Publications

Probabilistic Mobility Load Balancing for Multi-band 5G and Beyond Networks
Saria Al Lahham
Di Wu
Ekram Hossain
Xue (Steve) Liu
Gregory Dudek
2024-06-09
2024 IEEE International Conference on Communications Workshops (ICC Workshops) (publié)
doi.org
arxiv.org
Promoting Exploration in Memory-Augmented Adam using Critical Momenta
Pranshu Malviya
Goncalo Mordido
Aristide Baratin
Reza Babanezhad Harikandeh
Jerry Huang
Simon Lacoste-Julien
Razvan Pascanu
Sarath Chandar
Adaptive gradient-based optimizers, particularly Adam, have left their mark in training large-scale deep learning models. The strength of su… (voir plus)ch optimizers is that they exhibit fast convergence while being more robust to hyperparameter choice. However, they often generalize worse than non-adaptive methods. Recent studies have tied this performance gap to flat minima selection: adaptive methods tend to find solutions in sharper basins of the loss landscape, which in turn hurts generalization. To overcome this issue, we propose a new memory-augmented version of Adam that promotes exploration towards flatter minima by using a buffer of critical momentum terms during training. Intuitively, the use of the buffer makes the optimizer overshoot outside the basin of attraction if it is not wide enough. We empirically show that our method improves the performance of several variants of Adam on standard supervised language modelling and image classification tasks.
2024-06-09
TMLR (accepté)
doi.org
openreview.net
Tensor-based Space Debris Detection for Satellite Mega-constellations
Olivier Daoust
Hasan Nayir
Irfan Azam
Antoine Lesage-Landry
Gunes Karabulut Kurt
2024-06-09
2024 IEEE International Conference on Communications Workshops (ICC Workshops) (publié)
doi.org
arxiv.org
Why Don't Prompt-Based Fairness Metrics Correlate?
Abdelrahman Zayed
Goncalo Mordido
Ioana Baldini
Sarath Chandar
The widespread use of large language models has brought up essential questions about the potential biases these models might learn. This led… (voir plus) to the development of several metrics aimed at evaluating and mitigating these biases. In this paper, we first demonstrate that prompt-based fairness metrics exhibit poor agreement, as measured by correlation, raising important questions about the reliability of fairness assessment using prompts. Then, we outline six relevant reasons why such a low correlation is observed across existing metrics. Based on these insights, we propose a method called Correlated Fairness Output (CAIRO) to enhance the correlation between fairness metrics. CAIRO augments the original prompts of a given fairness metric by using several pre-trained language models and then selects the combination of the augmented prompts that achieves the highest correlation across metrics. We show a significant improvement in Pearson correlation from 0.3 and 0.18 to 0.90 and 0.98 across metrics for gender and religion biases, respectively. Our code is available at https://github.com/chandar-lab/CAIRO.
2024-06-09
ArXiv (prépublication)
doi.org
arxiv.org
Why Don't Prompt-Based Fairness Metrics Correlate?
Abdelrahman Zayed
Goncalo Mordido
Ioana Baldini
Sarath Chandar
The widespread use of large language models has brought up essential questions about the potential biases these models might learn. This led… (voir plus) to the development of several metrics aimed at evaluating and mitigating these biases. In this paper, we first demonstrate that prompt-based fairness metrics exhibit poor agreement, as measured by correlation, raising important questions about the reliability of fairness assessment using prompts. Then, we outline six relevant reasons why such a low correlation is observed across existing metrics. Based on these insights, we propose a method called Correlated Fairness Output (CAIRO) to enhance the correlation between fairness metrics. CAIRO augments the original prompts of a given fairness metric by using several pre-trained language models and then selects the combination of the augmented prompts that achieves the highest correlation across metrics. We show a significant improvement in Pearson correlation from 0.3 and 0.18 to 0.90 and 0.98 across metrics for gender and religion biases, respectively. Our code is available at https://github.com/chandar-lab/CAIRO.
2024-06-09
ArXiv (prépublication)
doi.org
arxiv.org
Baking Symmetry into GFlowNets
George Ma
Emmanuel Bengio
Yoshua Bengio
Dinghuai Zhang
GFlowNets have exhibited promising performance in generating diverse candidates with high rewards. These networks generate objects increment… (voir plus)ally and aim to learn a policy that assigns probability of sampling objects in proportion to rewards. However, the current training pipelines of GFlowNets do not consider the presence of isomorphic actions, which are actions resulting in symmetric or isomorphic states. This lack of symmetry increases the amount of samples required for training GFlowNets and can result in inefficient and potentially incorrect flow functions. As a consequence, the reward and diversity of the generated objects decrease. In this study, our objective is to integrate symmetries into GFlowNets by identifying equivalent actions during the generation process. Experimental results using synthetic data demonstrate the promising performance of our proposed approaches.
2024-06-08
ArXiv (prépublication)
doi.org
arxiv.org
A Deep Dive into the Trade-Offs of Parameter-Efficient Preference Alignment Techniques
Megh Thakkar
Quentin Fournier
Matthew D Riemer
Pin-Yu Chen
Amal Zouaq
Payel Das
Sarath Chandar
2024-06-07
ArXiv (prépublication)
doi.org
arxiv.org
A Deep Dive into the Trade-Offs of Parameter-Efficient Preference Alignment Techniques
Megh Thakkar
Quentin Fournier
Matthew D Riemer
Pin-Yu Chen
Amal Zouaq
Payel Das
Sarath Chandar
Large language models are first pre-trained on trillions of tokens and then instruction-tuned or aligned to specific preferences. While pre-… (voir plus)training remains out of reach for most researchers due to the compute required, fine-tuning has become affordable thanks to parameter-efficient methods such as LoRA and QLoRA. Alignment is known to be sensitive to the many factors involved, including the quantity and quality of data, the alignment method, and the adapter rank. However, there has not yet been an extensive study of their effect on downstream performance. To address this gap, we conduct an in-depth investigation of the impact of popular choices for three crucial axes: (i) the alignment dataset (HH-RLHF and BeaverTails), (ii) the alignment technique (SFT and DPO), and (iii) the model (LLaMA-1, Vicuna-v1.3, Mistral-7b, and Mistral-7b-Instruct). Our extensive setup spanning over 300 experiments reveals consistent trends and unexpected findings. We observe how more informative data helps with preference alignment, cases where supervised fine-tuning outperforms preference optimization, and how aligning to a distinct preference boosts performance on downstream tasks. Through our in-depth analyses, we put forward key guidelines to help researchers perform more effective parameter-efficient LLM alignment.
2024-06-07
ArXiv (prépublication)
doi.org
arxiv.org
A Deep Dive into the Trade-Offs of Parameter-Efficient Preference Alignment Techniques
Megh Thakkar
Quentin Fournier
Matthew D Riemer
Pin-Yu Chen
Amal Zouaq
Payel Das
Sarath Chandar
Large language models are first pre-trained on trillions of tokens and then instruction-tuned or aligned to specific preferences. While pre-… (voir plus)training remains out of reach for most researchers due to the compute required, fine-tuning has become affordable thanks to parameter-efficient methods such as LoRA and QLoRA. Alignment is known to be sensitive to the many factors involved, including the quantity and quality of data, the alignment method, and the adapter rank. However, there has not yet been an extensive study of their effect on downstream performance. To address this gap, we conduct an in-depth investigation of the impact of popular choices for three crucial axes: (i) the alignment dataset (HH-RLHF and BeaverTails), (ii) the alignment technique (SFT and DPO), and (iii) the model (LLaMA-1, Vicuna-v1.3, Mistral-7b, and Mistral-7b-Instruct). Our extensive setup spanning over 300 experiments reveals consistent trends and unexpected findings. We observe how more informative data helps with preference alignment, cases where supervised fine-tuning outperforms preference optimization, and how aligning to a distinct preference boosts performance on downstream tasks. Through our in-depth analyses, we put forward key guidelines to help researchers perform more effective parameter-efficient LLM alignment.
2024-06-07
ArXiv (prépublication)
doi.org
arxiv.org
Lifelong Learning of Video Diffusion Models From a Single Video Stream
Jinsoo Yoo
Yingchen He
Saeid Naderiparizi
Dylan Green
Gido M. van de Ven
Geoff Pleiss
Frank N. Wood
2024-06-07
ArXiv (prépublication)
arxiv.org
arxiv.org
Lifelong Learning of Video Diffusion Models From a Single Video Stream
Jinsoo Yoo
Yingchen He
Saeid Naderiparizi
Dylan Green
Gido M. van de Ven
Geoff Pleiss
Frank N. Wood
This work demonstrates that training autoregressive video diffusion models from a single, continuous video stream is not only possible but r… (voir plus)emarkably can also be competitive with standard offline training approaches given the same number of gradient steps. Our demonstration further reveals that this main result can be achieved using experience replay that only retains a subset of the preceding video stream. We also contribute three new single video generative modeling datasets suitable for evaluating lifelong video model learning: Lifelong Bouncing Balls, Lifelong 3D Maze, and Lifelong PLAICraft. Each dataset contains over a million consecutive frames from a synthetic environment of increasing complexity.
2024-06-07
ArXiv (prépublication)
arxiv.org
arxiv.org
Lifelong Learning of Video Diffusion Models From a Single Video Stream
Jinsoo Yoo
Yingchen He
Saeid Naderiparizi
Dylan Green
Gido M van de Ven
Geoff Pleiss
Frank N. Wood
2024-06-07
ArXiv (prépublication)
openreview.net
openreview.net