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Found 21 papers, 2 papers with code
OpenHEXAI: An Open-Source Framework for Human-Centered Evaluation of Explainable Machine Learning
However, properly evaluating the effectiveness of the XAI methods inevitably requires the involvement of human subjects, and conducting human-centered benchmarks is challenging in a number of ways: designing and implementing user studies is complex; numerous design choices in the design space of user study lead to problems of reproducibility; and running user studies can be challenging and even daunting for machine learning researchers.
Random Projection Layers for Multidimensional Time Series Forecasting
All-Multi-Layer Perceptron (all-MLP) mixer models have been shown to be effective for time series forecasting problems.
Towards Mitigating Dimensional Collapse of Representations in Collaborative Filtering
Here we propose a non-contrastive learning objective, named nCL, which explicitly mitigates dimensional collapse of representations in collaborative filtering.
Temporal Treasure Hunt: Content-based Time Series Retrieval System for Discovering Insights
To facilitate this investigation, we introduce a CTSR benchmark dataset that comprises time series data from a variety of domains, such as motion, power demand, and traffic.
Ego-Network Transformer for Subsequence Classification in Time Series Data
The ego-networks of all subsequences collectively form a time series subsequence graph, and we introduce an algorithm to efficiently construct this graph.
Toward a Foundation Model for Time Series Data
A foundation model is a machine learning model trained on a large and diverse set of data, typically using self-supervised learning-based pre-training techniques, that can be adapted to various downstream tasks.
An Efficient Content-based Time Series Retrieval System
A Content-based Time Series Retrieval (CTSR) system is an information retrieval system for users to interact with time series emerged from multiple domains, such as finance, healthcare, and manufacturing.
Adversarial Collaborative Filtering for Free
In this paper, we present Sharpness-aware Collaborative Filtering (SharpCF), a simple yet effective method that conducts adversarial training without extra computational cost over the base optimizer.
Enhancing Transformers without Self-supervised Learning: A Loss Landscape Perspective in Sequential Recommendation
Despite their success, Transformer-based models often require the optimization of a large number of parameters, making them difficult to train from sparse data in sequential recommendation.
Sharpness-Aware Graph Collaborative Filtering
Graph Neural Networks (GNNs) have achieved impressive performance in collaborative filtering.
Evaluating Evaluation Metrics: A Framework for Analyzing NLG Evaluation Metrics using Measurement Theory
We address a fundamental challenge in Natural Language Generation (NLG) model evaluation -- the design and evaluation of evaluation metrics.
Selective Explanations: Leveraging Human Input to Align Explainable AI
As a result, current XAI techniques are often found to be hard to use and lack effectiveness.
An Exploration of Post-Editing Effectiveness in Text Summarization
Automatic summarization methods are efficient but can suffer from low quality.
Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation
Despite impressive performance in many benchmark datasets, AI models can still make mistakes, especially among out-of-distribution examples.
Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies
Besides developing AI technologies for this purpose, the emerging field of human-AI decision making must embrace empirical approaches to form a foundational understanding of how humans interact and work with AI to make decisions.
Using Transformers to Provide Teachers with Personalized Feedback on their Classroom Discourse: The TalkMoves Application
TalkMoves is an innovative application designed to support K-12 mathematics teachers to reflect on, and continuously improve their instructional practices.
Understanding the Effect of Out-of-distribution Examples and Interactive Explanations on Human-AI Decision Making
Although AI holds promise for improving human decision making in societally critical domains, it remains an open question how human-AI teams can reliably outperform AI alone and human alone in challenging prediction tasks (also known as complementary performance).
Harnessing Explanations to Bridge AI and Humans
Machine learning models are increasingly integrated into societally critical applications such as recidivism prediction and medical diagnosis, thanks to their superior predictive power.
"Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humans
To support human decision making with machine learning models, we often need to elucidate patterns embedded in the models that are unsalient, unknown, or counterintuitive to humans.
Many Faces of Feature Importance: Comparing Built-in and Post-hoc Feature Importance in Text Classification
In this work, we systematically compare feature importance from built-in mechanisms in a model such as attention values and post-hoc methods that approximate model behavior such as LIME.
On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection
In this paper, we use deception detection as a testbed and investigate how we can harness explanations and predictions of machine learning models to improve human performance while retaining human agency.
