Search Results for author:
Found 13 papers, 4 papers with code
Multi-Pair Text Style Transfer for Unbalanced Data via Task-Adaptive Meta-Learning
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.
Novel Node Category Detection Under Subpopulation Shift
We introduce a new approach, Recall-Constrained Optimization with Selective Link Prediction (RECO-SLIP), to detect nodes belonging to novel categories in attributed graphs under subpopulation shifts.
LSTTN: A Long-Short Term Transformer-based Spatio-temporal Neural Network for Traffic Flow Forecasting
Accurate traffic forecasting is a fundamental problem in intelligent transportation systems and learning long-range traffic representations with key information through spatiotemporal graph neural networks (STGNNs) is a basic assumption of current traffic flow prediction models.
FuseMoE: Mixture-of-Experts Transformers for Fleximodal Fusion
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 to missing elements, and the temporal irregularity and sparsity of collected samples.
Split Localized Conformal Prediction
The modified score inherits the spirit of split conformal methods, which is simple and efficient and can scale to high dimensional settings.
Efficient Forecasting of Large Scale Hierarchical Time Series via Multilevel Clustering
To attain this goal, each time series is first assigned the forecast for its cluster representative, which can be considered as a "shrinkage prior" for the set of time series it represents.
Architecture Agnostic Federated Learning for Neural Networks
With growing concerns regarding data privacy and rapid increase in data volume, Federated Learning(FL) has become an important learning paradigm.
Dynamic Combination of Heterogeneous Models for Hierarchical Time Series
We conduct a comprehensive evaluation of both point and quantile forecasts for hierarchical time series (HTS), including public data and user records from a large financial software company.
MECATS: Mixture-of-Experts for Probabilistic Forecasts of Aggregated Time Series
We introduce a mixture of heterogeneous experts framework called MECATS, which simultaneously forecasts the values of a set of time series that are related through an aggregation hierarchy.
Multi-Pair Text Style Transfer on Unbalanced Data
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.
Simultaneously Reconciled Quantile Forecasting of Hierarchically Related Time Series
In such applications, it is important that the forecasts, in addition to being reasonably accurate, are also consistent w. r. t one another.
Certified Monotonic Neural Networks
In this work, we propose to certify the monotonicity of the general piece-wise linear neural networks by solving a mixed integer linear programming problem. This provides a new general approach for learning monotonic neural networks with arbitrary model structures.
Model-Agnostic Explanations using Minimal Forcing Subsets
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?
