Publications

High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise.
Eduard Gorbunov
Abdurakhmon Sadiev
Marina Danilova
Samuel Horváth
Gauthier Gidel
Pavel Dvurechensky
Alexander Gasnikov
Peter Richtárik
2024-07-07
Proceedings of the 41st International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
Improving fine-grained understanding in image-text pre-training
Ioana Bica
Anastasija Ili'c
Matthias Bauer
Goker Erdogan
Matko Bovsnjak
Christos Kaplanis
Alexey A. Gritsenko
Matthias Minderer
Charles Blundell
Razvan Pascanu
Jovana Mitrovic
We introduce SPARse Fine-grained Contrastive Alignment (SPARC), a simple method for pretraining more fine-grained multimodal representations… (voir plus) from image-text pairs. Given that multiple image patches often correspond to single words, we propose to learn a grouping of image patches for every token in the caption. To achieve this, we use a sparse similarity metric between image patches and language tokens and compute for each token a language-grouped vision embedding as the weighted average of patches. The token and language-grouped vision embeddings are then contrasted through a fine-grained sequence-wise loss that only depends on individual samples and does not require other batch samples as negatives. This enables more detailed information to be learned in a computationally inexpensive manner. SPARC combines this fine-grained loss with a contrastive loss between global image and text embeddings to learn representations that simultaneously encode global and local information. We thoroughly evaluate our proposed method and show improved performance over competing approaches both on image-level tasks relying on coarse-grained information, e.g. classification, as well as region-level tasks relying on fine-grained information, e.g. retrieval, object detection, and segmentation. Moreover, SPARC improves model faithfulness and captioning in foundational vision-language models.
2024-07-07
Proceedings of the 41st International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
Information Complexity of Stochastic Convex Optimization: Applications to Generalization, Memorization, and Tracing
Idan Attias
Gintare Karolina Dziugaite
MAHDI HAGHIFAM
Roi Livni
Daniel M. Roy
In this work, we investigate the interplay between memorization and learning in the context of stochastic convex optimization (SCO)… (voir plus). We define memorization via the information a learning algorithm reveals about its training data points. We then quantify this information using the framework of conditional mutual information (CMI) proposed by Steinke and Zakynthinou (2020). Our main result is a precise characterization of the tradeoff between the accuracy of a learning algorithm and its CMI, answering an open question posed by Livni (2023). We show that, in the
2024-07-07
Proceedings of the 41st International Conference on Machine Learning (publié)
proceedings.mlr.press
proceedings.mlr.press
Interacting Diffusion Processes for Event Sequence Forecasting
Mai Zeng
Florence Regol
Mark J. Coates
Neural Temporal Point Processes (TPPs) have emerged as the primary framework for predicting sequences of events that occur at irregular time… (voir plus) intervals, but their sequential nature can hamper performance for long-horizon forecasts. To address this, we introduce a novel approach that incorporates a diffusion generative model. The model facilitates sequence-to-sequence prediction, allowing multi-step predictions based on historical event sequences. In contrast to previous approaches, our model directly learns the joint probability distribution of types and inter-arrival times for multiple events. The model is composed of two diffusion processes, one for the time intervals and one for the event types. These processes interact through their respective denoising functions, which can take as input intermediate representations from both processes, allowing the model to learn complex interactions. We demonstrate that our proposal outperforms state-of-the-art baselines for long-horizon forecasting of TPPs.
2024-07-07
Proceedings of the 41st International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
Language-guided Skill Learning with Temporal Variational Inference
Haotian Fu
Pratyusha Sharma
Elias Stengel-Eskin
George Konidaris
Nicolas Le Roux
Marc-Alexandre Côté
Xingdi Yuan
We present an algorithm for skill discovery from expert demonstrations. The algorithm first utilizes Large Language Models (LLMs) to propose… (voir plus) an initial segmentation of the trajectories. Following that, a hierarchical variational inference framework incorporates the LLM-generated segmentation information to discover reusable skills by merging trajectory segments. To further control the trade-off between compression and reusability, we introduce a novel auxiliary objective based on the Minimum Description Length principle that helps guide this skill discovery process. Our results demonstrate that agents equipped with our method are able to discover skills that help accelerate learning and outperform baseline skill learning approaches on new long-horizon tasks in BabyAI, a grid world navigation environment, as well as ALFRED, a household simulation environment.
2024-07-07
Proceedings of the 41st International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
Layerwise Proximal Replay: A Proximal Point Method for Online Continual Learning
Jinsoo Yoo
Yunpeng Liu
Frank N. Wood
Geoff Pleiss
2024-07-07
Proceedings of the 41st International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
Leveraging Transformers for Weakly Supervised Object Localization in Unconstrained Videos
Shakeeb Murtaza
Marco Pedersoli
Aydin Sarraf
Eric Granger
Weakly-Supervised Video Object Localization (WSVOL) involves localizing an object in videos using only video-level labels, also referred to … (voir plus)as tags. State-of-the-art WSVOL methods like Temporal CAM (TCAM) rely on class activation mapping (CAM) and typically require a pre-trained CNN classifier. However, their localization accuracy is affected by their tendency to minimize the mutual information between different instances of a class and exploit temporal information during training for downstream tasks, e.g., detection and tracking. In the absence of bounding box annotation, it is challenging to exploit precise information about objects from temporal cues because the model struggles to locate objects over time. To address these issues, a novel method called transformer based CAM for videos (TrCAM-V), is proposed for WSVOL. It consists of a DeiT backbone with two heads for classification and localization. The classification head is trained using standard classification loss (CL), while the localization head is trained using pseudo-labels that are extracted using a pre-trained CLIP model. From these pseudo-labels, the high and low activation values are considered to be foreground and background regions, respectively. Our TrCAM-V method allows training a localization network by sampling pseudo-pixels on the fly from these regions. Additionally, a conditional random field (CRF) loss is employed to align the object boundaries with the foreground map. During inference, the model can process individual frames for real-time localization applications. Extensive experiments on challenging YouTube-Objects unconstrained video datasets show that our TrCAM-V method achieves new state-of-the-art performance in terms of classification and localization accuracy.
2024-07-07
ArXiv (prépublication)
doi.org
arxiv.org
Memory Efficient Neural Processes via Constant Memory Attention Block
Leo Feng
Frederick Tung
Hossein Hajimirsadeghi
Yoshua Bengio
Mohamed Osama Ahmed
2024-07-07
Proceedings of the 41st International Conference on Machine Learning (publié)
proceedings.mlr.press
proceedings.mlr.press
Meta- (out-of-context) learning in neural networks
Dmitrii Krasheninnikov
Egor Krasheninnikov
Bruno Mlodozeniec
Tegan Maharaj
David M. Krueger
Brown et al. (2020) famously introduced the phenomenon of in-context learning in large language models (LLMs). We establish the existence of… (voir plus) a phenomenon we call meta-out-of-context learning (meta-OCL) via carefully designed synthetic experiments with LLMs. Our results suggest that meta-OCL leads LLMs to more readily"internalize"the semantic content of text that is, or appears to be, broadly useful (such as true statements, or text from authoritative sources) and use it in appropriate circumstances. We further demonstrate meta-OCL in a synthetic computer vision setting, and propose two hypotheses for the emergence of meta-OCL: one relying on the way models store knowledge in their parameters, and another suggesting that the implicit gradient alignment bias of gradient-descent-based optimizers may be responsible. Finally, we reflect on what our results might imply about capabilities of future AI systems, and discuss potential risks. Our code can be found at https://github.com/krasheninnikov/internalization.
2024-07-07
Proceedings of the 41st International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
Nash Learning from Human Feedback
Remi Munos
Michal Valko
Daniele Calandriello
Mohammad Gheshlaghi Azar
Mark Rowland
Zhaohan Daniel Guo
Yunhao Tang
Matthieu Geist
Thomas Mesnard
Côme Fiegel
Andrea Michi
Marco Selvi
Sertan Girgin
Nikola Momchev
Olivier Bachem
Daniel J Mankowitz
Doina Precup
Bilal Piot
Reinforcement learning from human feedback (RLHF) has emerged as the main paradigm for aligning large language models (LLMs) with human pref… (voir plus)erences. Traditionally, RLHF involves the initial step of learning a reward model from pairwise human feedback, i.e., expressed as preferences between pairs of text generations. Subsequently, the LLM’s policy is fine-tuned to maximize the reward through a reinforcement learning algorithm. In this study, we introduce an alternative pipeline for the fine-tuning of LLMs using pairwise human feedback. Our approach entails the initial learning of a pairwise preference model, which is conditioned on two inputs (instead of a single input in the case of a reward model) given a prompt, followed by the pursuit of a policy that consistently generates responses preferred over those generated by any competing policy, thus defining the Nash equilibrium of this preference model. We term this approach Nash learning from human feedback (NLHF). In the context of a tabular policy representation, we present a novel algorithmic solution, Nash-MD, founded on the principles of mirror descent. This algorithm produces a sequence of policies, with the last iteration converging to the regularized Nash equilibrium. Additionally, we explore parametric representations of policies and introduce gradient descent algorithms for deep-learning architectures. We illustrate the effectiveness of our approach by presenting experimental results on a text summarization task. We believe NLHF offers a compelling avenue for fine-tuning LLMs and enhancing the alignment of LLMs with human preferences.
2024-07-07
Proceedings of the 41st International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
Nearest Neighbour Score Estimators for Diffusion Generative Models
Matthew Niedoba
Dylan Green
Saeid Naderiparizi
Vasileios Lioutas
Jonathan Wilder Lavington
Xiaoxuan Liang
Yunpeng Liu
Ke Zhang
Setareh Dabiri
Adam Ścibior
Berend Zwartsenberg
Frank N. Wood
Score function estimation is the cornerstone of both training and sampling from diffusion generative models. Despite this fact, the most com… (voir plus)monly used estimators are either biased neural network approximations or high variance Monte Carlo estimators based on the conditional score. We introduce a novel nearest neighbour score function estimator which utilizes multiple samples from the training set to dramatically decrease estimator variance. We leverage our low variance estimator in two compelling applications. Training consistency models with our estimator, we report a significant increase in both convergence speed and sample quality. In diffusion models, we show that our estimator can replace a learned network for probability-flow ODE integration, opening promising new avenues of future research. Code will be released upon paper acceptance.
2024-07-07
Proceedings of the 41st International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
Position: Application-Driven Innovation in Machine Learning
David Rolnick
Alán Aspuru-Guzik
Sara Beery
Bistra Dilkina
Priya L. Donti
Marzyeh Ghassemi
Hannah Kerner
Claire Monteleoni
Esther Rolf
Milind Tambe
Adam White
2024-07-07
Proceedings of the 41st International Conference on Machine Learning (publié)
proceedings.mlr.press
proceedings.mlr.press