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Found 13 papers, 5 papers with code
Decision-Oriented Learning for Future Power System Decision-Making under Uncertainty
This paper first elaborates on the mismatch between more accurate forecasts and more optimal decisions in the power system caused by statistical-based learning (SBL) and explains how DOL resolves this problem.
Adaptive Decision-Objective Loss for Forecast-then-Optimize in Power Systems
However, the implementation of decision loss in power systems faces challenges in 1) accommodating multi-stage decision-making problems where upstream optimality cannot guarantee final optimality; 2) adapting to dynamic environments such as changing parameters and nature of the problem like continuous or discrete optimization tasks.
Multiple Instance Learning for Uplift Modeling
Uplift modeling is widely used in performance marketing to estimate effects of promotion campaigns (e. g., increase of customer retention rate).
It Ain't That Bad: Understanding the Mysterious Performance Drop in OOD Generalization for Generative Transformer Models
Specifically, these models map unseen OOD inputs to outputs with equivalence relations in the ID domain.
Where Did the President Visit Last Week? Detecting Celebrity Trips from News Articles
Although news articles contain travel information of celebrities, it is not possible to perform large-scale and network-wise analysis due to the lack of automatic itinerary detection tools.
SIMPLE: Specialized Model-Sample Matching for Domain Generalization
Moreover, our method is highly efficient and achieves more than 1000 times training speedup compared to the conventional DG methods with fine-tuning a pretrained model.
Ranked #1 on
Domain Generalization
on PACS
Risk-Aware Objective-Based Forecasting in Inertia Management
The objective-based forecasting considers the asymmetric and non-linear impacts of forecasting errors on decision objectives, thus improving the effectiveness of its downstream decision-making process.
For the Underrepresented in Gender Bias Research: Chinese Name Gender Prediction with Heterogeneous Graph Attention Network
We open-source a more balanced multi-character dataset from an official source together with our code, hoping to help future research promoting gender equality.
Domain Generalization using Pretrained Models without Fine-tuning
Fine-tuning pretrained models is a common practice in domain generalization (DG) tasks.
Ranked #8 on
Domain Generalization
on TerraIncognita
SANE: Specialization-Aware Neural Network Ensemble
Real-world data is often generated by some complex distribution, which can be approximated by a composition of multiple simpler distributions.
Knowledge Graph Enhanced Event Extraction in Financial Documents
Event extraction is a classic task in natural language processing with wide use in handling large amount of yet rapidly growing financial, legal, medical, and government documents which often contain multiple events with their elements scattered and mixed across the documents, making the problem much more difficult.
Graph Neural Network Based VC Investment Success Prediction
Predicting the start-ups that will eventually succeed is essentially important for the venture capital business and worldwide policy makers, especially at an early stage such that rewards can possibly be exponential.
Sill-Net: Feature Augmentation with Separated Illumination Representation
For visual object recognition tasks, the illumination variations can cause distinct changes in object appearance and thus confuse the deep neural network based recognition models.
Ranked #1 on
Traffic Sign Recognition
on TopLogo-10
