180 citations · 352 across the 20 of their papers we have counts for
12 papers · 1 filter
Target Policy Optimization
Jean Kaddour
In RL, given a prompt, we sample a group of completions from a model and score them. Two questions follow: which completions should gain probability mass, and how should the parame…
REASONING GYM: Reasoning Environments for Reinforcement Learning with Verifiable Rewards
Zafir Stojanovski, Oliver Stanley, Joe Sharratt +4
We introduce Reasoning Gym (RG), a library of reasoning environments for reinforcement learning with verifiable rewards. It provides over 100 data generators and verifiers spanning…
Attention Is All You Need But You Don't Need All Of It For Inference of Large Language Models
Georgy Tyukin, Gbetondji J-S Dovonon, Jean Kaddour +1
The inference demand for LLMs has skyrocketed in recent months, and serving models with low latencies remains challenging due to the quadratic input length complexity of the attent…
No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-based Language Models
Jean Kaddour, Oscar Key, Piotr Nawrot +2
The computation necessary for training Transformer-based language models has skyrocketed in recent years. This trend has motivated research on efficient training algorithms designe…
Early Weight Averaging meets High Learning Rates for LLM Pre-training
Sunny Sanyal, Atula Neerkaje, Jean Kaddour +2
Training Large Language Models (LLMs) incurs significant cost; hence, any strategy that accelerates model convergence is helpful. In this paper, we investigate the ability of a sim…
DAG Learning on the Permutahedron
Valentina Zantedeschi, Luca Franceschi, Jean Kaddour +2
We propose a continuous optimization framework for discovering a latent directed acyclic graph (DAG) from observational data. Our approach optimizes over the polytope of permutatio…
Stop Wasting My Time! Saving Days of ImageNet and BERT Training with Latest Weight Averaging
Jean Kaddour
Training vision or language models on large datasets can take days, if not weeks. We show that averaging the weights of the k latest checkpoints, each collected at the end of an ep…
Causal Machine Learning: A Survey and Open Problems
Jean Kaddour, Aengus Lynch, Qi Liu +2
Causal Machine Learning (CausalML) is an umbrella term for machine learning methods that formalize the data-generation process as a structural causal model (SCM). This perspective…
Evaluating Self-Supervised Learning for Molecular Graph Embeddings
Hanchen Wang, Jean Kaddour, Shengchao Liu +3
Graph Self-Supervised Learning (GSSL) provides a robust pathway for acquiring embeddings without expert labelling, a capability that carries profound implications for molecular gra…
When Do Flat Minima Optimizers Work?
Jean Kaddour, Linqing Liu, Ricardo Silva +1
Recently, flat-minima optimizers, which seek to find parameters in low-loss neighborhoods, have been shown to improve a neural network's generalization performance over stochastic…
Causal Effect Inference for Structured Treatments
Jean Kaddour, Yuchen Zhu, Qi Liu +2
We address the estimation of conditional average treatment effects (CATEs) for structured treatments (e.g., graphs, images, texts). Given a weak condition on the effect, we propose…
Probabilistic Active Meta-Learning
Jean Kaddour, Steindór Sæmundsson, Marc Peter Deisenroth
Data-efficient learning algorithms are essential in many practical applications where data collection is expensive, e.g., in robotics due to the wear and tear. To address this prob…