Publications

RGFN: Synthesizable Molecular Generation Using GFlowNets
Michał Koziarski
Andrei Rekesh
Dmytro Shevchuk
Almer M. van der Sloot
Piotr Gaiński
Yoshua Bengio
Cheng-Hao Liu
Mike Tyers
Robert A. Batey
2024-09-25
NeurIPS.cc/2024/Conference (poster)
doi.org
openreview.net
Self-Consuming Generative Models with Curated Data Provably Optimize Human Preferences
Damien Ferbach
Quentin Bertrand
Joey Bose
Gauthier Gidel
The rapid progress in generative models has resulted in impressive leaps in generation quality, blurring the lines between synthetic and rea… (voir plus)l data. Web-scale datasets are now prone to the inevitable contamination by synthetic data, directly impacting the training of future generated models. Already, some theoretical results on self-consuming generative models (a.k.a., iterative retraining) have emerged in the literature, showcasing that either model collapse or stability could be possible depending on the fraction of generated data used at each retraining step. However, in practice, synthetic data is often subject to human feedback and curated by users before being used and uploaded online. For instance, many interfaces of popular text-to-image generative models, such as Stable Diffusion or Midjourney, produce several variations of an image for a given query which can eventually be curated by the users. In this paper, we theoretically study the impact of data curation on iterated retraining of generative models and show that it can be seen as an \emph{implicit preference optimization mechanism}. However, unlike standard preference optimization, the generative model does not have access to the reward function or negative samples needed for pairwise comparisons. Moreover, our study doesn't require access to the density function, only to samples. We prove that, if the data is curated according to a reward model, then the expected reward of the iterative retraining procedure is maximized. We further provide theoretical results on the stability of the retraining loop when using a positive fraction of real data at each step. Finally, we conduct illustrative experiments on both synthetic datasets and on CIFAR10 showing that such a procedure amplifies biases of the reward model.
2024-09-25
NeurIPS.cc/2024/Conference (spotlight)
doi.org
openreview.net
Simplifying Constraint Inference with Inverse Reinforcement Learning
Adriana Hugessen
Harley Wiltzer
Glen Berseth
2024-09-25
NeurIPS.cc/2024/Conference (poster)
openreview.net
openreview.net
Soft Prompt Threats: Attacking Safety Alignment and Unlearning in Open-Source LLMs through the Embedding Space
Leo Schwinn
David Dobre
Sophie Xhonneux
Gauthier Gidel
Stephan Günnemann
Current research in adversarial robustness of LLMs focuses on discrete input manipulations in the natural language space, which can be direc… (voir plus)tly transferred to closed-source models. However, this approach neglects the steady progression of open-source models. As open-source models advance in capability, ensuring their safety also becomes increasingly imperative. Yet, attacks tailored to open-source LLMs that exploit full model access remain largely unexplored. We address this research gap and propose the embedding space attack, which directly attacks the continuous embedding representation of input tokens. We find that embedding space attacks circumvent model alignments and trigger harmful behaviors more efficiently than discrete attacks or model fine-tuning. Furthermore, we present a novel threat model in the context of unlearning and show that embedding space attacks can extract supposedly deleted information from unlearned LLMs across multiple datasets and models. Our findings highlight embedding space attacks as an important threat model in open-source LLMs. Trigger Warning: the appendix contains LLM-generated text with violence and harassment.
2024-09-25
NeurIPS.cc/2024/Conference (poster)
doi.org
openreview.net
Stress-Testing Capability Elicitation With Password-Locked Models
Ryan Greenblatt
Fabien Roger
Dmitrii Krasheninnikov
David Scott Krueger
2024-09-25
NeurIPS.cc/2024/Conference (poster)
doi.org
openreview.net
The Factorization Curse: Which Tokens You Predict Underlie the Reversal Curse and More
Ouail Kitouni
Niklas Nolte
Adina Williams
Michael Rabbat
Diane Bouchacourt
Mark Ibrahim
2024-09-25
NeurIPS.cc/2024/Conference (poster)
openreview.net
openreview.net
The High Line: Exact Risk and Learning Rate Curves of Stochastic Adaptive Learning Rate Algorithms
Elizabeth Collins-Woodfin
Inbar Seroussi
Begoña García Malaxechebarría
Andrew Mackenzie
Elliot Paquette
Courtney Paquette
2024-09-25
NeurIPS.cc/2024/Conference (poster)
openreview.net
openreview.net
On the Scalability of Certified Adversarial Robustness with Generated Data
Thomas Altstidl
David Dobre
Arthur Kosmala
Bjoern Eskofier
Gauthier Gidel
Leo Schwinn
Certified defenses against adversarial attacks offer formal guarantees on the robustness of a model, making them more reliable than empirica… (voir plus)l methods such as adversarial training, whose effectiveness is often later reduced by unseen attacks. Still, the limited certified robustness that is currently achievable has been a bottleneck for their practical adoption. Gowal et al. and Wang et al. have shown that generating additional training data using state-of-the-art diffusion models can considerably improve the robustness of adversarial training. In this work, we demonstrate that a similar approach can substantially improve deterministic certified defenses but also reveal notable differences in the scaling behavior between certified and empirical methods. In addition, we provide a list of recommendations to scale the robustness of certified training approaches. Our approach achieves state-of-the-art deterministic robustness certificates on CIFAR-10 for the
2024-09-25
NeurIPS.cc/2024/Conference (poster)
openreview.net
openreview.net
On the Scalability of GNNs for Molecular Graphs
Maciej Sypetkowski
Frederik Wenkel
Farimah Poursafaei
Nia Dickson
Karush Suri
Philip Fradkin
Dominique Beaini
Scaling deep learning models has been at the heart of recent revolutions in language modelling and image generation. Practitioners have obse… (voir plus)rved a strong relationship between model size, dataset size, and performance. However, structure-based architectures such as Graph Neural Networks (GNNs) are yet to show the benefits of scale mainly due to the lower efficiency of sparse operations, large data requirements, and lack of clarity about the effectiveness of various architectures. We address this drawback of GNNs by studying their scaling behavior. Specifically, we analyze message-passing networks, graph Transformers, and hybrid architectures on the largest public collection of 2D molecular graphs. For the first time, we observe that GNNs benefit tremendously from the increasing scale of depth, width, number of molecules, number of labels, and the diversity in the pretraining datasets, resulting in a 30.25% improvement when scaling to 1 billion parameters and 28.98% improvement when increasing size of dataset to eightfold. We further demonstrate strong finetuning scaling behavior on 38 tasks, outclassing previous large models. We hope that our work paves the way for an era where foundational GNNs drive pharmaceutical drug discovery.
2024-09-25
NeurIPS.cc/2024/Conference (poster)
doi.org
openreview.net
Towards a "Universal Translator" for Neural Dynamics at Single-Cell, Single-Spike Resolution
Yizi Zhang
Yanchen Wang
Donato M. Jiménez-Benetó
Zixuan Wang
Mehdi Azabou
Blake Richards
Renee Tung
Olivier Winter
International Brain Laboratory
Eva L Dyer
Liam Paninski
Cole Lincoln Hurwitz
2024-09-25
NeurIPS.cc/2024/Conference (poster)
openreview.net
openreview.net
Trajectory Flow Matching with Applications to Clinical Time Series Modelling
Xi Zhang
Yuan Pu
Yuki Kawamura
Andrew Loza
Yoshua Bengio
Dennis Shung
Alexander Tong
Modeling stochastic and irregularly sampled time series is a challenging problem found in a wide range of applications, especially in medici… (voir plus)ne. Neural stochastic differential equations (Neural SDEs) are an attractive modeling technique for this problem, which parameterize the drift and diffusion terms of an SDE with neural networks. However, current algorithms for training Neural SDEs require backpropagation through the SDE dynamics, greatly limiting their scalability and stability. To address this, we propose **Trajectory Flow Matching** (TFM), which trains a Neural SDE in a *simulation-free* manner, bypassing backpropagation through the dynamics. TFM leverages the flow matching technique from generative modeling to model time series. In this work we first establish necessary conditions for TFM to learn time series data. Next, we present a reparameterization trick which improves training stability. Finally, we adapt TFM to the clinical time series setting, demonstrating improved performance on three clinical time series datasets both in terms of absolute performance and uncertainty prediction.
2024-09-25
NeurIPS.cc/2024/Conference (spotlight)
openreview.net
openreview.net
VisMin: Visual Minimal-Change Understanding
Rabiul Awal
Saba Ahmadi
Le Zhang
Aishwarya Agrawal
Fine-grained understanding of objects, attributes, and relationships between objects is crucial for visual-language models (VLMs). To evalua… (voir plus)te VLMs' fine-grained understanding, existing benchmarks primarily focus on evaluating VLMs' capability to distinguish between two very similar captions given an image. In this paper, our focus is on evaluating VLMs' capability to distinguish between two very similar images given a caption. To this end, we introduce a new, challenging benchmark termed Visual Minimal-Change Understanding (VisMin), which requires models to predict the correct image-caption match given two images and two captions. Importantly, the image pair (as well as the caption pair) contains minimal changes, i.e., between the two images (as well as between the two captions), only one aspect changes at a time from among the following possible types of changes: object, attribute, count, and spatial relation. These four types of minimal changes are specifically designed to test the models' understanding of objects, attributes of objects (such as color, material, shape), counts of objects, and spatial relationships between objects. To curate our benchmark, we built an automatic pipeline using large language models and diffusion models, followed by a rigorous 4-step verification process by human annotators. Empirical experiments reveal that current VLMs exhibit notable deficiencies in understanding spatial relationships and counting abilities. Furthermore, leveraging the automated nature of our data creation process, we generate a large-scale training dataset, which we use to finetune CLIP (a foundational VLM) and Idefics2 (a multimodal large language model). Our findings show that both these models benefit significantly from fine-tuning on this data, as evident by marked improvements in fine-grained understanding across a wide range of benchmarks. Additionally, such fine-tuning improves CLIP's general image-text alignment capabilities too. All resources including the benchmark, the training data, and the finetuned model checkpoints will be released.
2024-09-25
NeurIPS.cc/2024/Conference (poster)
doi.org
openreview.net