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Found 13 papers, 5 papers with code
Subgroup analysis methods for time-to-event outcomes in heterogeneous randomized controlled trials
Identifying such heterogeneous treatment effects is key for precision medicine and many post-hoc analysis methods have been developed for that purpose.
Toward Stronger Textual Attack Detectors
The landscape of available textual adversarial attacks keeps growing, posing severe threats and raising concerns regarding the deep NLP system's integrity.
A Functional Data Perspective and Baseline On Multi-Layer Out-of-Distribution Detection
A key feature of out-of-distribution (OOD) detection is to exploit a trained neural network by extracting statistical patterns and relationships through the multi-layer classifier to detect shifts in the expected input data distribution.
Towards More Robust NLP System Evaluation: Handling Missing Scores in Benchmarks
This paper formalize an existing problem in NLP research: benchmarking when some systems scores are missing on the task, and proposes a novel approach to address it.
Beyond Mahalanobis-Based Scores for Textual OOD Detection
Deep learning methods have boosted the adoption of NLP systems in real-life applications.
Mitigating Gender Bias in Face Recognition Using the von Mises-Fisher Mixture Model
In spite of the high performance and reliability of deep learning algorithms in a wide range of everyday applications, many investigations tend to show that a lot of models exhibit biases, discriminating against specific subgroups of the population (e. g. gender, ethnicity).
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The Glass Ceiling of Automatic Evaluation in Natural Language Generation
Automatic evaluation metrics capable of replacing human judgments are critical to allowing fast development of new methods.
Learning Disentangled Textual Representations via Statistical Measures of Similarity
When working with textual data, a natural application of disentangled representations is fair classification where the goal is to make predictions without being biased (or influenced) by sensitive attributes that may be present in the data (e. g., age, gender or race).
What are the best systems? New perspectives on NLP Benchmarking
In Machine Learning, a benchmark refers to an ensemble of datasets associated with one or multiple metrics together with a way to aggregate different systems performances.
Learning an Ethical Module for Bias Mitigation of pre-trained Models
In spite of the high performance and reliability of deep learning algorithms in broad range everyday applications, many investigations tend to show that a lot of models exhibit biases, discriminating against some subgroups of the population.
Learning to Rank Anomalies: Scalar Performance Criteria and Maximization of Two-Sample Rank Statistics
The ability to collect and store ever more massive databases has been accompanied by the need to process them efficiently.
Online Matching in Sparse Random Graphs: Non-Asymptotic Performances of Greedy Algorithm
Motivated by sequential budgeted allocation problems, we investigate online matching problems where connections between vertices are not i. i. d., but they have fixed degree distributions -- the so-called configuration model.
