12 citations · 38 across the 12 of their papers we have counts for
12 papers
LLMs can learn self-restraint through iterative self-reflection
Alexandre Piché, Aristides Milios, Dzmitry Bahdanau +1
In order to be deployed safely, Large Language Models (LLMs) must be capable of dynamically adapting their behavior based on their level of knowledge and uncertainty associated wit…
Causal Discovery with Language Models as Imperfect Experts
Stephanie Long, Alexandre Piché, Valentina Zantedeschi +2
Understanding the causal relationships that underlie a system is a fundamental prerequisite to accurate decision-making. In this work, we explore how expert knowledge can be used t…
Can large language models build causal graphs?
Stephanie Long, Tibor Schuster, Alexandre Piché
Building causal graphs can be a laborious process. To ensure all relevant causal pathways have been captured, researchers often have to discuss with clinicians and experts while al…
Exploring validation metrics for offline model-based optimisation with diffusion models
Christopher Beckham, Alexandre Piche, David Vazquez +1
In model-based optimisation (MBO) we are interested in using machine learning to design candidates that maximise some measure of reward with respect to a black box function called…
Implicit Offline Reinforcement Learning via Supervised Learning
Alexandre Piche, Rafael Pardinas, David Vazquez +2
Offline Reinforcement Learning (RL) via Supervised Learning is a simple and effective way to learn robotic skills from a dataset collected by policies of different expertise levels…
Bridging the Gap Between Target Networks and Functional Regularization
Alexandre Piche, Valentin Thomas, Joseph Marino +4
Bootstrapping is behind much of the successes of Deep Reinforcement Learning. However, learning the value function via bootstrapping often leads to unstable training due to fast-ch…
Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels
Sai Rajeswar, Pietro Mazzaglia, Tim Verbelen +4
Controlling artificial agents from visual sensory data is an arduous task. Reinforcement learning (RL) algorithms can succeed but require large amounts of interactions between the…
Bridging the Gap Between Target Networks and Functional Regularization
Alexandre Piché, Valentin Thomas, Rafael Pardinas +4
Bootstrapping is behind much of the successes of deep Reinforcement Learning. However, learning the value function via bootstrapping often leads to unstable training due to fast-ch…
Iterative Amortized Policy Optimization
Joseph Marino, Alexandre Piché, Alessandro Davide Ialongo +1
Policy networks are a central feature of deep reinforcement learning (RL) algorithms for continuous control, enabling the estimation and sampling of high-value actions. From the va…
Reward Estimation for Variance Reduction in Deep Reinforcement Learning
Joshua Romoff, Peter Henderson, Alexandre Piché +2
Reinforcement Learning (RL) agents require the specification of a reward signal for learning behaviours. However, introduction of corrupt or stochastic rewards can yield high varia…
Adaptive Stochastic Dual Coordinate Ascent for Conditional Random Fields
Rémi Le Priol, Alexandre Piché, Simon Lacoste-Julien
This work investigates the training of conditional random fields (CRFs) via the stochastic dual coordinate ascent (SDCA) algorithm of Shalev-Shwartz and Zhang (2016). SDCA enjoys a…
Bayesian Nonparametric Modeling of Heterogeneous Groups of Censored Data
Alexandre Piché, Russell Steele, Ian Shrier +1
Datasets containing large samples of time-to-event data arising from several small heterogeneous groups are commonly encountered in statistics. This presents problems as they canno…