Publications

Intra-host viral populations of SARS-CoV-2 in immunosuppressed patients with hematologic cancers

Dominique Fournelle

Fatima Mostefai

Elsa Brunet-Ratnasingham

Raphaël Poujol

Jean-Christophe Grenier

José Héctor Gálvez

Amélie Pagliuzza

Inès Levade

Sandrine Moreira

Simon Grandjean Lapierre

Nicolas Chomont

Daniel E. Kaufmann

Morgan Craig

Julie G. Hussin

Throughout the SARS-CoV-2 pandemic, several variants of concern (VOC) have been identified, many of which share recurrent mutations in the s… (voir plus)pike protein’s receptor binding domain (RBD). This region coincides with known epitopes and can therefore have an impact on immune escape. Protracted infections in immunosuppressed patients have been hypothesized to lead to an enrichment of such mutations and therefore drive evolution towards VOCs. Here, we show that immunosuppressed patients with hematologic cancers develop distinct populations with immune escape mutations throughout the course of their infection. Notably, by investigating the co-occurrence of substitutions on individual sequencing reads in the RBD, we found quasispecies harboring mutations that confer resistance to known monoclonal antibodies (mAbs) such as S:E484K and S:E484A. Furthermore, we provide the first evidence for a viral reservoir based on intra-host phylogenetics. Our results on viral reservoirs can shed light on protracted infections interspersed with periods where the virus is undetectable as well as an alternative explanation for some long-COVID cases. Our findings also highlight that protracted infections should be treated with combination therapies rather than by a single mAbs to clear pre-existing resistant mutations.

2022-10-19

bioRxiv (prépublication)

Simulating Human Gaze with Neural Visual Attention

Leo Schwinn

Doina Precup

Bjoern Eskofier

Dario Zanca

2022-10-19

NeurIPS.cc/2022/Workshop/GMML (présentation orale)

The Paradox of Choice: On the Role of Attention in Hierarchical Reinforcement Learning

Andrei Cristian Nica

Khimya Khetarpal

Doina Precup

Decision-making AI agents are often faced with two important challenges: the depth of the planning horizon, and the branching factor due to … (voir plus)having many choices. Hierarchical reinforcement learning methods aim to solve the first problem, by providing shortcuts that skip over multiple time steps. To cope with the breadth, it is desirable to restrict the agent's attention at each step to a reasonable number of possible choices. The concept of affordances (Gibson, 1977) suggests that only certain actions are feasible in certain states. In this work, we first characterize "affordances" as a "hard" attention mechanism that strictly limits the available choices of temporally extended options. We then investigate the role of hard versus soft attention in training data collection, abstract value learning in long-horizon tasks, and handling a growing number of choices. To this end, we present an online, model-free algorithm to learn affordances that can be used to further learn subgoal options. Finally, we identify and empirically demonstrate the settings in which the "paradox of choice" arises, i.e. when having fewer but more meaningful choices improves the learning speed and performance of a reinforcement learning agent.

2022-10-19

NeurIPS.cc/2022/Workshop/Attention (poster)

Timeliness of reporting of SARS-CoV-2 seroprevalence results and their utility for infectious disease surveillance

Claire Donnici

Natasha Ilincic

Christian Cao

Caseng Zhang

Gabriel Deveaux

David A. Clifton

David L Buckeridge

Niklas Bobrovitz

Rahul K. Arora

2022-10-19

Epidemics (publié)

Gintare Karolina Dziugaite

Unmasking the Lottery Ticket Hypothesis: Efficient Adaptive Pruning for Finding Winning Tickets

Mansheej Paul

Feng Chen

Brett W. Larsen

Jonathan Frankle

Surya Ganguli

Modern deep learning involves training costly, highly overparameterized networks, thus motivating the search for sparser networks that requi… (voir plus)re less compute and memory but can still be trained to the same accuracy as the full network (i.e. matching). Iterative magnitude pruning (IMP) is a state of the art algorithm that can find such highly sparse matching subnetworks, known as winning tickets, that can be retrained from initialization or an early training stage. IMP operates by iterative cycles of training, masking a fraction of smallest magnitude weights, rewinding unmasked weights back to an early training point, and repeating. Despite its simplicity, the underlying principles for when and how IMP finds winning tickets remain elusive. In particular, what useful information does an IMP mask found at the end of training convey to a rewound network near the beginning of training? We find that—at higher sparsities—pairs of pruned networks at successive pruning iterations are connected by a linear path with zero error barrier if and only if they are matching. This indicates that masks found at the end of training encodes information about the identity of an axial subspace that intersects a desired linearly connected mode of a matching sublevel set. We leverage this observation to design a simple adaptive pruning heuristic for speeding up the discovery of winning tickets and achieve a 30% reduction in computation time on CIFAR-100. These results make progress toward demystifying the existence of winning tickets with an eye towards enabling the development of more efficient pruning algorithms.

2022-10-19

NeurIPS.cc/2022/Workshop/HITY (accepté)

Causal inference from text: A commentary

Dhanya Sridhar

David Blei

2022-10-18

Science Advances (publié)

Aligning MAGMA by Few-Shot Learning and Finetuning

Jean-Charles Layoun

Alexis Roger

Irina Rish

2022-10-17

ArXiv (prépublication)

Generalizing in the Real World with Representation Learning

Tegan Maharaj

2022-10-17

ArXiv (prépublication)

Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation

Haolun Wu

Chen Ma

Yingxue Zhang

Xue Liu

Ruiming Tang

Mark J. Coates

2022-10-16

Proceedings of the 31st ACM International Conference on Information & Knowledge Management (publié)

Lifelong Online Learning from Accumulated Knowledge

Changjian Shui

William Wang

Ihsen Hedhli

Chi Man Wong

Feng Wan

Boyu Wang

Christian Gagné

In this article, we formulate lifelong learning as an online transfer learning procedure over consecutive tasks, where learning a given task… (voir plus) depends on the accumulated knowledge. We propose a novel theoretical principled framework, lifelong online learning, where the learning process for each task is in an incremental manner. Specifically, our framework is composed of two-level predictions: the prediction information that is solely from the current task; and the prediction from the knowledge base by previous tasks. Moreover, this article tackled several fundamental challenges: arbitrary or even non-stationary task generation process, an unknown number of instances in each task, and constructing an efficient accumulated knowledge base. Notably, we provide a provable bound of the proposed algorithm, which offers insights on the how the accumulated knowledge improves the predictions. Finally, empirical evaluations on both synthetic and real datasets validate the effectiveness of the proposed algorithm.

2022-10-16

ACM Transactions on Knowledge Discovery from Data (publié)

OptEmbed: Learning Optimal Embedding Table for Click-through Rate Prediction

Fuyuan Lyu

Xing Tang

Hao Zhu

Huifeng Guo

Yingxue Zhang

Ruiming Tang

Xue Liu

Click-through rate (CTR) prediction model usually consists of three components: embedding table, feature interaction layer, and classifier. … (voir plus)Learning embedding table plays a fundamental role in CTR prediction from the view of the model performance and memory usage. The embedding table is a two-dimensional tensor, with its axes indicating the number of feature values and the embedding dimension, respectively. To learn an efficient and effective embedding table, recent works either assign various embedding dimensions for feature fields and reduce the number of embeddings respectively or mask the embedding table parameters. However, all these existing works cannot get an optimal embedding table. On the one hand, various embedding dimensions still require a large amount of memory due to the vast number of features in the dataset. On the other hand, decreasing the number of embeddings usually suffers from performance degradation, which is intolerable in CTR prediction. Finally, pruning embedding parameters will lead to a sparse embedding table, which is hard to be deployed. To this end, we propose an optimal embedding table learning framework OptEmbed, which provides a practical and general method to find an optimal embedding table for various base CTR models. Specifically, we propose pruning the redundant embeddings regarding corresponding features' importance by learnable pruning thresholds. Furthermore, we consider assigning various embedding dimensions as one single candidate architecture. To efficiently search the optimal embedding dimensions, we design a uniform embedding dimension sampling scheme to equally train all candidate architectures, meaning architecture-related parameters and learnable thresholds are trained simultaneously in one supernet. We then propose an evolution search method based on the supernet to find the optimal embedding dimensions for each field. Experiments on public datasets show that OptEmbed can learn a compact embedding table which can further improve the model performance.

2022-10-16

Proceedings of the 31st ACM International Conference on Information & Knowledge Management (publié)