Publications

Tracking white and grey matter degeneration along the spinal cord axis in degenerative cervical myelopathy
Kevin Vallotton
Gergely David
Markus Hupp
Nikolai Pfender
Julien Cohen-Adad
Michael Fehlings
Rebecca S. Samson
Claudia A. M. Gandini Wheeler-Kingshott
Armin Curt
Patrick Freund
Maryam Seif
Tissue-specific neurodegeneration revealed by quantitative MRI, already apparent across the spinal cord in mild-moderate DCM prior to the on… (see more)set of severe clinical impairments. WM microstructural changes are particularly sensitive to remote pathologically and clinically eloquent changes in DCM.
2021-04-21
medRxiv (preprint)
doi.org
Gradient Masked Federated Optimization
Irene Tenison
Sreya Francis
Irina Rish
2021-04-20
ArXiv (preprint)
arxiv.org
arxiv.org
hBERT + BiasCorp - Fighting Racism on the Web
Olawale Moses Onabola
Zhuang Ma
Xie Yang
Benjamin Akera
Ibraheem Abdulrahman
Jia Xue
Dianbo Liu
Yoshua Bengio
Subtle and overt racism is still present both in physical and online communities today and has impacted many lives in different segments of … (see more)the society. In this short piece of work, we present how we’re tackling this societal issue with Natural Language Processing. We are releasing BiasCorp, a dataset containing 139,090 comments and news segment from three specific sources - Fox News, BreitbartNews and YouTube. The first batch (45,000 manually annotated) is ready for publication. We are currently in the final phase of manually labeling the remaining dataset using Amazon Mechanical Turk. BERT has been used widely in several downstream tasks. In this work, we present hBERT, where we modify certain layers of the pretrained BERT model with the new Hopfield Layer. hBert generalizes well across different distributions with the added advantage of a reduced model complexity. We are also releasing a JavaScript library 3 and a Chrome Extension Application, to help developers make use of our trained model in web applications (say chat application) and for users to identify and report racially biased contents on the web respectively
2021-04-18
OpenReview.net/Archive (published)
openreview.net
openreview.net
INFOSHIELD: Generalizable Information-Theoretic Human-Trafficking Detection
Meng-Chieh Lee
Catalina Vajiac
Aayushi Kulshrestha
Sacha Lévy
Namyong Park
Cara Jones
Reihaneh Rabbany
Christos Faloutsos
Given a million escort advertisements, how can we spot near-duplicates? Such micro-clusters of ads are usually signals of human trafficking.… (see more) How can we summarize them, visually, to convince law enforcement to act? Can we build a general tool that works for different languages? Spotting micro-clusters of near-duplicate documents is useful in multiple, additional settings, including spam-bot detection in Twitter ads, plagiarism, and more.We present INFOSHIELD, which makes the following contributions: (a) Practical, being scalable and effective on real data, (b) Parameter-free and Principled, requiring no user-defined parameters, (c) Interpretable, finding a document to be the cluster representative, highlighting all the common phrases, and automatically detecting "slots", i.e. phrases that differ in every document; and (d) Generalizable, beating or matching domain-specific methods in Twitter bot detection and human trafficking detection respectively, as well as being language-independent finding clusters in Spanish, Italian, and Japanese. Interpretability is particularly important for the anti human-trafficking domain, where law enforcement must visually inspect ads.Our experiments on real data show that INFOSHIELD correctly identifies Twitter bots with an F1 score over 90% and detects human-trafficking ads with 84% precision. Moreover, it is scalable, requiring about 8 hours for 4 million documents on a stock laptop.
2021-04-18
2021 IEEE 37th International Conference on Data Engineering (ICDE) (published)
doi.org
The Surprising Performance of Simple Baselines for Misinformation Detection
Kellin Pelrine
Jacob Danovitch
Reihaneh Rabbany
As social media becomes increasingly prominent in our day to day lives, it is increasingly important to detect informative content and preve… (see more)nt the spread of disinformation and unverified rumours. While many sophisticated and successful models have been proposed in the literature, they are often compared with older NLP baselines such as SVMs, CNNs, and LSTMs. In this paper, we examine the performance of a broad set of modern transformer-based language models and show that with basic fine-tuning, these models are competitive with and can even significantly outperform recently proposed state-of-the-art methods. We present our framework as a baseline for creating and evaluating new methods for misinformation detection. We further study a comprehensive set of benchmark datasets, and discuss potential data leakage and the need for careful design of the experiments and understanding of datasets to account for confounding variables. As an extreme case example, we show that classifying only based on the first three digits of tweet ids, which contain information on the date, gives state-of-the-art performance on a commonly used benchmark dataset for fake news detection --Twitter16. We provide a simple tool to detect this problem and suggest steps to mitigate it in future datasets.
2021-04-18
Proceedings of the Web Conference 2021 (published)
doi.org
arxiv.org
Ethics of Corporeal, Co-present Robots as Agents of Influence: a Review
AJung Moon
Shalaleh Rismani
H. V. D. Van der Loos
2021-04-13
Current Robotics Reports (published)
doi.org
Towards Causal Federated Learning For Enhanced Robustness and Privacy
Sreya Francis
Irene Tenison
Irina Rish
Federated Learning is an emerging privacy-preserving distributed machine learning approach to building a shared model by performing distribu… (see more)ted training locally on participating devices (clients) and aggregating the local models into a global one. As this approach prevents data collection and aggregation, it helps in reducing associated privacy risks to a great extent. However, the data samples across all participating clients are usually not independent and identically distributed (non-iid), and Out of Distribution(OOD) generalization for the learned models can be poor. Besides this challenge, federated learning also remains vulnerable to various attacks on security wherein a few malicious participating entities work towards inserting backdoors, degrading the generated aggregated model as well as inferring the data owned by participating entities. In this paper, we propose an approach for learning invariant (causal) features common to all participating clients in a federated learning setup and analyze empirically how it enhances the Out of Distribution (OOD) accuracy as well as the privacy of the final learned model.
2021-04-13
ArXiv (preprint)
arxiv.org
arxiv.org
Science-Software Linkage: The Challenges of Traceability between Scientific Knowledge and Software Artifacts
Hideaki Hata
Jin L.C. Guo
Raula Gaikovina Kula
Christoph Treude
Although computer science papers are often accompanied by software artifacts, connecting research papers to their software artifacts and vic… (see more)e versa is not always trivial. First of all, there is a lack of well-accepted standards for how such links should be provided. Furthermore, the provided links, if any, often become outdated: they are affected by link rot when pre-prints are removed, when repositories are migrated, or when papers and repositories evolve independently. In this paper, we summarize the state of the practice of linking research papers and associated source code, highlighting the recent efforts towards creating and maintaining such links. We also report on the results of several empirical studies focusing on the relationship between scientific papers and associated software artifacts, and we outline challenges related to traceability and opportunities for overcoming these challenges.
2021-04-12
ArXiv (preprint)
arxiv.org
arxiv.org
Common Limitations of Image Processing Metrics: A Picture Story
Annika Reinke
Matthias Eisenmann
Minu Dietlinde Tizabi
Carole H. Sudre
TIM RÄDSCH
Michela Antonelli
Tal Arbel
Spyridon Bakas
M. Jorge Cardoso
Veronika Cheplygina
Keyvan Farahani
B. Glocker
DOREEN HECKMANN-NÖTZEL
Fabian Isensee
Pierre Jannin
Charles E. Jr. Kahn
Jens Kleesiek
Tahsin Kurc
Michal Kozubek
Bennett Landman … (see 14 more)
GEERT LITJENS
Klaus Maier-Hein
Bjoern Menze
Henning Müller
Jens Petersen
Mauricio Reyes
Nicola Rieke
Bram Stieltjes
R. Summers
Sotirios A. Tsaftaris
Bram van Ginneken
Annette Kopp-Schneider
PAUL F. JÄGER
Lena Maier-Hein
2021-04-11
ArXiv (preprint)
arxiv.org
arxiv.org
Maintenance of a collection of machines under partial observability: Indexability and computation of Whittle index
Nima Akbarzadeh
Aditya Mahajan
We consider the problem of scheduling maintenance for a collection of machines under partial observations when the state of each machine det… (see more)eriorates stochastically in a Markovian manner. We consider two observational models: first, the state of each machine is not observable at all, and second, the state of each machine is observable only if a service-person visits them. The agent takes a maintenance action, e.g., machine replacement, if he is chosen for the task. We model both problems as restless multi-armed bandit problem and propose the Whittle index policy for scheduling the visits. We show that both models are indexable. For the first model, we derive a closed-form expression for the Whittle index. For the second model, we propose an efficient algorithm to compute the Whittle index by exploiting the qualitative properties of the optimal policy. We present detailed numerical experiments which show that for multiple instances of the model, the Whittle index policy outperforms myopic policy and can be close-to-optimal in different setups.
2021-04-11
arXiv.org (preprint)
dblp.uni-trier.de
Safe Option-Critic: Learning Safety in the Option-Critic Architecture
Arushi Jain
Khimya Khetarpal
Doina Precup
Designing hierarchical reinforcement learning algorithms that exhibit safe behaviour is not only vital for practical applications but also, … (see more)facilitates a better understanding of an agent's decisions. We tackle this problem in the options framework, a particular way to specify temporally abstract actions which allow an agent to use sub-policies with start and end conditions. We consider a behaviour as safe that avoids regions of state-space with high uncertainty in the outcomes of actions. We propose an optimization objective that learns safe options by encouraging the agent to visit states with higher behavioural consistency. The proposed objective results in a trade-off between maximizing the standard expected return and minimizing the effect of model uncertainty in the return. We propose a policy gradient algorithm to optimize the constrained objective function. We examine the quantitative and qualitative behaviour of the proposed approach in a tabular grid-world, continuous-state puddle-world, and three games from the Arcade Learning Environment: Ms.Pacman, Amidar, and Q*Bert. Our approach achieves a reduction in the variance of return, boosts performance in environments with intrinsic variability in the reward structure, and compares favorably both with primitive actions as well as with risk-neutral options.
2021-04-06
The Knowledge Engineering Review (published)
doi.org
arxiv.org
Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark
Vincent Dumoulin
Neil Houlsby
Utku Evci
Xiaohua Zhai
Ross Goroshin
Sylvain Gelly
Hugo Larochelle
Meta and transfer learning are two successful families of approaches to few-shot learning. Despite highly related goals, state-of-the-art ad… (see more)vances in each family are measured largely in isolation of each other. As a result of diverging evaluation norms, a direct or thorough comparison of different approaches is challenging. To bridge this gap, we perform a cross-family study of the best transfer and meta learners on both a large-scale meta-learning benchmark (Meta-Dataset, MD), and a transfer learning benchmark (Visual Task Adaptation Benchmark, VTAB). We find that, on average, large-scale transfer methods (Big Transfer, BiT) outperform competing approaches on MD, even when trained only on ImageNet. In contrast, meta-learning approaches struggle to compete on VTAB when trained and validated on MD. However, BiT is not without limitations, and pushing for scale does not improve performance on highly out-of-distribution MD tasks. In performing this study, we reveal a number of discrepancies in evaluation norms and study some of these in light of the performance gap. We hope that this work facilitates sharing of insights from each community, and accelerates progress on few-shot learning.
2021-04-05
ArXiv (preprint)
arxiv.org
arxiv.org