Training Data

Training data is crucial for machine learning model development, with current research focusing on improving data quality, efficiency, and mitigating biases. Active areas include generating synthetic data to address scarcity or privacy concerns, developing algorithms to optimize data selection and usage (e.g., self-paced learning, active learning), and mitigating issues like data contamination and imbalance through techniques such as data augmentation, selective parameter merging, and novel loss functions. The quality and characteristics of training data significantly impact model performance, generalization, and robustness, influencing various applications from natural language processing and image recognition to scientific computing and medical diagnosis.

Papers

February 27, 2023

Deep Imbalanced Time-series Forecasting via Local Discrepancy Density
Junwoo Park, Jungsoo Lee, Youngin Cho, Woncheol Shin, Dongmin Kim, Jaegul Choo, Edward Choi
Training Data Time Varying Dynamic Reweighting Distribution Discrepancy Dynamic Shift Deep Imbalanced

February 25, 2023

Data-Copying in Generative Models: A Formal Framework
Robi Bhattacharjee, Sanjoy Dasgupta, Kamalika Chaudhuri
Deep Neural Network Generative Model Training Data Limited Memorization Unintended Memorization Data Copying

February 24, 2023

HULAT at SemEval-2023 Task 10: Data augmentation for pre-trained transformers applied to the detection of sexism in social media
Isabel Segura-Bedmar
Data Augmentation Training Data Data Detection SemEval 2022 Task Pre Trained Transformer Data Augmentation Method Sexist Content

February 22, 2023

February 21, 2023

Valid Inference for Machine Learning Model Parameters
Neil Dey, Jonathan P. Williams
Machine Learning Training Data Machine Learning Model Loss Function Scientific Inference Confidence Set

February 16, 2023

cGAN-Based High Dimensional IMU Sensor Data Generation for Enhanced Human Activity Recognition in Therapeutic Activities
Mohammad Mohammadzadeh, Ali Ghadami, Alireza Taheri, Saeed Behzadipour
Training Data Activity Recognition Inertial Measurement Unit Wearable Device Priority User

February 14, 2023

Bounding Training Data Reconstruction in DP-SGD
Jamie Hayes, Saeed Mahloujifar, Borja Balle
Training Data Membership Inference Attack Private Model Dp SGD

February 13, 2023

February 12, 2023

Flag Aggregator: Scalable Distributed Training under Failures and Augmented Losses using Convex Optimization
Hamidreza Almasi, Harsh Mishra, Balajee Vamanan, Sathya N. Ravi
Training Data Potential Scalability Convex Optimization Convergence Guarantee Subspace Learning Country Recognition Planning Loss Byzantine Robust Aggregation Byzantine Failure One Shot Aggregation

February 8, 2023

Two-step hyperparameter optimization method: Accelerating hyperparameter search by using a fraction of a training dataset
Sungduk Yu, Mike Pritchard, Po-Lun Ma, Balwinder Singh, Sam Silva
Training Data Hyperparameter Optimization Related Hyperparameters Hyperparameter Selection

February 6, 2023

Holistic Deep-Reinforcement-Learning-based Training of Autonomous Navigation Systems
Linh Kästner, Marvin Meusel, Teham Bhuiyan, Jens Lambrecht
Deep Reinforcement Learning Training Data Autonomous Navigation Obstacle Avoidance Deep Reinforcement Learning Approach Holistic Approach Autonomous Navigation System Navigation Behavior

February 5, 2023

February 4, 2023

How Many and Which Training Points Would Need to be Removed to Flip this Prediction?
Jinghan Yang, Sarthak Jain, Byron C. Wallace
Native Robustness Training Data Human Prediction Model Prediction Convex Machine Learning Training Point

February 3, 2023

February 2, 2023

Effective Robustness against Natural Distribution Shifts for Models with Different Training Data
Zhouxing Shi, Nicholas Carlini, Ananth Balashankar, Ludwig Schmidt, Cho-Jui Hsieh, Alex Beutel, Yao Qin
Native Robustness Training Data Full Model Natural Distribution Shift Effective Robustness

February 1, 2023

Are UD Treebanks Getting More Consistent? A Report Card for English UD
Amir Zeldes, Nathan Schneider
Training Data Large Corpus High Resource Language Dependency Treebanks Bethesda Report

Training Data

Papers

Deep Imbalanced Time-series Forecasting via Local Discrepancy Density

Data-Copying in Generative Models: A Formal Framework

HULAT at SemEval-2023 Task 10: Data augmentation for pre-trained transformers applied to the detection of sexism in social media

Learning from Predictions: Fusing Training and Autoregressive Inference for Long-Term Spatiotemporal Forecasts

Preventing Catastrophic Forgetting in Continual Learning of New Natural Language Tasks

Valid Inference for Machine Learning Model Parameters

cGAN-Based High Dimensional IMU Sensor Data Generation for Enhanced Human Activity Recognition in Therapeutic Activities

Bounding Training Data Reconstruction in DP-SGD

Transferable Deep Metric Learning for Clustering

One Transformer for All Time Series: Representing and Training with Time-Dependent Heterogeneous Tabular Data

Flag Aggregator: Scalable Distributed Training under Failures and Augmented Losses using Convex Optimization

Two-step hyperparameter optimization method: Accelerating hyperparameter search by using a fraction of a training dataset

Holistic Deep-Reinforcement-Learning-based Training of Autonomous Navigation Systems

Quantized Distributed Training of Large Models with Convergence Guarantees

Achieving Robust Generalization for Wireless Channel Estimation Neural Networks by Designed Training Data

How Many and Which Training Points Would Need to be Removed to Flip this Prediction?

Leveraging Contaminated Datasets to Learn Clean-Data Distribution with Purified Generative Adversarial Networks

Symbiosis of an artificial neural network and models of biological neurons: training and testing

Effective Robustness against Natural Distribution Shifts for Models with Different Training Data

Are UD Treebanks Getting More Consistent? A Report Card for English UD