Publications

Are we making progress in unlearning? Findings from the first NeurIPS unlearning competition
Eleni Triantafillou
Peter Kairouz
Fabian Pedregosa
Jamie Hayes
Meghdad Kurmanji
Kairan Zhao
Vincent Dumoulin
Julio C. S. Jacques Junior
Ioannis Mitliagkas
Jun Wan
Lisheng Sun-Hosoya
Sergio Escalera
Gintare Karolina Dziugaite
Peter Triantafillou
Isabelle Guyon
We present the findings of the first NeurIPS competition on unlearning, which sought to stimulate the development of novel algorithms and in… (see more)itiate discussions on formal and robust evaluation methodologies. The competition was highly successful: nearly 1,200 teams from across the world participated, and a wealth of novel, imaginative solutions with different characteristics were contributed. In this paper, we analyze top solutions and delve into discussions on benchmarking unlearning, which itself is a research problem. The evaluation methodology we developed for the competition measures forgetting quality according to a formal notion of unlearning, while incorporating model utility for a holistic evaluation. We analyze the effectiveness of different instantiations of this evaluation framework vis-a-vis the associated compute cost, and discuss implications for standardizing evaluation. We find that the ranking of leading methods remains stable under several variations of this framework, pointing to avenues for reducing the cost of evaluation. Overall, our findings indicate progress in unlearning, with top-performing competition entries surpassing existing algorithms under our evaluation framework. We analyze trade-offs made by different algorithms and strengths or weaknesses in terms of generalizability to new datasets, paving the way for advancing both benchmarking and algorithm development in this important area.
2024-06-13
ArXiv (preprint)
doi.org
arxiv.org
Exploring validation metrics for offline model-based optimisation with diffusion models
Christopher Beckham
Alexandre Piché
David Vazquez
Chris Pal
2024-06-13
TMLR (accepted)
openreview.net
openreview.net
GIST: Generated Inputs Sets Transferability in Deep Learning
Florian Tambon
Foutse Khomh
Giuliano Antoniol
2024-06-13
ACM Transactions on Software Engineering and Methodology (published)
doi.org
arxiv.org
Turns Out I'm Not Real: Towards Robust Detection of AI-Generated Videos
Qingyuan Liu
Pengyuan Shi
Yun-Yun Tsai
Chengzhi Mao
Junfeng Yang
2024-06-13
ArXiv (preprint)
doi.org
arxiv.org
Grounding Multimodal Large Language Models in Actions
Andrew Szot
Bogdan Mazoure
Harsh Agrawal
(Rex) Devon Hjelm
Zsolt Kira
Alexander T Toshev
2024-06-12
ArXiv (preprint)
doi.org
arxiv.org
PathOCL: Path-Based Prompt Augmentation for OCL Generation with GPT-4
Seif Abukhalaf
Mohammad Hamdaqa
Foutse Khomh
The rapid progress of AI-powered programming assistants, such as GitHub Copilot, has facilitated the development of software applications. T… (see more)hese assistants rely on large language models (LLMs), which are foundation models (FMs) that support a wide range of tasks related to understanding and generating language. LLMs have demonstrated their ability to express UML model specifications using formal languages like the Object Constraint Language (OCL). However, the context size of the prompt is limited by the number of tokens an LLM can process. This limitation becomes significant as the size of UML class models increases. In this study, we introduce PathOCL, a novel path-based prompt augmentation technique designed to facilitate OCL generation. PathOCL addresses the limitations of LLMs, specifically their token processing limit and the challenges posed by large UML class models. PathOCL is based on the concept of chunking, which selectively augments the prompts with a subset of UML classes relevant to the English specification. Our findings demonstrate that PathOCL, compared to augmenting the complete UML class model (UML-Augmentation), generates a higher number of valid and correct OCL constraints using the GPT-4 model. Moreover, the average prompt size crafted using PathOCL significantly decreases when scaling the size of the UML class models.
2024-06-12
Proceedings of the 2024 IEEE/ACM First International Conference on AI Foundation Models and Software Engineering (published)
doi.org
arxiv.org
GLIMPSE: Pragmatically Informative Multi-Document Summarization for Scholarly Reviews
Maxime DARRIN
Ines Arous
Pablo Piantanida
Jackie Cheung
Scientific peer review is essential for the quality of academic publications. However, the increasing number of paper submissions to confere… (see more)nces has strained the reviewing process. This surge poses a burden on area chairs who have to carefully read an ever-growing volume of reviews and discern each reviewer's main arguments as part of their decision process. In this paper, we introduce \sys, a summarization method designed to offer a concise yet comprehensive overview of scholarly reviews. Unlike traditional consensus-based methods, \sys extracts both common and unique opinions from the reviews. We introduce novel uniqueness scores based on the Rational Speech Act framework to identify relevant sentences in the reviews. Our method aims to provide a pragmatic glimpse into all reviews, offering a balanced perspective on their opinions. Our experimental results with both automatic metrics and human evaluation show that \sys generates more discriminative summaries than baseline methods in terms of human evaluation while achieving comparable performance with these methods in terms of automatic metrics.
2024-06-11
ArXiv (preprint)
doi.org
arxiv.org
Global rewards in multi-agent deep reinforcement learning for autonomous mobility on demand systems
Heiko Hoppe
Tobias Enders
Quentin Cappart
Maximilian Schiffer
We study vehicle dispatching in autonomous mobility on demand (AMoD) systems, where a central operator assigns vehicles to customer requests… (see more) or rejects these with the aim of maximizing its total profit. Recent approaches use multi-agent deep reinforcement learning (MADRL) to realize scalable yet performant algorithms, but train agents based on local rewards, which distorts the reward signal with respect to the system-wide profit, leading to lower performance. We therefore propose a novel global-rewards-based MADRL algorithm for vehicle dispatching in AMoD systems, which resolves so far existing goal conflicts between the trained agents and the operator by assigning rewards to agents leveraging a counterfactual baseline. Our algorithm shows statistically significant improvements across various settings on real-world data compared to state-of-the-art MADRL algorithms with local rewards. We further provide a structural analysis which shows that the utilization of global rewards can improve implicit vehicle balancing and demand forecasting abilities. An extended version of our paper, including an appendix, can be found at https://arxiv.org/abs/2312.08884. Our code is available at https://github.com/tumBAIS/GR-MADRL-AMoD.
2024-06-11
Proceedings of the 6th Annual Learning for Dynamics & Control Conference (published)
doi.org
arxiv.org
MAP: Low-compute Model Merging with Amortized Pareto Fronts via Quadratic Approximation
Lu Li
Tianyu Zhang
Zhiqi Bu
Suyuchen Wang
Huan He
Jie Fu
Yonghui Wu
Jiang Bian
Yong Chen
Yoshua Bengio
Model merging has emerged as an effective approach to combine multiple single-task models, fine-tuned from the same pre-trained model, into … (see more)a multitask model. This process typically involves computing a weighted average of the model parameters without any additional training. Existing model-merging methods focus on enhancing average task accuracy. However, interference and conflicts between the objectives of different tasks can lead to trade-offs during model merging. In real-world applications, a set of solutions with various trade-offs can be more informative, helping practitioners make decisions based on diverse preferences. In this paper, we introduce a novel low-compute algorithm, Model Merging with Amortized Pareto Front (MAP). MAP identifies a Pareto set of scaling coefficients for merging multiple models to reflect the trade-offs. The core component of MAP is approximating the evaluation metrics of the various tasks using a quadratic approximation surrogate model derived from a pre-selected set of scaling coefficients, enabling amortized inference. Experimental results on vision and natural language processing tasks show that MAP can accurately identify the Pareto front. To further reduce the required computation of MAP, we propose (1) a Bayesian adaptive sampling algorithm and (2) a nested merging scheme with multiple stages.
2024-06-11
ArXiv (preprint)
doi.org
arxiv.org
MINERS: Multilingual Language Models as Semantic Retrievers
Genta Indra Winata
Ruochen Zhang
David Ifeoluwa Adelani
Words have been represented in a high-dimensional vector space that encodes their semantic similarities, enabling downstream applications su… (see more)ch as retrieving synonyms, antonyms, and relevant contexts. However, despite recent advances in multilingual language models (LMs), the effectiveness of these models' representations in semantic retrieval contexts has not been comprehensively explored. To fill this gap, this paper introduces the MINERS, a benchmark designed to evaluate the ability of multilingual LMs in semantic retrieval tasks, including bitext mining and classification via retrieval-augmented contexts. We create a comprehensive framework to assess the robustness of LMs in retrieving samples across over 200 diverse languages, including extremely low-resource languages in challenging cross-lingual and code-switching settings. Our results demonstrate that by solely retrieving semantically similar embeddings yields performance competitive with state-of-the-art approaches, without requiring any fine-tuning.
2024-06-11
ArXiv (preprint)
doi.org
arxiv.org
When is an Embedding Model More Promising than Another?
Maxime DARRIN
Philippe Formont
Ismail Ben Ayed
Jackie Cheung
Pablo Piantanida
Embedders play a central role in machine learning, projecting any object into numerical representations that can, in turn, be leveraged to p… (see more)erform various downstream tasks. The evaluation of embedding models typically depends on domain-specific empirical approaches utilizing downstream tasks, primarily because of the lack of a standardized framework for comparison. However, acquiring adequately large and representative datasets for conducting these assessments is not always viable and can prove to be prohibitively expensive and time-consuming. In this paper, we present a unified approach to evaluate embedders. First, we establish theoretical foundations for comparing embedding models, drawing upon the concepts of sufficiency and informativeness. We then leverage these concepts to devise a tractable comparison criterion (information sufficiency), leading to a task-agnostic and self-supervised ranking procedure. We demonstrate experimentally that our approach aligns closely with the capability of embedding models to facilitate various downstream tasks in both natural language processing and molecular biology. This effectively offers practitioners a valuable tool for prioritizing model trials.
2024-06-11
ArXiv (preprint)
doi.org
arxiv.org
Simulating federated learning for steatosis detection using ultrasound images
Yue Qi
Pedro Vianna
Alexandre Cadrin-Chênevert
Katleen Blanchet
Emmanuel Montagnon
Eugene Belilovsky
Guy Wolf
Louis-Antoine Mullie
Guy Cloutier
Michael Chassé
An Tang
2024-06-10
Scientific Reports (published)
doi.org