Publications

Recall, Robustness, and Lexicographic Evaluation
Fernando Diaz
Bhaskar Mitra
2023-02-22
ArXiv (preprint)
arxiv.org
arxiv.org
Unsupervised Layer-wise Score Aggregation for Textual OOD Detection
Maxime Darrin
Guillaume Staerman
Eduardo Dadalto Câmara Gomes
Jackie Ck Cheung
Pablo Piantanida
Pierre Colombo
2023-02-20
ArXiv (preprint)
doi.org
arxiv.org
Interpret Your Care: Predicting the Evolution of Symptoms for Cancer Patients
Rupali Bhati
Jennifer Jones
Audrey Durand
Cancer treatment is an arduous process for patients and causes many side-effects during and post-treatment. The treatment can affect almost … (see more)all body systems and result in pain, fatigue, sleep disturbances, cognitive impairments, etc. These conditions are often under-diagnosed or under-treated. In this paper, we use patient data to predict the evolution of their symptoms such that treatment-related impairments can be prevented or effects meaningfully ameliorated. The focus of this study is on predicting the pain and tiredness level of a patient post their diagnosis. We implement an interpretable decision tree based model called LightGBM on real-world patient data consisting of 20163 patients. There exists a class imbalance problem in the dataset which we resolve using the oversampling technique of SMOTE. Our empirical results show that the value of the previous level of a symptom is a key indicator for prediction and the weighted average deviation in prediction of pain level is 3.52 and of tiredness level is 2.27.
2023-02-19
ArXiv (preprint)
doi.org
arxiv.org
LAGrad: Statically Optimized Differentiable Programming in MLIR
Mai Jacob Peng
Christophe Dubach
2023-02-17
International Conference on Compiler Construction (published)
doi.org
Effects of incoming particle energy and cluster size on the G-value of hydrated electrons.
Alaina Bui
H. Bekerat
Lilian Childress
Jack C Sankey
Jan Seuntjens
Shirin A. Enger
2023-02-16
Physica medica (Testo stampato) (published)
doi.org
MOT: A Multi-Omics Transformer for Multiclass Classification Tumour Types Predictions
Mazid Osseni
Prudencio Tossou
François Laviolette
Jacques Corbeil
2023-02-16
Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (published)
doi.org
Refactoring practices in the context of data-intensive systems
Biruk Asmare Muse
Foutse Khomh
Giuliano Antoniol
2023-02-16
Empirical Software Engineering (published)
doi.org
Learning to Substitute Ingredients in Recipes
Bahare Fatemi
Quentin Duval
Rohit Girdhar
Michal Drozdzal
Adriana Romero Soriano
Recipe personalization through ingredient substitution has the potential to help people meet their dietary needs and preferences, avoid pote… (see more)ntial allergens, and ease culinary exploration in everyone's kitchen. To address ingredient substitution, we build a benchmark, composed of a dataset of substitution pairs with standardized splits, evaluation metrics, and baselines. We further introduce Graph-based Ingredient Substitution Module (GISMo), a novel model that leverages the context of a recipe as well as generic ingredient relational information encoded within a graph to rank plausible substitutions. We show through comprehensive experimental validation that GISMo surpasses the best performing baseline by a large margin in terms of mean reciprocal rank. Finally, we highlight the benefits of GISMo by integrating it in an improved image-to-recipe generation pipeline, enabling recipe personalization through user intervention. Quantitative and qualitative results show the efficacy of our proposed system, paving the road towards truly personalized cooking and tasting experiences.
2023-02-15
ArXiv (preprint)
doi.org
arxiv.org
New wave theory
Paul François
2023-02-15
Development (published)
doi.org
Score-based Diffusion Models in Function Space
Jae Hyun Lim
Nikola B. Kovachki
R. Baptista
Christopher Beckham
Kamyar Azizzadenesheli
Jean Kossaifi
Vikram Voleti
Jiaming Song
Karsten Kreis
Jan Kautz
Chris Pal
Arash Vahdat
Animashree Anandkumar
2023-02-14
ArXiv (preprint)
doi.org
arxiv.org
The Stable Entropy Hypothesis and Entropy-Aware Decoding: An Analysis and Algorithm for Robust Natural Language Generation
Kushal Arora
Timothy O'Donnell
Doina Precup
Jason Aaron Edward Weston
Jackie C.K.Cheung
State-of-the-art language generation models can degenerate when applied to open-ended generation problems such as text completion, story gen… (see more)eration, or dialog modeling. This degeneration usually shows up in the form of incoherence, lack of vocabulary diversity, and self-repetition or copying from the context. In this paper, we postulate that ``human-like'' generations usually lie in a narrow and nearly flat entropy band, and violation of these entropy bounds correlates with degenerate behavior. Our experiments show that this stable narrow entropy zone exists across models, tasks, and domains and confirm the hypothesis that violations of this zone correlate with degeneration. We then use this insight to propose an entropy-aware decoding algorithm that respects these entropy bounds resulting in less degenerate, more contextual, and"human-like"language generation in open-ended text generation settings.
2023-02-14
ArXiv (preprint)
doi.org
arxiv.org
DEUP: Direct Epistemic Uncertainty Prediction
Moksh J. Jain
Salem Lahlou
Hadi Nekoei
Victor I Butoi
Paul Bertin
Jarrid Rector-Brooks
Maksym Korablyov
Yoshua Bengio
Epistemic Uncertainty is a measure of the lack of knowledge of a learner which diminishes with more evidence. While existing work focuses on… (see more) using the variance of the Bayesian posterior due to parameter uncertainty as a measure of epistemic uncertainty, we argue that this does not capture the part of lack of knowledge induced by model misspecification. We discuss how the excess risk, which is the gap between the generalization error of a predictor and the Bayes predictor, is a sound measure of epistemic uncertainty which captures the effect of model misspecification. We thus propose a principled framework for directly estimating the excess risk by learning a secondary predictor for the generalization error and subtracting an estimate of aleatoric uncertainty, i.e., intrinsic unpredictability. We discuss the merits of this novel measure of epistemic uncertainty, and highlight how it differs from variance-based measures of epistemic uncertainty and addresses its major pitfall. Our framework, Direct Epistemic Uncertainty Prediction (DEUP) is particularly interesting in interactive learning environments, where the learner is allowed to acquire novel examples in each round. Through a wide set of experiments, we illustrate how existing methods in sequential model optimization can be improved with epistemic uncertainty estimates from DEUP, and how DEUP can be used to drive exploration in reinforcement learning. We also evaluate the quality of uncertainty estimates from DEUP for probabilistic image classification and predicting synergies of drug combinations.
2023-02-13
TMLR (accepted)
openreview.net
openreview.net