128 citations · 483 across the 31 of their papers we have counts for
12 papers · 1 filter
Encoding Time-Series Explanations through Self-Supervised Model Behavior Consistency
Owen Queen, Thomas Hartvigsen, Teddy Koker +3
Interpreting time series models is uniquely challenging because it requires identifying both the location of time series signals that drive model predictions and their matching to…
ERM++: An Improved Baseline for Domain Generalization
Piotr Teterwak, Kuniaki Saito, Theodoros Tsiligkaridis +2
Domain Generalization (DG) aims to develop classifiers that can generalize to new, unseen data distributions, a critical capability when collecting new domain-specific data is impr…
Domain Adaptation for Time Series Under Feature and Label Shifts
Huan He, Owen Queen, Teddy Koker +3
Unsupervised domain adaptation (UDA) enables the transfer of models trained on source domains to unlabeled target domains. However, transferring complex time series models presents…
Supervised Metric Learning to Rank for Retrieval via Contextual Similarity Optimization
Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis
There is extensive interest in metric learning methods for image retrieval. Many metric learning loss functions focus on learning a correct ranking of training samples, but strongl…
Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency
Xiang Zhang, Ziyuan Zhao, Theodoros Tsiligkaridis +1
Pre-training on time series poses a unique challenge due to the potential mismatch between pre-training and target domains, such as shifts in temporal dynamics, fast-evolving trend…
Pick up the PACE: Fast and Simple Domain Adaptation via Ensemble Pseudo-Labeling
Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis
Domain Adaptation (DA) has received widespread attention from deep learning researchers in recent years because of its potential to improve test accuracy with out-of-distribution l…
Graph-Guided Network for Irregularly Sampled Multivariate Time Series
Xiang Zhang, Marko Zeman, Theodoros Tsiligkaridis +1
In many domains, including healthcare, biology, and climate science, time series are irregularly sampled with varying time intervals between successive readouts and different subse…
Diverse Gaussian Noise Consistency Regularization for Robustness and Uncertainty Calibration
Theodoros Tsiligkaridis, Athanasios Tsiligkaridis
Deep neural networks achieve high prediction accuracy when the train and test distributions coincide. In practice though, various types of corruptions occur which deviate from this…
Understanding and Increasing Efficiency of Frank-Wolfe Adversarial Training
Theodoros Tsiligkaridis, Jay Roberts
Deep neural networks are easily fooled by small perturbations known as adversarial attacks. Adversarial Training (AT) is a technique that approximately solves a robust optimization…
Failure Prediction by Confidence Estimation of Uncertainty-Aware Dirichlet Networks
Theodoros Tsiligkaridis
Reliably assessing model confidence in deep learning and predicting errors likely to be made are key elements in providing safety for model deployment, in particular for applicatio…
Second Order Optimization for Adversarial Robustness and Interpretability
Theodoros Tsiligkaridis, Jay Roberts
Deep neural networks are easily fooled by small perturbations known as adversarial attacks. Adversarial Training (AT) is a technique aimed at learning features robust to such attac…
Information Aware Max-Norm Dirichlet Networks for Predictive Uncertainty Estimation
Theodoros Tsiligkaridis
Precise estimation of uncertainty in predictions for AI systems is a critical factor in ensuring trust and safety. Deep neural networks trained with a conventional method are prone…