30 citations · 60 across the 14 of their papers we have counts for
14 papers
Massively Multimodal Foundation Models: A Framework for Capturing Interactions with Specialized Mixture-of-Experts
Xing Han, Hsing-Huan Chung, Joydeep Ghosh +2
Modern applications increasingly involve many heterogeneous input streams, such as clinical sensors, wearable device data, imaging, and text, each with distinct measurement models,…
Between Linear and Sinusoidal: Rethinking the Time Encoder in Dynamic Graph Learning
Hsing-Huan Chung, Shravan Chaudhari, Xing Han +3
Dynamic graph learning is essential for applications involving temporal networks and requires effective modeling of temporal relationships. Seminal attention-based models like TGAT…
Achieving Fairness Across Local and Global Models in Federated Learning
Disha Makhija, Xing Han, Joydeep Ghosh +1
Achieving fairness across diverse clients in Federated Learning (FL) remains a significant challenge due to the heterogeneity of the data and the inaccessibility of sensitive attri…
Novel Node Category Detection Under Subpopulation Shift
Hsing-Huan Chung, Shravan Chaudhari, Yoav Wald +2
In real-world graph data, distribution shifts can manifest in various ways, such as the emergence of new categories and changes in the relative proportions of existing categories.…
FuseMoE: Mixture-of-Experts Transformers for Fleximodal Fusion
Xing Han, Huy Nguyen, Carl Harris +2
As machine learning models in critical fields increasingly grapple with multimodal data, they face the dual challenges of handling a wide array of modalities, often incomplete due…
Designing Robust Transformers using Robust Kernel Density Estimation
Xing Han, Tongzheng Ren, Tan Minh Nguyen +3
Recent advances in Transformer architectures have empowered their empirical success in a variety of tasks across different domains. However, existing works mainly focus on predicti…
Split Localized Conformal Prediction
Xing Han, Ziyang Tang, Joydeep Ghosh +1
Conformal prediction is a simple and powerful tool that can quantify uncertainty without any distributional assumptions. Many existing methods only address the average coverage gua…
Efficient Forecasting of Large Scale Hierarchical Time Series via Multilevel Clustering
Xing Han, Tongzheng Ren, Jing Hu +2
We propose a novel approach to the problem of clustering hierarchically aggregated time-series data, which has remained an understudied problem though it has several commercial app…
Architecture Agnostic Federated Learning for Neural Networks
Disha Makhija, Xing Han, Nhat Ho +1
With growing concerns regarding data privacy and rapid increase in data volume, Federated Learning(FL) has become an important learning paradigm. However, jointly learning a deep n…
Dynamic Combination of Heterogeneous Models for Hierarchical Time Series
Xing Han, Jing Hu, Joydeep Ghosh
We introduce a framework to dynamically combine heterogeneous models called \texttt{DYCHEM}, which forecasts a set of time series that are related through an aggregation hierarchy.…
Multi-Pair Text Style Transfer on Unbalanced Data
Xing Han, Jessica Lundin
Text-style transfer aims to convert text given in one domain into another by paraphrasing the sentence or substituting the keywords without altering the content. By necessity, stat…
Simultaneously Reconciled Quantile Forecasting of Hierarchically Related Time Series
Xing Han, Sambarta Dasgupta, Joydeep Ghosh
Many real-life applications involve simultaneously forecasting multiple time series that are hierarchically related via aggregation or disaggregation operations. For instance, comm…
Certified Monotonic Neural Networks
Xingchao Liu, Xing Han, Na Zhang +1
Learning monotonic models with respect to a subset of the inputs is a desirable feature to effectively address the fairness, interpretability, and generalization issues in practice…
Model-Agnostic Explanations using Minimal Forcing Subsets
Xing Han, Joydeep Ghosh
How can we find a subset of training samples that are most responsible for a specific prediction made by a complex black-box machine learning model? More generally, how can we expl…