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Theodoros Rekatsinas

7 accepted papers

2024

Construction of Paired Knowledge Graph - Text Datasets Informed by Cyclic Evaluation

COLING 2024main

Datasets that pair Knowledge Graphs (KG) and text together (KG-T) can be used to train forward and reverse neural models that generate text from KG and vice versa. However models trained on datasets where KG and text pairs are not equivalent can suffer from more hallucination and poorer recall. In t…

Cited by 2SourcePDFScholar
2024

Repeated Random Sampling for Minimizing the Time-to-Accuracy of Learning

ICLR 2024poster

Methods for carefully selecting or generating a small set of training data to learn from, i.e., data pruning, coreset selection, and dataset distillation, have been shown to be effective in reducing the ever-increasing cost of training neural networks. Behind this success are rigorously designed, ye…

2021

On Robust Mean Estimation under Coordinate-level Corruption

ICML 2021spotlight

We study the problem of robust mean estimation and introduce a novel Hamming distance-based measure of distribution shift for coordinate-level corruptions. We show that this measure yields adversary models that capture more realistic corruptions than those used in prior works, and present an informa…

Cited by 10SourcePDFScholar
2020

Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models

ICML 2020poster

Directed graphical models (DGMs) are a class of probabilistic models that are widely used for predictive analysis in sensitive domains such as medical diagnostics. In this paper, we present an algorithm for differentially-private learning of the parameters of a DGM. Our solution optimizes for the ut…

Cited by 12SourcePDFScholar
2019

Approximate Inference in Structured Instances with Noisy Categorical Observations

UAI 2019poster

We study the problem of recovering the latent ground truth labeling of a structured instance with categorical random variables in the presence of noisy observations. We present a new approximate algorithm for graphs with categorical variables that achieves low Hamming error in the presence of noisy…

Cited by 9SourcePDFScholar
2015

HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades

ICML 2015poster

Understanding the diffusion of information in social network and social media requires modeling the text diffusion process. In this work, we develop the HawkesTopic model (HTM) for analyzing text-based cascades, such as "retweeting a post" or "publishing a follow-up blog post". HTM combines Hawkes p…

Cited by 123SourcePDFScholar