Publications

The Pitfalls and Promise of Conformal Inference Under Adversarial Attacks
Ziquan Liu
Yufei Cui
Yan Yan
Yi Xu
Xiangyang Ji
Xue Liu
Antoni B. Chan
In safety-critical applications such as medical imaging and autonomous driving, where decisions have profound implications for patient healt… (voir plus)h and road safety, it is imperative to maintain both high adversarial robustness to protect against potential adversarial attacks and reliable uncertainty quantification in decision-making. With extensive research focused on enhancing adversarial robustness through various forms of adversarial training (AT), a notable knowledge gap remains concerning the uncertainty inherent in adversarially trained models. To address this gap, this study investigates the uncertainty of deep learning models by examining the performance of conformal prediction (CP) in the context of standard adversarial attacks within the adversarial defense community. It is first unveiled that existing CP methods do not produce informative prediction sets under the commonly used
2024-07-07
Proceedings of the 41st International Conference on Machine Learning (publié)
proceedings.mlr.press
proceedings.mlr.press
Think Before You Act: Decision Transformers with Working Memory
Jikun Kang
Romain Laroche
Xingdi Yuan
Adam Trischler
Xue Liu
Jie Fu
Decision Transformer-based decision-making agents have shown the ability to generalize across multiple tasks. However, their performance rel… (voir plus)ies on massive data and computation. We argue that this inefficiency stems from the forgetting phenomenon, in which a model memorizes its behaviors in parameters throughout training. As a result, training on a new task may deteriorate the model's performance on previous tasks. In contrast to LLMs' implicit memory mechanism, the human brain utilizes distributed memory storage, which helps manage and organize multiple skills efficiently, mitigating the forgetting phenomenon. Inspired by this, we propose a working memory module to store, blend, and retrieve information for different downstream tasks. Evaluation results show that the proposed method improves training efficiency and generalization in Atari games and Meta-World object manipulation tasks. Moreover, we demonstrate that memory fine-tuning further enhances the adaptability of the proposed architecture.
2024-07-07
Proceedings of the 41st International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
Do Transformer World Models Give Better Policy Gradients?
Michel Ma
Tianwei Ni
Clement Gehring
Pierluca D'Oro
Pierre-Luc Bacon
A natural approach for reinforcement learning is to predict future rewards by unrolling a neural network world model, and to backpropagate t… (voir plus)hrough the resulting computational graph to learn a policy. However, this method often becomes impractical for long horizons since typical world models induce hard-to-optimize loss landscapes. Transformers are known to efficiently propagate gradients over long horizons: could they be the solution to this problem? Surprisingly, we show that commonly-used transformer world models produce circuitous gradient paths, which can be detrimental to long-range policy gradients. To tackle this challenge, we propose a class of world models called Actions World Models (AWMs), designed to provide more direct routes for gradient propagation. We integrate such AWMs into a policy gradient framework that underscores the relationship between network architectures and the policy gradient updates they inherently represent. We demonstrate that AWMs can generate optimization landscapes that are easier to navigate even when compared to those from the simulator itself. This property allows transformer AWMs to produce better policies than competitive baselines in realistic long-horizon tasks.
2024-07-07
Proceedings of the 41st International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
Universality of Linear Recurrences Followed by Non-linear Projections: Finite-Width Guarantees and Benefits of Complex Eigenvalues
Antonio Orvieto
Soham De
Caglar Gulçehre
Razvan Pascanu
Samuel L. Smith
Deep neural networks based on linear RNNs interleaved with position-wise MLPs are gaining traction as competitive approaches for sequence mo… (voir plus)deling. Examples of such architectures include state-space models (SSMs) like S4, LRU, and Mamba: recently proposed models that achieve promising performance on text, genetics, and other data that require long-range reasoning. Despite experimental evidence highlighting these architectures’ effectiveness and computational efficiency, their expressive power remains relatively unexplored, especially in connection to specific choices crucial in practice - e.g., carefully designed initialization distribution and potential use of complex numbers. In this paper, we show that combining MLPs with both real or complex linear diagonal recurrences leads to arbitrarily precise approximation of regular causal sequence-to-sequence maps. At the heart of our proof, we rely on a separation of concerns: the linear RNN provides a lossless encoding of the input sequence, and the MLP performs non-linear processing on this encoding. While we show that real diagonal linear recurrences are enough to achieve universality in this architecture, we prove that employing complex eigenvalues near unit disk - i.e., empirically the most successful strategy in S4 - greatly helps the RNN in storing information. We connect this finding with the vanishing gradient issue and provide experiments supporting our claims.
2024-07-07
Proceedings of the 41st International Conference on Machine Learning (publié)
proceedings.mlr.press
proceedings.mlr.press
Unsupervised Concept Discovery Mitigates Spurious Correlations
Md Rifat Arefin
Yan Zhang
Aristide Baratin
Francesco Locatello
Irina Rish
Dianbo Liu
Kenji Kawaguchi
Models prone to spurious correlations in training data often produce brittle predictions and introduce unintended biases. Addressing this ch… (voir plus)allenge typically involves methods relying on prior knowledge and group annotation to remove spurious correlations, which may not be readily available in many applications. In this paper, we establish a novel connection between unsupervised object-centric learning and mitigation of spurious correlations. Instead of directly inferring subgroups with varying correlations with labels, our approach focuses on discovering concepts: discrete ideas that are shared across input samples. Leveraging existing object-centric representation learning, we introduce CoBalT: a concept balancing technique that effectively mitigates spurious correlations without requiring human labeling of subgroups. Evaluation across the benchmark datasets for sub-population shifts demonstrate superior or competitive performance compared state-of-the-art baselines, without the need for group annotation. Code is available at https://github.com/rarefin/CoBalT.
2024-07-07
Proceedings of the 41st International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
When is Transfer Learning Possible?
My Phan
Kianté Brantley
Stephanie Milani
Soroush Mehri
Gokul Swamy
Geoff Gordon
2024-07-07
Proceedings of the 41st International Conference on Machine Learning (publié)
proceedings.mlr.press
proceedings.mlr.press
WorkArena: How Capable are Web Agents at Solving Common Knowledge Work Tasks?
Alexandre Drouin
Maxime Gasse
Massimo Caccia
Issam H. Laradji
Manuel Del Verme
Tom Marty
Léo Boisvert
Megh Thakkar
Quentin Cappart
David Vázquez
Nicolas Chapados
Alexandre Lacoste
We study the use of large language model-based agents for interacting with software via web browsers. Unlike prior work, we focus on measuri… (voir plus)ng the agents' ability to perform tasks that span the typical daily work of knowledge workers utilizing enterprise software systems. To this end, we propose WorkArena, a remote-hosted benchmark of 33 tasks based on the widely-used ServiceNow platform. We also introduce BrowserGym, an environment for the design and evaluation of such agents, offering a rich set of actions as well as multimodal observations. Our empirical evaluation reveals that while current agents show promise on WorkArena, there remains a considerable gap towards achieving full task automation. Notably, our analysis uncovers a significant performance disparity between open and closed-source LLMs, highlighting a critical area for future exploration and development in the field.
2024-07-07
Proceedings of the 41st International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths
Charles Guille-escuret
Hiroki Naganuma
Kilian Fatras
Ioannis Mitliagkas
Understanding the optimization dynamics of neural networks is necessary for closing the gap between theory and practice. Stochastic first-or… (voir plus)der optimization algorithms are known to efficiently locate favorable minima in deep neural networks. This efficiency, however, contrasts with the non-convex and seemingly complex structure of neural loss landscapes. In this study, we delve into the fundamental geometric properties of sampled gradients along optimization paths. We focus on two key quantities, which appear in the restricted secant inequality and error bound. Both hold high significance for first-order optimization. Our analysis reveals that these quantities exhibit predictable, consistent behavior throughout training, despite the stochasticity induced by sampling minibatches. Our findings suggest that not only do optimization trajectories never encounter significant obstacles, but they also maintain stable dynamics during the majority of training. These observed properties are sufficiently expressive to theoretically guarantee linear convergence and prescribe learning rate schedules mirroring empirical practices. We conduct our experiments on image classification, semantic segmentation and language modeling across different batch sizes, network architectures, datasets, optimizers, and initialization seeds. We discuss the impact of each factor. Our work provides novel insights into the properties of neural network loss functions, and opens the door to theoretical frameworks more relevant to prevalent practice.
2024-07-07
Proceedings of the 41st International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
Prospective Messaging: Learning in Networks with Communication Delays
Ryan Fayyazi
Christian Dietrich Weilbach
Frank N. Wood
Inter-neuron communication delays are ubiquitous in physically realized neural networks such as biological neural circuits and neuromorphic … (voir plus)hardware. These delays have significant and often disruptive consequences on network dynamics during training and inference. It is therefore essential that communication delays be accounted for, both in computational models of biological neural networks and in large-scale neuromorphic systems. Nonetheless, communication delays have yet to be comprehensively addressed in either domain. In this paper, we first show that delays prevent state-of-the-art continuous-time neural networks called Latent Equilibrium (LE) networks from learning even simple tasks despite significant overparameterization. We then propose to compensate for communication delays by predicting future signals based on currently available ones. This conceptually straightforward approach, which we call prospective messaging (PM), uses only neuron-local information, and is flexible in terms of memory and computation requirements. We demonstrate that incorporating PM into delayed LE networks prevents reaction lags, and facilitates successful learning on Fourier synthesis and autoregressive video prediction tasks.
2024-07-06
ArXiv (prépublication)
doi.org
arxiv.org
A Novel Bifurcation Method for Observation Perturbation Attacks on Reinforcement Learning Agents: Load Altering Attacks on a Cyber Physical Power System
KIERNAN BRODA-MILIAN
Ranwa Al Mallah
Hanane Dagdougui
Components of cyber physical systems, which affect real-world processes, are often exposed to the internet. Replacing conventional control m… (voir plus)ethods with Deep Reinforcement Learning (DRL) in energy systems is an active area of research, as these systems become increasingly complex with the advent of renewable energy sources and the desire to improve their efficiency. Artificial Neural Networks (ANN) are vulnerable to specific perturbations of their inputs or features, called adversarial examples. These perturbations are difficult to detect when properly regularized, but have significant effects on the ANN's output. Because DRL uses ANN to map optimal actions to observations, they are similarly vulnerable to adversarial examples. This work proposes a novel attack technique for continuous control using Group Difference Logits loss with a bifurcation layer. By combining aspects of targeted and untargeted attacks, the attack significantly increases the impact compared to an untargeted attack, with drastically smaller distortions than an optimally targeted attack. We demonstrate the impacts of powerful gradient-based attacks in a realistic smart energy environment, show how the impacts change with different DRL agents and training procedures, and use statistical and time-series analysis to evaluate attacks' stealth. The results show that adversarial attacks can have significant impacts on DRL controllers, and constraining an attack's perturbations makes it difficult to detect. However, certain DRL architectures are far more robust, and robust training methods can further reduce the impact.
2024-07-05
ArXiv (prépublication)
doi.org
arxiv.org
Masked Multi-Query Slot Attention for Unsupervised Object Discovery
Rishav Pramanik
José-Fabian Villa-Vásquez
Marco Pedersoli
Unsupervised object discovery is becoming an essential line of research for tackling recognition problems that require decomposing an image … (voir plus)into entities, such as semantic segmentation and object detection. Recently, object-centric methods that leverage self-supervision have gained popularity, due to their simplicity and adaptability to different settings and conditions. However, those methods do not exploit effective techniques already employed in modern self-supervised approaches. In this work, we consider an object-centric approach in which DINO ViT features are reconstructed via a set of queried representations called slots. Based on that, we propose a masking scheme on input features that selectively disregards the background regions, inducing our model to focus more on salient objects during the reconstruction phase. Moreover, we extend the slot attention to a multi-query approach, allowing the model to learn multiple sets of slots, producing more stable masks. During training, these multiple sets of slots are learned independently while, at test time, these sets are merged through Hungarian matching to obtain the final slots. Our experimental results and ablations on the PASCAL-VOC 2012 dataset show the importance of each component and highlight how their combination consistently improves object localization. Our source code is available at: github.com/rishavpramanik/maskedmultiqueryslot
2024-07-04
2024 International Joint Conference on Neural Networks (IJCNN) (publié)
doi.org
arxiv.org
Unraveling Radiomics Complexity: Strategies for Optimal Simplicity in Predictive Modeling
Mahdi A. L. Loutfi
Teodora Boblea Podasca
Alex Zwanenburg
Taman Upadhaya
Jorge Barrios
David R Raleigh
William C. Chen
Dante P. I. Capaldi
Hao Zhang
Olivier Gevaert
Jinglan Wu
Alvin C. Silva
Paul J. Zhang
Harrison X. Bai
Jan Seuntjens
Steffen Löck
Patrick O. Richard
Olivier Morin
Caroline Reinhold
Martin Lepage … (voir 1 de plus)
Martin Vallières
Background: The high dimensionality of radiomic feature sets, the variability in radiomic feature types and potentially high computational r… (voir plus)equirements all underscore the need for an effective method to identify the smallest set of predictive features for a given clinical problem. Purpose: Develop a methodology and tools to identify and explain the smallest set of predictive radiomic features. Materials and Methods: 89,714 radiomic features were extracted from five cancer datasets: low-grade glioma, meningioma, non-small cell lung cancer (NSCLC), and two renal cell carcinoma cohorts (n=2104). Features were categorized by computational complexity into morphological, intensity, texture, linear filters, and nonlinear filters. Models were trained and evaluated on each complexity level using the area under the curve (AUC). The most informative features were identified, and their importance was explained. The optimal complexity level and associated most informative features were identified using systematic statistical significance analyses and a false discovery avoidance procedure, respectively. Their predictive importance was explained using a novel tree-based method. Results: MEDimage, a new open-source tool, was developed to facilitate radiomic studies. Morphological features were optimal for MRI-based meningioma (AUC: 0.65) and low-grade glioma (AUC: 0.68). Intensity features were optimal for CECT-based renal cell carcinoma (AUC: 0.82) and CT-based NSCLC (AUC: 0.76). Texture features were optimal for MRI-based renal cell carcinoma (AUC: 0.72). Tuning the Hounsfield unit range improved results for CECT-based renal cell carcinoma (AUC: 0.86). Conclusion: Our proposed methodology and software can estimate the optimal radiomics complexity level for specific medical outcomes, potentially simplifying the use of radiomics in predictive modeling across various contexts.
2024-07-04
ArXiv (prépublication)
doi.org
arxiv.org