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Found 34 papers, 19 papers with code
Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback
A trustworthy real-world prediction system should be well-calibrated; that is, its confidence in an answer is indicative of the likelihood that the answer is correct, enabling deferral to a more expensive expert in cases of low-confidence predictions.
Last-Layer Fairness Fine-tuning is Simple and Effective for Neural Networks
Although imposing fairness constraints have been studied extensively for classical machine learning models, the effect these techniques have on deep neural networks is still unclear.
Leveraging Domain Relations for Domain Generalization
Distribution shift is a major challenge in machine learning, as models often perform poorly during the test stage if the test distribution differs from the training distribution.
Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time
Temporal shifts -- distribution shifts arising from the passage of time -- often occur gradually and have the additional structure of timestamp metadata.
Holistic Evaluation of Language Models
We present Holistic Evaluation of Language Models (HELM) to improve the transparency of language models.
Multi-Domain Long-Tailed Learning by Augmenting Disentangled Representations
There is an inescapable long-tailed class-imbalance issue in many real-world classification problems.
Surgical Fine-Tuning Improves Adaptation to Distribution Shifts
A common approach to transfer learning under distribution shift is to fine-tune the last few layers of a pre-trained model, preserving learned features while also adapting to the new task.
C-Mixup: Improving Generalization in Regression
In this paper, we propose a simple yet powerful algorithm, C-Mixup, to improve generalization on regression tasks.
Knowledge-Driven New Drug Recommendation
However, newly approved drugs do not have much historical prescription data and cannot leverage existing drug recommendation methods.
Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer
To address this challenge, cross-city knowledge transfer has shown its promise, where the model learned from data-sufficient cities is leveraged to benefit the learning process of data-scarce cities.
Improving Meta-learning for Low-resource Text Classification and Generation via Memory Imitation
Building models of natural language processing (NLP) is challenging in low-resource scenarios where only limited data are available.
Diversify and Disambiguate: Learning From Underspecified Data
Many datasets are underspecified: there exist multiple equally viable solutions to a given task.
Improving Out-of-Distribution Robustness via Selective Augmentation
Machine learning algorithms typically assume that training and test examples are drawn from the same distribution.
Functionally Regionalized Knowledge Transfer for Low-resource Drug Discovery
More recently, there has been a surge of interest in employing machine learning approaches to expedite the drug discovery process where virtual screening for hit discovery and ADMET prediction for lead optimization play essential roles.
Meta-learning with an Adaptive Task Scheduler
In ATS, for the first time, we design a neural scheduler to decide which meta-training tasks to use next by predicting the probability being sampled for each candidate task, and train the scheduler to optimize the generalization capacity of the meta-model to unseen tasks.
Knowledge-Aware Meta-learning for Low-Resource Text Classification
Meta-learning has achieved great success in leveraging the historical learned knowledge to facilitate the learning process of the new task.
Meta-Learning with Fewer Tasks through Task Interpolation
Meta-learning enables algorithms to quickly learn a newly encountered task with just a few labeled examples by transferring previously learned knowledge.
FEW-SHOTLEARNING WITH WEAK SUPERVISION
Few-shot meta-learning methods aim to learn the common structure shared across a set of tasks to facilitate learning new tasks with small amounts of data.
Online Structured Meta-learning
When a new task is encountered, it constructs a meta-knowledge pathway by either utilizing the most relevant knowledge blocks or exploring new blocks.
Relation-aware Meta-learning for Market Segment Demand Prediction with Limited Records
The goal is to facilitate the learning process in the target segments by leveraging the learned knowledge from data-sufficient source segments.
Improving Generalization in Meta-learning via Task Augmentation
Moreover, both MetaMix and Channel Shuffle outperform state-of-the-art results by a large margin across many datasets and are compatible with existing meta-learning algorithms.
Investigating and Mitigating Degree-Related Biases in Graph Convolutional Networks
Pseudo labels increase the chance of connecting to labeled neighbors for low-degree nodes, thus reducing the biases of GCNs from the data perspective.
Automated Relational Meta-learning
In order to efficiently learn with small amount of data on new tasks, meta-learning transfers knowledge learned from previous tasks to the new ones.
Few-Shot Knowledge Graph Completion
Knowledge graphs (KGs) serve as useful resources for various natural language processing applications.
Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values
Thus, jointly modeling local and global temporal dynamics is very promising for MTS forecasting with missing values.
Graph Few-shot Learning via Knowledge Transfer
Towards the challenging problem of semi-supervised node classification, there have been extensive studies.
Transferable Neural Processes for Hyperparameter Optimization
Automated machine learning aims to automate the whole process of machine learning, including model configuration.
Targeted Source Detection for Environmental Data
In the face of growing needs for water and energy, a fundamental understanding of the environmental impacts of human activities becomes critical for managing water and energy resources, remedying water pollution, and making regulatory policy wisely.
Transferring Robustness for Graph Neural Network Against Poisoning Attacks
To optimize PA-GNN for a poisoned graph, we design a meta-optimization algorithm that trains PA-GNN to penalize perturbations using clean graphs and their adversarial counterparts, and transfers such ability to improve the robustness of PA-GNN on the poisoned graph.
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Hierarchically Structured Meta-learning
In order to learn quickly with few samples, meta-learning utilizes prior knowledge learned from previous tasks.
Joint Modeling of Dense and Incomplete Trajectories for Citywide Traffic Volume Inference
In this paper, we propose a novel framework for the citywide traffic volume inference using both dense GPS trajectories and incomplete trajectories captured by camera surveillance systems.
Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction
Specifically, our proposed model is designed as a spatial-temporal network with a meta-learning paradigm.
Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction
Although both factors have been considered in modeling, existing works make strong assumptions about spatial dependence and temporal dynamics, i. e., spatial dependence is stationary in time, and temporal dynamics is strictly periodical.
Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction
Traditional demand prediction methods mostly rely on time series forecasting techniques, which fail to model the complex non-linear spatial and temporal relations.
