhang +3
Test-time training (TTT) methods explicitly update the weights of a model to adapt to the specific test instance, and they have found success in a variety of settings, including mo…
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…
On the Power of Convolution Augmented Transformer
Mingchen Li, Xuechen Zhang, Yixiao Huang +1
The transformer architecture has catalyzed revolutionary advances in language modeling. However, recent architectural recipes, such as state-space models, have bridged the performa…
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…
Max-Margin Token Selection in Attention Mechanism
Davoud Ataee Tarzanagh, Yingcong Li, Xuechen Zhang +1
Attention mechanism is a central component of the transformer architecture which led to the phenomenal success of large language models. However, the theoretical principles underly…
Learning on Manifolds: Universal Approximations Properties using Geometric Controllability Conditions for Neural ODEs
Karthik Elamvazhuthi, Xuechen Zhang, Samet Oymak +1
In numerous robotics and mechanical engineering applications, among others, data is often constrained on smooth manifolds due to the presence of rotational degrees of freedom. Comm…
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…