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06/03/2023
Encoding Time-Series Explanations through Self-Supervised Model Behavior Consistency
Interpreting time series models is uniquely challenging because it requi...
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04/04/2023
ERM++: An Improved Baseline for Domain Generalization
Multi-source Domain Generalization (DG) measures a classifier's ability ...
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02/06/2023
Domain Adaptation for Time Series Under Feature and Label Shifts
The transfer of models trained on labeled datasets in a source domain to...
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10/04/2022
Supervised Metric Learning for Retrieval via Contextual Similarity Optimization
Existing deep metric learning approaches fall into three general categor...
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06/17/2022
Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency
Pre-training on time series poses a unique challenge due to the potentia...
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05/26/2022
Pick up the PACE: Fast and Simple Domain Adaptation via Ensemble Pseudo-Labeling
Domain Adaptation (DA) has received widespread attention from deep learn...
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02/24/2022
Fourier-Based Augmentations for Improved Robustness and Uncertainty Calibration
Diverse data augmentation strategies are a natural approach to improving...
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10/11/2021
Graph-Guided Network for Irregularly Sampled Multivariate Time Series
In many domains, including healthcare, biology, and climate science, tim...
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04/02/2021
Misclassification-Aware Gaussian Smoothing improves Robustness against Domain Shifts
Deep neural networks achieve high prediction accuracy when the train and...
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12/22/2020
On Frank-Wolfe Optimization for Adversarial Robustness and Interpretability
Deep neural networks are easily fooled by small perturbations known as a...
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11/30/2020
Ultrasound Diagnosis of COVID-19: Robustness and Explainability
Diagnosis of COVID-19 at point of care is vital to the containment of th...
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10/19/2020
Failure Prediction by Confidence Estimation of Uncertainty-Aware Dirichlet Networks
Reliably assessing model confidence in deep learning and predicting erro...
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09/10/2020
Second Order Optimization for Adversarial Robustness and Interpretability
Deep neural networks are easily fooled by small perturbations known as a...
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10/10/2019
Information Robust Dirichlet Networks for Predictive Uncertainty Estimation
Precise estimation of uncertainty in predictions for AI systems is a cri...
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06/28/2018
Active query-driven visual search using probabilistic bisection and convolutional neural networks
We present a novel efficient object detection and localization framework...
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06/14/2017
Accelerated Reinforcement Learning Algorithms with Nonparametric Function Approximation for Opportunistic Spectrum Access
We study the problem of throughput maximization by predicting spectrum o...
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11/10/2015
Asynchronous Decentralized 20 Questions for Adaptive Search
This paper considers the problem of adaptively searching for an unknown ...
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09/29/2014
Adaptive Low-Complexity Sequential Inference for Dirichlet Process Mixture Models
We develop a sequential low-complexity inference procedure for Dirichlet...
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07/27/2013
Kronecker Sum Decompositions of Space-Time Data
In this paper we consider the use of the space vs. time Kronecker produc...
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02/12/2013
Covariance Estimation in High Dimensions via Kronecker Product Expansions
This paper presents a new method for estimating high dimensional covaria...
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04/03/2012
