Publications

Price forecasting in the Ontario electricity market via TriConvGRU hybrid model: Univariate vs. multivariate frameworks
Behdad Ehsani
Pierre-Olivier Pineau
Laurent Charlin
Electricity price forecasting is a challenging task for decision-makers in deregulated power markets due to the inherent characteristics of … (see more)electricity prices, e.g., high frequency and volatility. Therefore, accurate forecasting of electricity prices can assist market participants in maximizing their profit. Accordingly, we proposed a novel hybrid Deep Learning model to forecast one-step, two-step, and three-step ahead Ontario electricity prices based on a Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU). Our model consists of three consecutive CNN-GRU models combined in parallel with different input data. We downsampled input data via pooling layers at the beginning of two streams of the model to capture different frequencies of price patterns concurrently. Also, a set of external variables, including previous prices, electricity load, generation, import and export, and weather data, were considered in our forecasting models to test whether these features improve the efficiency of the models. Finally, three experiments in various weeks of 2022 were carried out in the Ontario electricity market to assess the model. The results indicate that the proposed model reduced the forecasting error significantly by 63.3% in the first experiment, 41.8% in the second, and 28.2% in the third, on average, with respect to a Root Mean Square Error (RMSE). Also, the proposed model was compared with outperformed several baseline models, including statistical time-series, Machine Learning, and Deep Learning models. Furthermore, the comparison of results in univariate and multivariate settings indicated that adding variables to forecasting models did not help reduce forecasting errors.
2022-12-31
SSRN Electronic Journal (unknown)
doi.org
PromptMix: A Class Boundary Augmentation Method for Large Language Model Distillation
Gaurav Sahu
Olga Vechtomova
Dzmitry Bahdanau
Issam Laradji
Data augmentation is a widely used technique to address the problem of text classification when there is a limited amount of training data. … (see more)Recent work often tackles this problem using large language models (LLMs) like GPT3 that can generate new examples given already available ones. In this work, we propose a method to generate more helpful augmented data by utilizing the LLM's abilities to follow instructions and perform few-shot classifications. Our specific PromptMix method consists of two steps: 1) generate challenging text augmentations near class boundaries; however, generating borderline examples increases the risk of false positives in the dataset, so we 2) relabel the text augmentations using a prompting-based LLM classifier to enhance the correctness of labels in the generated data. We evaluate the proposed method in challenging 2-shot and zero-shot settings on four text classification datasets: Banking77, TREC6, Subjectivity (SUBJ), and Twitter Complaints. Our experiments show that generating and, crucially, relabeling borderline examples facilitates the transfer of knowledge of a massive LLM like GPT3.5-turbo into smaller and cheaper classifiers like DistilBERT
2022-12-31
EMNLP (published)
doi.org
openreview.net
Prototype-Sample Relation Distillation: Towards Replay-Free Continual Learning
Nader Asadi
MohammadReza Davari
Sudhir Mudur
Rahaf Aljundi
Eugene Belilovsky
In Continual learning (CL) balancing effective adaptation while combating catastrophic forgetting is a central challenge. Many of the recent… (see more) best-performing methods utilize various forms of prior task data, e.g. a replay buffer, to tackle the catastrophic forgetting problem. Having access to previous task data can be restrictive in many real-world scenarios, for example when task data is sensitive or proprietary. To overcome the necessity of using previous tasks' data, in this work, we start with strong representation learning methods that have been shown to be less prone to forgetting. We propose a holistic approach to jointly learn the representation and class prototypes while maintaining the relevance of old class prototypes and their embedded similarities. Specifically, samples are mapped to an embedding space where the representations are learned using a supervised contrastive loss. Class prototypes are evolved continually in the same latent space, enabling learning and prediction at any point. To continually adapt the prototypes without keeping any prior task data, we propose a novel distillation loss that constrains class prototypes to maintain relative similarities as compared to new task data. This method yields state-of-the-art performance in the task-incremental setting, outperforming methods relying on large amounts of data, and provides strong performance in the class-incremental setting without using any stored data points.
2022-12-31
ICML (published)
doi.org
proceedings.mlr.press
Publisher Correction: Advancing ethics review practices in AI research
Madhulika Srikumar
Rebecca Finlay
Grace M. Abuhamad
Carolyn Ashurst
Rosie Campbell
Emily Campbell-Ratcliffe
Hudson Hongo
Sara Rene Jordan
Joseph Lindley
Aviv Ovadya
Joelle Pineau
2022-12-31
Nature Machine Intelligence (published)
doi.org
Questions Are All You Need to Train a Dense Passage Retriever
Devendra Singh Sachan
Mike Lewis
Dani Yogatama
Luke Zettlemoyer
Joelle Pineau
Manzil Zaheer
We introduce ART, a new corpus-level autoencoding approach for training dense retrieval models that does not require any labeled training da… (see more)ta. Dense retrieval is a central challenge for open-domain tasks, such as Open QA, where state-of-the-art methods typically require large supervised datasets with custom hard-negative mining and denoising of positive examples. ART, in contrast, only requires access to unpaired inputs and outputs (e.g. questions and potential answer documents). It uses a new document-retrieval autoencoding scheme, where (1) an input question is used to retrieve a set of evidence documents, and (2) the documents are then used to compute the probability of reconstructing the original question. Training for retrieval based on question reconstruction enables effective unsupervised learning of both document and question encoders, which can be later incorporated into complete Open QA systems without any further finetuning. Extensive experiments demonstrate that ART obtains state-of-the-art results on multiple QA retrieval benchmarks with only generic initialization from a pre-trained language model, removing the need for labeled data and task-specific losses.
2022-12-31
Transactions of the Association for Computational Linguistics (published)
doi.org
arxiv.org
A rapid review for developing a co-design framework for a pediatric surgical communication application
Michelle Cwintal
Hamed Ranjbar
Parsa Bandamiri
Elena Guadagno
Esli Osmanlliu
Dan Poenaru
2022-12-31
Journal of Pediatric Surgery (published)
doi.org
Recall as a Measure of Ranking Robustness
Fernando Diaz
Bhaskar Mitra
2022-12-31
arXiv.org (preprint)
doi.org
Relationship between prediction accuracy and feature importance reliability: An empirical and theoretical study
Jianzhong Chen
Leon Qi Rong Ooi
Leon Qi Rong Ooi
Trevor Wei Kiat Tan
Shaoshi Zhang
Jingwei Li
Christopher L. Asplund
Simon B Eickhoff
Danilo Bzdok
Avram J Holmes
B.T. Thomas Yeo
There is significant interest in using neuroimaging data to predict behavior. The predictive models are often interpreted by the computation… (see more) of feature importance, which quantifies the predictive relevance of an imaging feature. Tian and Zalesky (2021) suggest that feature importance estimates exhibit low split-half reliability, as well as a trade-off between prediction accuracy and feature importance reliability across parcellation resolutions. However, it is unclear whether the trade-off between prediction accuracy and feature importance reliability is universal. Here, we demonstrate that, with a sufficient sample size, feature importance (operationalized as Haufe-transformed weights) can achieve fair to excellent split-half reliability. With a sample size of 2600 participants, Haufe-transformed weights achieve average intra-class correlation coefficients of 0.75, 0.57 and 0.53 for cognitive, personality and mental health measures respectively. Haufe-transformed weights are much more reliable than original regression weights and univariate FC-behavior correlations. Original regression weights are not reliable even with 2600 participants. Intriguingly, feature importance reliability is strongly positively correlated with prediction accuracy across phenotypes. Within a particular behavioral domain, there is no clear relationship between prediction performance and feature importance reliability across regression models. Furthermore, we show mathematically that feature importance reliability is necessary, but not sufficient, for low feature importance error. In the case of linear models, lower feature importance error is mathematically related to lower prediction error. Therefore, higher feature importance reliability might yield lower feature importance error and higher prediction accuracy. Finally, we discuss how our theoretical results relate with the reliability of imaging features and behavioral measures. Overall, the current study provides empirical and theoretical insights into the relationship between prediction accuracy and feature importance reliability.
2022-12-31
NeuroImage (published)
doi.org
A reproducible benchmark of resting-state fMRI denoising strategies using fMRIPrep and Nilearn
Hao-Ting Wang
Steven L. Meisler
Hanad Sharmarke
Natasha Clarke
Nicolas Gensollen
Christopher J Markiewicz
Fraçois Paugam
Bertrand Thirion
Lune P Bellec
Reducing contributions from non-neuronal sources is a crucial step in functional magnetic resonance imaging (fMRI) analyses. Many viable str… (see more)ategies for denoising fMRI are used in the literature, and practitioners rely on denoising benchmarks for guidance in the selection of an appropriate choice for their study. However, fMRI denoising software is an ever-evolving field, and the benchmarks can quickly become obsolete as the techniques or implementations change. In this work, we present a fully reproducible denoising benchmark featuring a range of denoising strategies and evaluation metrics, built primarily on the fMRIPrep and Nilearn software packages. We apply this reproducible benchmark to investigate the robustness of the conclusions across two different datasets and two versions of fMRIPrep. The majority of benchmark results were consistent with prior literature. Scrubbing, a technique which excludes time points with excessive motion, combined with global signal regression, is generally effective at noise removal. Scrubbing however disrupts the continuous sampling of brain images and is incompatible with some statistical analyses, e.g. auto-regressive modeling. In this case, a simple strategy using motion parameters, average activity in select brain compartments, and global signal regression should be preferred. Importantly, we found that certain denoising strategies behave inconsistently across datasets and/or versions of fMRIPrep, or had a different behavior than in previously published benchmarks, especially ICA-AROMA. These results demonstrate that a reproducible denoising benchmark can effectively assess the robustness of conclusions across multiple datasets and software versions. Technologies such as BIDS-App, the Jupyter Book and Neurolibre provided the infrastructure to publish the metadata and report figures. Readers can reproduce the report figures beyond the ones reported in the published manuscript. With the denoising benchmark, we hope to provide useful guidelines for the community, and that our software infrastructure will facilitate continued development as the state-of-the-art advances.
2022-12-31
(published)
www.semanticscholar.org
Responsible AI Considerations in Text Summarization Research: A Review of Current Practices
Yu Lu Liu
Meng Cao
Su Lin Blodgett
Jackie CK Cheung
A.R. Olteanu
Adam Trischler
2022-12-31
EMNLP (Findings) (published)
doi.org
openreview.net
Re-Weighted Softmax Cross-Entropy to Control Forgetting in Federated Learning
Gwen Legate
Lucas Caccia
Eugene Belilovsky
In Federated Learning a global model is learned by aggregating model updates computed at a set of independent client nodes. To reduce commun… (see more)ication costs, multiple gradient steps are performed at each node prior to aggregation. A key challenge in this setting is data heterogeneity across clients resulting in differing local objectives. This can lead clients to overly minimize their own local objective consequently diverging from the global solution. We demonstrate that individual client models experience a catastrophic forgetting with respect to data from other clients and propose an efficient approach that modifies the cross-entropy objective on a per-client basis by re-weighting the softmax logits prior to computing the loss. This approach shields classes outside a client’s label set from abrupt representation change and we empirically demonstrate it can alleviate client forgetting and provide consistent improvements to standard federated learning algorithms. Our method is particularly beneficial under the most challenging federated learning settings where data heterogeneity is high and client participation in each round is low.
2022-12-31
CoLLAs (published)
doi.org
proceedings.mlr.press
Robust and Versatile Bipedal Jumping Control through Multi-Task Reinforcement Learning
Zhongyu Li
Xue Bin Peng
Pieter Abbeel
Sergey Levine
Glen Berseth
Koushil Sreenath
2022-12-31
arXiv.org (preprint)
doi.org