publications

2021

1. NAACL21

   DATE: Detecting Anomalies in Text via Self-Supervision of Transformers

   Andrei Manolache, Florin Brad, and Elena Burceanu

   In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies Jun 2021

   Abs

   Leveraging deep learning models for Anomaly Detection (AD) has seen widespread use in recent years due to superior performances over traditional methods. Recent deep methods for anomalies in images learn better features of normality in an end-to-end self-supervised setting. These methods train a model to discriminate between different transformations applied to visual data and then use the output to compute an anomaly score. We use this approach for AD in text, by introducing a novel pretext task on text sequences. We learn our DATE model end-to-end, enforcing two independent and complementary self-supervision signals, one at the token-level and one at the sequence-level. Under this new task formulation, we show strong quantitative and qualitative results on the 20Newsgroups and AG News datasets. In the semi-supervised setting, we outperform state-of-the-art results by +13.5% and +6.9%, respectively (AUROC). In the unsupervised configuration, DATE surpasses all other methods even when 10% of its training data is contaminated with outliers (compared with 0% for the others).

2. arXiv

   ICLR 2021 Challenge for Computational Geometry & Topology: Design and Results

   Nina Miolane, Matteo Caorsi, Umberto Lupo, and 30 more authors

   CoRR Jun 2021

2022

1. EMNLP22

   Rethinking the Authorship Verification Experimental Setups

   Florin Brad, Andrei Manolache, Elena Burceanu, and 3 more authors

   Dec 2022
2. NeurIPS22

   VeriDark: A Large-Scale Benchmark for Authorship Verification on the Dark Web

   Andrei Manolache, Florin Brad, Antonio Barbalau, and 2 more authors

   Dec 2022
3. NeurIPS22

   AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection

   Marius Drăgoi, Elena Burceanu, Emanuela Haller, and 2 more authors

   Dec 2022

2023

1. EMNLP23

   AD-NLP: A Benchmark for Anomaly Detection in Natural Language Processing

   Matei Bejan, Andrei Manolache, and Marius Popescu

   In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing Dec 2023

   Abs

   Deep learning models have reignited the interest in Anomaly Detection research in recent years. Methods for Anomaly Detection in text have shown strong empirical results on ad-hoc anomaly setups that are usually made by downsampling some classes of a labeled dataset. This can lead to reproducibility issues and models that are biased toward detecting particular anomalies while failing to recognize them in more sophisticated scenarios. In the present work, we provide a unified benchmark for detecting various types of anomalies, focusing on problems that can be naturally formulated as Anomaly Detection in text, ranging from syntax to stylistics. In this way, we are hoping to facilitate research in Text Anomaly Detection. We also evaluate and analyze two strong shallow baselines, as well as two of the current state-of-the-art neural approaches, providing insights into the knowledge the neural models are learning when performing the anomaly detection task. We provide the code for evaluation, downloading, and preprocessing the dataset at https://github.com/mateibejan1/ad-nlp/.

2. ICML23DiffAlmostEverything

   Probabilistic Task-Adaptive Graph Rewiring

   Chendi Qian, Andrei Manolache, Kareem Ahmed, and 4 more authors

   In ICML 2023 Workshop on Differentiable Almost Everything: Differentiable Relaxations, Algorithms, Operators, and Simulators Jul 2023
3. ICDM23AI4TS

   Time Series Anomaly Detection using Diffusion-based Models

   Ioana Pintilie, Andrei Manolache, and Florin Brad

   Dec 2023

2024

1. ICLR24

   Probabilistically Rewired Message-Passing Neural Networks

   Chendi Qian, Andrei Manolache, Kareem Ahmed, and 4 more authors

   Jan 2024

   Abs

   Message-passing graph neural networks (MPNNs) emerged as powerful tools for processing graph-structured input. However, they operate on a fixed input graph structure, ignoring potential noise and missing information. Furthermore, their local aggregation mechanism can lead to problems such as over-squashing and limited expressive power in capturing relevant graph structures. Existing solutions to these challenges have primarily relied on heuristic methods, often disregarding the underlying data distribution. Hence, devising principled approaches for learning to infer graph structures relevant to the given prediction task remains an open challenge. In this work, leveraging recent progress in exact and differentiable k-subset sampling, we devise probabilistically rewired MPNNs (PR-MPNNs), which learn to add relevant edges while omitting less beneficial ones. For the first time, our theoretical analysis explores how PR-MPNNs enhance expressive power, and we identify precise conditions under which they outperform purely randomized approaches. Empirically, we demonstrate that our approach effectively mitigates issues like over-squashing and under-reaching. In addition, on established real-world datasets, our method exhibits competitive or superior predictive performance compared to traditional MPNN models and recent graph transformer architectures.

2025

2026