no code implementations • 4 Apr 2023 • Piotr Teterwak, Kuniaki Saito, Theodoros Tsiligkaridis, Kate Saenko, Bryan A. Plummer
We call the resulting method ERM++, and show it significantly improves the performance of DG on five multi-source datasets by over 5% compared to standard ERM, and beats state-of-the-art despite being less computationally expensive.
1 code implementation • 6 Feb 2023 • Huan He, Owen Queen, Teddy Koker, Consuelo Cuevas, Theodoros Tsiligkaridis, Marinka Zitnik
Furthermore, tasks in the source and target domains can have vastly different label distributions, making it difficult for UDA to mitigate label shifts and recognize labels that only exist in the target domain.
1 code implementation • 4 Oct 2022 • Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis
There is extensive interest in metric learning methods for image retrieval.
1 code implementation • 17 Jun 2022 • Xiang Zhang, Ziyuan Zhao, Theodoros Tsiligkaridis, Marinka Zitnik
Experiments against eight state-of-the-art methods show that TF-C outperforms baselines by 15. 4% (F1 score) on average in one-to-one settings (e. g., fine-tuning an EEG-pretrained model on EMG data) and by 8. 4% (precision) in challenging one-to-many settings (e. g., fine-tuning an EEG-pretrained model for either hand-gesture recognition or mechanical fault prediction), reflecting the breadth of scenarios that arise in real-world applications.
1 code implementation • 26 May 2022 • 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 labeled data.
no code implementations • 24 Feb 2022 • Ryan Soklaski, Michael Yee, Theodoros Tsiligkaridis
Diverse data augmentation strategies are a natural approach to improving robustness in computer vision models against unforeseen shifts in data distribution.
2 code implementations • ICLR 2022 • Xiang Zhang, Marko Zeman, Theodoros Tsiligkaridis, Marinka Zitnik
Here, we introduce RAINDROP, a graph neural network that embeds irregularly sampled and multivariate time series while also learning the dynamics of sensors purely from observational data.
no code implementations • ICML Workshop AML 2021 • Theodoros Tsiligkaridis, Jay Roberts
We develop a theoretical framework for adversarial training (AT) with FW optimization (FW-AT) that reveals a geometric connection between the loss landscape and the distortion of $\ell_\infty$ FW attacks (the attack's $\ell_2$ norm).
no code implementations • 2 Apr 2021 • Theodoros Tsiligkaridis, Athanasios Tsiligkaridis
In this paper, we propose a diverse Gaussian noise consistency regularization method for improving robustness of image classifiers under a variety of corruptions while still maintaining high clean accuracy.
1 code implementation • CVPR 2022 • Theodoros Tsiligkaridis, Jay Roberts
We develop a theoretical framework for adversarial training with FW optimization (FW-AT) that reveals a geometric connection between the loss landscape and the $\ell_2$ distortion of $\ell_\infty$ FW attacks.
no code implementations • 30 Nov 2020 • Jay Roberts, Theodoros Tsiligkaridis
Diagnosis of COVID-19 at point of care is vital to the containment of the global pandemic.
no code implementations • 19 Oct 2020 • 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 applications with dire consequences.
no code implementations • 10 Sep 2020 • Theodoros Tsiligkaridis, Jay Roberts
It is shown that using only a single iteration in our regularizer achieves stronger robustness than prior gradient and curvature regularization schemes, avoids gradient obfuscation, and, with additional iterations, achieves strong robustness with significantly lower training time than AT.
no code implementations • 10 Oct 2019 • Theodoros Tsiligkaridis
Precise estimation of uncertainty in predictions for AI systems is a critical factor in ensuring trust and safety.
no code implementations • 28 Jun 2018 • Athanasios Tsiligkaridis, Theodoros Tsiligkaridis
We present a novel efficient object detection and localization framework based on the probabilistic bisection algorithm.
no code implementations • 14 Jun 2017 • Theodoros Tsiligkaridis, David Romero
Opportunistic spectrum access is one of the emerging techniques for maximizing throughput in congested bands and is enabled by predicting idle slots in spectrum.
no code implementations • 21 Aug 2016 • Athanasios Tsiligkaridis, Theodoros Tsiligkaridis
We present a novel distributed probabilistic bisection algorithm using social learning with application to target localization.
no code implementations • 10 Nov 2015 • Theodoros Tsiligkaridis
This paper considers the problem of adaptively searching for an unknown target using multiple agents connected through a time-varying network topology.
no code implementations • NeurIPS 2015 • Theodoros Tsiligkaridis, Keith W. Forsythe
We develop a sequential low-complexity inference procedure for Dirichlet process mixtures of Gaussians for online clustering and parameter estimation when the number of clusters are unknown a-priori.
no code implementations • 27 Jul 2013 • Kristjan Greenewald, Theodoros Tsiligkaridis, Alfred O. Hero III
To allow a smooth tradeoff between the reduction in the number of parameters (to reduce estimation variance) and the accuracy of the covariance approximation (affecting estimation bias), we introduce a diagonally loaded modification of the sum of kronecker products representation .
no code implementations • 12 Feb 2013 • Theodoros Tsiligkaridis, Alfred O. Hero III
We show that a class of block Toeplitz covariance matrices is approximatable by low separation rank and give bounds on the minimal separation rank $r$ that ensures a given level of bias.
no code implementations • 3 Apr 2012 • Theodoros Tsiligkaridis, Alfred O. Hero III, Shuheng Zhou
The KGlasso algorithm generalizes Glasso, introduced by Yuan and Lin ["Model selection and estimation in the Gaussian graphical model," Biometrika, vol.