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Tristan Sylvain

Alumni

Publications

Rejecting Hallucinated State Targets during Planning
Harry Zhao
Tristan Sylvain
Romain Laroche
Doina Precup
Yoshua Bengio
In planning processes of computational decision-making agents, generative or predictive models are often used as "generators" to propose "ta… (voir plus)rgets" representing sets of expected or desirable states. Unfortunately, learned models inevitably hallucinate infeasible targets that can cause delusional behaviors and safety concerns. We first investigate the kinds of infeasible targets that generators can hallucinate. Then, we devise a strategy to identify and reject infeasible targets by learning a target feasibility evaluator. To ensure that the evaluator is robust and non-delusional, we adopted a design choice combining off-policy compatible learning rule, distributional architecture, and data augmentation based on hindsight relabeling. Attaching to a planning agent, the designed evaluator learns by observing the agent’s interactions with the environment and the targets produced by its generator, without the need to change the agent or its generator. Our controlled experiments show significant reductions in delusional behaviors and performance improvements for various kinds of existing agents.
2025-10-06
Proceedings of the 42nd International Conference on Machine Learning (publié)
proceedings.mlr.press
Rejecting Hallucinated State Targets during Planning
Harry Zhao
Mingde Zhao
Tristan Sylvain
Romain Laroche
Doina Precup
Yoshua Bengio
2025-05-01
ICML.cc/2025/Conference (poster)
openreview.net
openreview.net
Identifying and Addressing Delusions for Target-Directed Decision-Making
Harry Zhao
Mingde Zhao
Tristan Sylvain
Romain Laroche
Doina Precup
Yoshua Bengio
We are interested in target-directed agents, which produce targets during decision-time planning, to guide their behaviors and achieve bette… (voir plus)r generalization during evaluation. Improper training of these agents can result in delusions: the agent may come to hold false beliefs about the targets, which cannot be properly rejected, leading to unwanted behaviors and damaging out-of-distribution generalization. We identify different types of delusions by using intuitive examples in carefully controlled environments, and investigate their causes. We demonstrate how delusions can be addressed for agents trained by hindsight relabeling, a mainstream approach in for training target-directed RL agents. We validate empirically the effectiveness of the proposed solutions in correcting delusional behaviors and improving out-of-distribution generalization.
2024-10-12
NeurIPS.cc/2024/Workshop/SafeGenAi (poster)
doi.org
openreview.net
Rejecting Hallucinated State Targets during Planning
Mingde Zhao
Tristan Sylvain
Romain Laroche
Doina Precup
Yoshua Bengio
2024-10-09
ArXiv (prépublication)
arxiv.org
arxiv.org
Self-supervised multimodal learning for group inferences from MRI data: Discovering disorder-relevant brain regions and multimodal links
Alex Fedorov
Eloy Geenjaar
Lei Wu
Tristan Sylvain
Thomas P. DeRamus
Margaux Luck
Maria Misiura
Girish Mittapalle
(Rex) Devon Hjelm
Sergey Plis
Vince D. Calhoun
2023-12-16
NeuroImage (publié)
doi.org
CMIM: Cross-Modal Information Maximization For Medical Imaging
Tristan Sylvain
Francis Dutil
Tess Berthier
Lisa Di Jorio
Margaux Luck
(Rex) Devon Hjelm
Yoshua Bengio
In hospitals, data are siloed to specific information systems that make the same information available under different modalities such as th… (voir plus)e different medical imaging exams the patient undergoes (CT scans, MRI, PET, Ultrasound, etc.) and their associated radiology reports. This offers unique opportunities to obtain and use at train-time those multiple views of the same information that might not always be available at test-time.In this paper, we propose an innovative framework that makes the most of available data by learning good representations of a multi-modal input that are resilient to modality dropping at test-time, using recent advances in mutual information maximization. By maximizing cross-modal information at train time, we are able to outperform several state-of-the-art baselines in two different settings, medical image classification, and segmentation. In particular, our method is shown to have a strong impact on the inference-time performance of weaker modalities.
2021-06-06
IEEE International Conference on Acoustics, Speech, and Signal Processing (publié)
doi.org
Object-Centric Image Generation from Layouts
Tristan Sylvain
Pengchuan Zhang
Yoshua Bengio
(Rex) Devon Hjelm
Shikhar Sharma
2021-05-18
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
arxiv.org
Exploring the Wasserstein metric for time-to-event analysis.
Tristan Sylvain
Margaux Luck
Joseph Paul Cohen
Heloise Cardinal
Andrea Lodi
Yoshua Bengio
2021-01-01
SPACA (publié)
proceedings.mlr.press
Exploring the Wasserstein metric for survival analysis
Tristan Sylvain
Margaux Luck
Joseph Paul Cohen
Andrea Lodi
Yoshua Bengio
Survival analysis is a type of semi-supervised task where the target output (the survival time) is often right-censored. Utilizing this info… (voir plus)rmation is a challenge because it is not obvious how to correctly incorporate these censored examples into a model. We study how three categories of loss functions can take advantage of this information: partial likelihood methods, rank methods, and our own classification method based on a Wasserstein metric (WM) and the non-parametric Kaplan Meier (KM) estimate of the probability density to impute the labels of censored examples. The proposed method predicts the probability distribution of an event, letting us compute survival curves and expected times of survival that are easier to interpret than the rank. We also demonstrate that this approach directly optimizes the expected C-index which is the most common evaluation metric for survival models.
Cross-Modal Information Maximization for Medical Imaging: CMIM
Tristan Sylvain
Francis Dutil
Tess Berthier
Lisa Di Jorio
Margaux Luck
(Rex) Devon Hjelm
Yoshua Bengio
2020-10-20
ArXiv (prépublication)
arxiv.org
arxiv.org
Image-to-image Mapping with Many Domains by Sparse Attribute Transfer
Matthew Amodio
Rim Assouel
Victor Schmidt
Tristan Sylvain
Smita Krishnaswamy
Yoshua Bengio
2020-06-23
ArXiv (prépublication)
arxiv.org
arxiv.org
Joint Learning of Generative Translator and Classifier for Visually Similar Classes
Byungin Yoo
Tristan Sylvain
Yoshua Bengio
Junmo Kim
In this paper, we propose a Generative Translation Classification Network (GTCN) for improving visual classification accuracy in settings wh… (voir plus)ere classes are visually similar and data is scarce. For this purpose, we propose joint learning from a scratch to train a classifier and a generative stochastic translation network end-to-end. The translation network is used to perform on-line data augmentation across classes, whereas previous works have mostly involved domain adaptation. To help the model further benefit from this data-augmentation, we introduce an adaptive fade-in loss and a quadruplet loss. We perform experiments on multiple datasets to demonstrate the proposed method’s performance in varied settings. Of particular interest, training on 40% of the dataset is enough for our model to surpass the performance of baselines trained on the full dataset. When our architecture is trained on the full dataset, we achieve comparable performance with state-of-the-art methods despite using a light-weight architecture.
2020-01-01
IEEE Access (publié)
doi.org
arxiv.org