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Found 9 papers, 2 papers with code
Detecting Out-of-Distribution Samples via Conditional Distribution Entropy with Optimal Transport
When deploying a trained machine learning model in the real world, it is inevitable to receive inputs from out-of-distribution (OOD) sources.
An Incremental Update Framework for Online Recommenders with Data-Driven Prior
To address the aforementioned issue, we propose an incremental update framework for online recommenders with Data-Driven Prior (DDP), which is composed of Feature Prior (FP) and Model Prior (MP).
Parallel Ranking of Ads and Creatives in Real-Time Advertising Systems
In this paper, we propose for the first time a novel architecture for online parallel estimation of ads and creatives ranking, as well as the corresponding offline joint optimization model.
Post-hoc Bias Scoring Is Optimal For Fair Classification
We consider a binary classification problem under group fairness constraints, which can be one of Demographic Parity (DP), Equalized Opportunity (EOp), or Equalized Odds (EO).
The Robust Semantic Segmentation UNCV2023 Challenge Results
This paper outlines the winning solutions employed in addressing the MUAD uncertainty quantification challenge held at ICCV 2023.
Calibrating Transformers via Sparse Gaussian Processes
Transformer models have achieved profound success in prediction tasks in a wide range of applications in natural language processing, speech recognition and computer vision.
JDRec: Practical Actor-Critic Framework for Online Combinatorial Recommender System
A combinatorial recommender (CR) system feeds a list of items to a user at a time in the result page, in which the user behavior is affected by both contextual information and items.
Blending Advertising with Organic Content in E-Commerce: A Virtual Bids Optimization Approach
In e-commerce platforms, sponsored and non-sponsored content are jointly displayed to users and both may interactively influence their engagement behavior.
Gradient-based tuning of Hamiltonian Monte Carlo hyperparameters
Existing approaches for automating this task either optimise a proxy for mixing speed or consider the HMC chain as an implicit variational distribution and optimize a tractable lower bound that is too loose to be useful in practice.
