2 citations · 5 across the 17 of their papers we have counts for
16 papers
CR: Cross-sample Consistency Regularization Mitigates Feature Splitting and Absorption in Sparse Autoencoders
Haoran Jin, Xiting Wang, Shijie Ren +2
Sparse Autoencoders (SAEs) are widely used to interpret large language models by decomposing activations into sparse, human-understandable features, but scaling to large dictionari…
Learning Generated Controls under Fractured Geometry: Projective Residualization and Variation-Allocation Frontiers
Rui Wu, Zongyuan Chen, Hong Xie +2
Many two-stage estimators assess the first-stage learner by prediction error, even when the next stage uses its residual. In control-function instrumental variables, that residual…
Scaling Federated Linear Contextual Bandits via Sketching
Hantao Yang, Hong Xie, Xutong Liu +1
In federated contextual linear bandits, high data dimensionality incurs prohibitive computation and communication costs: local agents perform -time determinant computation…
AgentCAT: An LLM Agent for Extracting and Analyzing Catalytic Reaction Data from Chemical Engineering Literature
Wei Yang, Zihao Liu, Tao Tan +6
This paper presents a large language model (LLM) agent named AgentCAT, which extracts and analyzes catalytic reaction data from chemical engineering papers, %and supports natural l…
A Unified Frequency Domain Decomposition Framework for Interpretable and Robust Time Series Forecasting
Cheng He, Xijie Liang, Zengrong Zheng +6
Current approaches for time series forecasting, whether in the time or frequency domain, predominantly use deep learning models based on linear layers or transformers. They often e…
LLM Cache Bandit Revisited: Addressing Query Heterogeneity for Cost-Effective LLM Inference
Hantao Yang, Hong Xie, Defu Lian +1
This paper revisits the LLM cache bandit problem, with a special focus on addressing the query heterogeneity for cost-effective LLM inference. Previous works often assume uniform q…
M-MFP: A Multi-Scale and Multi-Level Memory Failure Prediction Framework for Reliable Cloud Infrastructure
Hongyi Xie, Min Zhou, Qiao Yu +4
As cloud services become increasingly integral to modern IT infrastructure, ensuring hardware reliability is essential to sustain high-quality service. Memory failures pose a signi…
Advancing and Benchmarking Personalized Tool Invocation for LLMs
Xu Huang, Yuefeng Huang, Weiwen Liu +5
Tool invocation is a crucial mechanism for extending the capabilities of Large Language Models (LLMs) and has recently garnered significant attention. It enables LLMs to solve comp…
GTM: A General Time-series Model for Enhanced Representation Learning of Time-Series Data
Cheng He, Xu Huang, Gangwei Jiang +7
Despite recent progress in time-series foundation models, challenges persist in improving representation learning and adapting to diverse downstream tasks. We introduce a General T…
Analytical and Empirical Study of Herding Effects in Recommendation Systems
Hong Xie, Mingze Zhong, Defu Lian +2
Online rating systems are often used in numerous web or mobile applications, e.g., Amazon and TripAdvisor, to assess the ground-truth quality of products. Due to herding effects, t…
Multi-agent Multi-armed Bandits with Stochastic Sharable Arm Capacities
Hong Xie, Jinyu Mo, Defu Lian +2
Motivated by distributed selection problems, we formulate a new variant of multi-player multi-armed bandit (MAB) model, which captures stochastic arrival of requests to each arm, a…
Foundations and Frontiers of Graph Learning Theory
Yu Huang, Min Zhou, Menglin Yang +7
Recent advancements in graph learning have revolutionized the way to understand and analyze data with complex structures. Notably, Graph Neural Networks (GNNs), i.e. neural network…
Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Users
Hantao Yang, Xutong Liu, Zhiyong Wang +4
We study the problem of federated contextual combinatorial cascading bandits, where agents collaborate under the coordination of a central server to provide tailore…
Understanding and Patching Compositional Reasoning in LLMs
Zhaoyi Li, Gangwei Jiang, Hong Xie +3
LLMs have marked a revolutonary shift, yet they falter when faced with compositional reasoning tasks. Our research embarks on a quest to uncover the root causes of compositional re…
Invariant Representation via Decoupling Style and Spurious Features from Images
Ruimeng Li, Yuanhao Pu, Zhaoyi Li +2
This paper considers the out-of-distribution (OOD) generalization problem under the setting that both style distribution shift and spurious features exist and domain labels are mis…
Interactive Graph Convolutional Filtering
Jin Zhang, Defu Lian, Hong Xie +2
Interactive Recommender Systems (IRS) have been increasingly used in various domains, including personalized article recommendation, social media, and online advertising. However,…