Publications

The StatCan Dialogue Dataset: Retrieving Data Tables through Conversations with Genuine Intents
Xing Han Lu
Siva Reddy
Harm de Vries
We introduce the StatCan Dialogue Dataset consisting of 19,379 conversation turns between agents working at Statistics Canada and online use… (see more)rs looking for published data tables. The conversations stem from genuine intents, are held in English or French, and lead to agents retrieving one of over 5000 complex data tables. Based on this dataset, we propose two tasks: (1) automatic retrieval of relevant tables based on a on-going conversation, and (2) automatic generation of appropriate agent responses at each turn. We investigate the difficulty of each task by establishing strong baselines. Our experiments on a temporal data split reveal that all models struggle to generalize to future conversations, as we observe a significant drop in performance across both tasks when we move from the validation to the test set. In addition, we find that response generation models struggle to decide when to return a table. Considering that the tasks pose significant challenges to existing models, we encourage the community to develop models for our task, which can be directly used to help knowledge workers find relevant tables for live chat users.
2023-04-30
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics (published)
doi.org
arxiv.org
The study of plasticity has always been about gradients
Blake Aaron Richards
Konrad Paul Kording
2023-04-30
The Journal of Physiology (published)
doi.org
A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games
Samuel Sokota
Ryan D'Orazio
J. Zico Kolter
Nicolas Loizou
Marc Lanctot
Ioannis Mitliagkas
Noam Brown
Christian Kroer
This work studies an algorithm, which we call magnetic mirror descent, that is inspired by mirror descent and the non-Euclidean proximal gra… (see more)dient algorithm. Our contribution is demonstrating the virtues of magnetic mirror descent as both an equilibrium solver and as an approach to reinforcement learning in two-player zero-sum games. These virtues include: 1) Being the first quantal response equilibria solver to achieve linear convergence for extensive-form games with first order feedback; 2) Being the first standard reinforcement learning algorithm to achieve empirically competitive results with CFR in tabular settings; 3) Achieving favorable performance in 3x3 Dark Hex and Phantom Tic-Tac-Toe as a self-play deep reinforcement learning algorithm.
2023-04-30
International Conference on Learning Representations (Accept: poster)
doi.org
openreview.net
What did the T cell see? A deep-learning model of CD8+ T cell activation reveals sharp antigen discrimination at the single cell level
Madison Wahlsten
Amin Akhshi
Sooraj R. Achar
Anagha Yogam Krishnan
Paul François
Grégoire Altan-Bonnet
Immunotherapies such as checkpoint blockade antibodies to block T cell exhaustion have been successful in several cancers such as non-small … (see more)cell lung cancer and melanoma, but limited in others (e.g., pancreatic or prostate carcinomas) owing to differences in tumor antigenicity. Therefore, quantifying tumor antigenicity is critical for successful immunotherapies. Our lab has shown that antigenicity can be encoded in a single parameter derived from bulk cytokine dynamics in ex vivo co-cultures between antigen presenting cells (APCs) and T cells. Here we built a model that can capture the antigenicity seen by individual cells. Using a custom robotic platform, we generated high-throughput kinetics of T cell activation in co-culture with APCs by analyzing cells at various timepoints across a large set of activation conditions. We performed spectral flow cytometry to measure the expression of up to 30 surface markers and intracellular signals per cell. To analyze our content-rich datasets, we designed a machine learning-based model that can classify the antigen seen by an individual cell using expression values from flow cytometry. The model performs well not only at classifying T cells (ROC-AUC > 0.91), but also APCs (ROC-AUC > 0.88), suggesting that each individual leukocyte may register the quality of antigen being presented. Blocking cytokine signaling disrupted this antigen classification. Our study demonstrates that every individual lymphocyte can bridge local and global response to achieve high discriminatory power of antigens.
2023-04-30
Journal of Immunology (published)
doi.org
Estimating individual minimum calibration for deep-learning with predictive performance recovery: An example case of gait surface classification from wearable sensor gait data
Guillaume Lam
Irina Rish
Philippe C. Dixon
2023-04-29
Journal of Biomechanics (unknown)
doi.org
Social Media as a Vector for Escort Ads: A Study on OnlyFans advertisements on Twitter.
Maricarmen Arenas
Pratheeksha Nair
Reihaneh Rabbany
Golnoosh Farnadi
Online sex trafficking is on the rise and a majority of trafficking victims report being advertised online. The use of OnlyFans as a platfor… (see more)m for adult content is also increasing, with Twitter as its main advertising tool. Furthermore, we know that traffickers usually work within a network and control multiple victims. Consequently, we suspect that there may be networks of traffickers promoting multiple OnlyFans accounts belonging to their victims. To this end, we present the first study of OnlyFans advertisements on Twitter in the context of finding organized activities. Preliminary analysis of this space shows that most tweets related to OnlyFans contain generic text, making text-based methods less reliable. Instead, focusing on what ties the authors of these tweets together, we propose a novel method for uncovering coordinated networks of users based on their behaviour. Our method, called Multi-Level Clustering (MLC), combines two levels of clustering that considers both the network structure as well as embedded node attribute information. It focuses jointly on user connections (through mentions) and content (through shared URLs). We apply MLC to real-world data of 2 million tweets pertaining to OnlyFans and analyse the detected groups. We also evaluate our method on synthetically generated data (with injected ground truth) and show its superior performance compared to competitive baselines. Finally, we discuss examples of organized clusters as case studies and provide interesting conclusions to our study.
2023-04-29
Web Science Conference (published)
doi.org
Can ChatGPT Pass An Introductory Level Functional Language Programming Course?
Chuqin Geng
Yihan Zhang
Brigitte Pientka
Xujie Si
The recent introduction of ChatGPT has drawn significant attention from both industry and academia due to its impressive capabilities in sol… (see more)ving a diverse range of tasks, including language translation, text summarization, and computer programming. Its capability for writing, modifying, and even correcting code together with its ease of use and access is already dramatically impacting computer science education. This paper aims to explore how well ChatGPT can perform in an introductory-level functional language programming course. In our systematic evaluation, we treated ChatGPT as one of our students and demonstrated that it can achieve a grade B- and its rank in the class is 155 out of 314 students overall. Our comprehensive evaluation provides valuable insights into ChatGPT's impact from both student and instructor perspectives. Additionally, we identify several potential benefits that ChatGPT can offer to both groups. Overall, we believe that this study significantly clarifies and advances our understanding of ChatGPT's capabilities and potential impact on computer science education.
2023-04-28
ArXiv (preprint)
doi.org
arxiv.org
Leveraging Data Mining Algorithms to Recommend Source Code Changes
AmirHossein Naghshzan
Saeed Khalilazar
Pierre Poilane
Olga Baysal
Latifa Guerrouj
Foutse Khomh
2023-04-28
ArXiv (preprint)
doi.org
arxiv.org
Group Fairness in Reinforcement Learning
Harsh Satija
Alessandro Lazaric
Matteo Pirotta
Joelle Pineau
We pose and study the problem of satisfying fairness in the online Reinforcement Learning (RL) setting. We focus on the group notions of fai… (see more)rness, according to which agents belonging to different groups should have similar performance based on some given measure. We consider the setting of maximizing return in an unknown environment (unknown transition and reward function) and show that it is possible to have RL algorithms that learn the best fair policies without violating the fairness requirements at any point in time during the learning process. In the tabular finite-horizon episodic setting, we provide an algorithm that combines the principle of optimism and pessimism under uncertainty to achieve zero fairness violation with arbitrarily high probability while also maintaining sub-linear regret guarantees. For the high-dimensional Deep-RL setting, we present algorithms based on the performance-difference style approximate policy improvement update step and we report encouraging empirical results on various traditional RL-inspired benchmarks showing that our algorithms display the desired behavior of learning the optimal policy while performing a fair learning process.
2023-04-27
TMLR (accepted)
openreview.net
openreview.net
Overcoming Interpretability and Accuracy Trade-off in Medical Imaging
Ivaxi Sheth
S Ebrahimi Kahou
2023-04-27
MIDL.io/2023/Short_Paper_Track (poster)
openreview.net
openreview.net
Use of Invasive Brain-Computer Interfaces in Pediatric Neurosurgery: Technical and Ethical Considerations
David Bergeron
Christian Iorio-Morin
Marco Bonizzato
Guillaume Lajoie
Nathalie Orr Gaucher
Éric Racine
Alexander G. Weil
2023-04-27
Journal of Child Neurology (published)
doi.org
Lightweight, Pre-trained Transformers for Remote Sensing Timeseries
Gabriel Tseng
Ivan Zvonkov
Ruben Cartuyvels
Mirali Purohit
David Rolnick
Hannah Kerner
Machine learning methods for satellite data have a range of societally relevant applications, but labels used to train models can be difficu… (see more)lt or impossible to acquire. Self-supervision is a natural solution in settings with limited labeled data, but current self-supervised models for satellite data fail to take advantage of the characteristics of that data, including the temporal dimension (which is critical for many applications, such as monitoring crop growth) and availability of data from many complementary sensors (which can significantly improve a model's predictive performance). We present Presto (the Pretrained Remote Sensing Transformer), a model pre-trained on remote sensing pixel-timeseries data. By designing Presto specifically for remote sensing data, we can create a significantly smaller but performant model. Presto excels at a wide variety of globally distributed remote sensing tasks and performs competitively with much larger models while requiring far less compute. Presto can be used for transfer learning or as a feature extractor for simple models, enabling efficient deployment at scale.
2023-04-26
ArXiv (preprint)
doi.org
openreview.net