72 citations · 81 across the 13 of their papers we have counts for
7 papers · 1 filter
A Query Is Not a Commitment: Learning to Correct Expert Answers in Online Deferral
Yannis Montreuil, Axel Carlier, Lai Xing Ng +1
An inaccurate expert can still provide useful information after correction. We study online learning to defer in which the learner chooses an expert and fixes a correction function…
Toward On-Chip Training of Spiking Neural Networks for Dense Event-Based Vision
Maxime Vaillant, Axel Carlier, Lai Xing Ng +2
Event cameras provide low-latency, asynchronous visual sensing for resource-constrained robotics. Spiking neural networks (SNNs) process event streams naturally, but training deep…
Beyond Augmented-Action Surrogates for Multi-Expert Learning-to-Defer
Yannis Montreuil, Axel Carlier, Lai Xing Ng +1
A learning-to-defer (L2D) system decides, for each input, whether to predict on its own or to hand it to one of several available experts. The very well established recipe trains c…
Online Learning-to-Defer with Varying Experts
Dang Hoang Duy, Yannis Montreuil, Maxime Meyer +3
Learning-to-Defer (L2D) methods route each query either to a predictive model or to external experts. Real-world deployments require handling streaming data, changing expert availa…
SpikeCLR: Contrastive Self-Supervised Learning for Few-Shot Event-Based Vision using Spiking Neural Networks
Maxime Vaillant, Axel Carlier, Lai Xing Ng +2
Event-based vision sensors provide significant advantages for high-speed perception, including microsecond temporal resolution, high dynamic range, and low power consumption. When…
Consistent Learning-to-Defer with Expert-Conditional Advice
Yannis Montreuil, Leïna Montreuil, Axel Carlier +2
We introduce learning-to-defer with advice, a new setting in which a system learns both which expert should handle an input and which information that expert should receive. The po…
Learning-to-Defer in Non-Stationary Time Series via Switching State-Space Models
Yannis Montreuil, Letian Yu, Axel Carlier +2
Learning-to-defer (L2D) lets a predictor decide, at each round, whether to issue its own forecast or pay for an expert's. In non-stationary time series this decision must keep adap…