Publications

Only Tails Matter: Average-Case Universality and Robustness in the Convex Regime

Fabian Pedregosa

The recently developed average-case analysis of optimization methods allows a more fine-grained and representative convergence analysis than… (voir plus) usual worst-case results. In exchange, this analysis requires a more precise hypothesis over the data generating process, namely assuming knowledge of the expected spectral distribution (ESD) of the random matrix associated with the problem. This work shows that the concentration of eigenvalues near the edges of the ESD determines a problem's asymptotic average complexity. This a priori information on this concentration is a more grounded assumption than complete knowledge of the ESD. This approximate concentration is effectively a middle ground between the coarseness of the worst-case scenario convergence and the restrictive previous average-case analysis. We also introduce the Generalized Chebyshev method, asymptotically optimal under a hypothesis on this concentration and globally optimal when the ESD follows a Beta distribution. We compare its performance to classical optimization algorithms, such as gradient descent or Nesterov's scheme, and we show that, in the average-case context, Nesterov's method is universally nearly optimal asymptotically.

2022-07-18

International Conference on Machine Learning (Accept for Short Presentation)

doi.org

proceedings.mlr.press

SpeqNets: Sparsity-aware Permutation-equivariant Graph Networks

Christopher Morris

Gaurav Rattan

Sandra Kiefer

Siamak Ravanbkash

While (message-passing) graph neural networks have clear limitations in approximating permutation-equivariant functions over graphs or gener… (voir plus)al relational data, more expressive, higher-order graph neural networks do not scale to large graphs. They either operate on

2022-07-18

International Conference on Machine Learning (Accept for Short Presentation)

doi.org

proceedings.mlr.press

The Primacy Bias in Deep Reinforcement Learning

This work identifies a common flaw of deep reinforcement learning (RL) algorithms: a tendency to rely on early interactions and ignore usefu… (voir plus)l evidence encountered later. Because of training on progressively growing datasets, deep RL agents incur a risk of overfitting to earlier experiences, negatively affecting the rest of the learning process. Inspired by cognitive science, we refer to this effect as the primacy bias. Through a series of experiments, we dissect the algorithmic aspects of deep RL that exacerbate this bias. We then propose a simple yet generally-applicable mechanism that tackles the primacy bias by periodically resetting a part of the agent. We apply this mechanism to algorithms in both discrete (Atari 100k) and continuous action (DeepMind Control Suite) domains, consistently improving their performance.

2022-07-18

ICML (Accept for Short Presentation)

doi.org

proceedings.mlr.press

Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods

Yi Wan

Ali Rahimi-Kalahroudi

Janarthanan Rajendran

Ida Momennejad

Sarath Chandar

Harm van Seijen

In recent years, a growing number of deep model-based reinforcement learning (RL) methods have been introduced. The interest in deep model-b… (voir plus)ased RL is not surprising, given its many potential benefits, such as higher sample efficiency and the potential for fast adaption to changes in the environment. However, we demonstrate, using an improved version of the recently introduced Local Change Adaptation (LoCA) setup, that well-known model-based methods such as PlaNet and DreamerV2 perform poorly in their ability to adapt to local environmental changes. Combined with prior work that made a similar observation about the other popular model-based method, MuZero, a trend appears to emerge, suggesting that current deep model-based methods have serious limitations. We dive deeper into the causes of this poor performance, by identifying elements that hurt adaptive behavior and linking these to underlying techniques frequently used in deep model-based RL. We empirically validate these insights in the case of linear function approximation by demonstrating that a modified version of linear Dyna achieves effective adaptation to local changes. Furthermore, we provide detailed insights into the challenges of building an adaptive nonlinear model-based method, by experimenting with a nonlinear version of Dyna.

2022-07-18

International Conference on Machine Learning (Accept for Short Presentation)

doi.org

proceedings.mlr.press

Towards Scaling Difference Target Propagation by Learning Backprop Targets

The development of biologically-plausible learning algorithms is important for understanding learning in the brain, but most of them fail to… (voir plus) scale-up to real-world tasks, limiting their potential as explanations for learning by real brains. As such, it is important to explore learning algorithms that come with strong theoretical guarantees and can match the performance of backpropagation (BP) on complex tasks. One such algorithm is Difference Target Propagation (DTP), a biologically-plausible learning algorithm whose close relation with Gauss-Newton (GN) optimization has been recently established. However, the conditions under which this connection rigorously holds preclude layer-wise training of the feedback pathway synaptic weights (which is more biologically plausible). Moreover, good alignment between DTP weight updates and loss gradients is only loosely guaranteed and under very specific conditions for the architecture being trained. In this paper, we propose a novel feedback weight training scheme that ensures both that DTP approximates BP and that layer-wise feedback weight training can be restored without sacrificing any theoretical guarantees. Our theory is corroborated by experimental results and we report the best performance ever achieved by DTP on CIFAR-10 and ImageNet 32

2022-07-18

International Conference on Machine Learning (Accept for Short Presentation)

doi.org

proceedings.mlr.press

Utility Theory for Sequential Decision Making

Mehran Shakerinava

Siamak Ravanbakhsh

The von Neumann-Morgenstern (VNM) utility theorem shows that under certain axioms of rationality, decision-making is reduced to maximizing t… (voir plus)he expectation of some utility function. We extend these axioms to increasingly structured sequential decision making settings and identify the structure of the corresponding utility functions. In particular, we show that memoryless preferences lead to a utility in the form of a per transition reward and multiplicative factor on the future return. This result motivates a generalization of Markov Decision Processes (MDPs) with this structure on the agent's returns, which we call Affine-Reward MDPs. A stronger constraint on preferences is needed to recover the commonly used cumulative sum of scalar rewards in MDPs. A yet stronger constraint simplifies the utility function for goal-seeking agents in the form of a difference in some function of states that we call potential functions. Our necessary and sufficient conditions demystify the reward hypothesis that underlies the design of rational agents in reinforcement learning by adding an axiom to the VNM rationality axioms and motivates new directions for AI research involving sequential decision making.

2022-07-18

International Conference on Machine Learning (Accept for Short Presentation)

doi.org

proceedings.mlr.press

Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error

Scott Fujimoto

David Meger

Doina Precup

Ofir Nachum

Shixiang Shane Gu

In this work, we study the use of the Bellman equation as a surrogate objective for value prediction accuracy. While the Bellman equation is… (voir plus) uniquely solved by the true value function over all state-action pairs, we find that the Bellman error (the difference between both sides of the equation) is a poor proxy for the accuracy of the value function. In particular, we show that (1) due to cancellations from both sides of the Bellman equation, the magnitude of the Bellman error is only weakly related to the distance to the true value function, even when considering all state-action pairs, and (2) in the finite data regime, the Bellman equation can be satisfied exactly by infinitely many suboptimal solutions. This means that the Bellman error can be minimized without improving the accuracy of the value function. We demonstrate these phenomena through a series of propositions, illustrative toy examples, and empirical analysis in standard benchmark domains.

2022-07-18

International Conference on Machine Learning (Accept for Short Presentation)

doi.org

proceedings.mlr.press

Near-Optimal Glimpse Sequences for Improved Hard Attention Neural Network Training

William Harvey

Michael Teng

Frank N. Wood

Hard visual attention is a promising approach to reduce the computational burden of modern computer vision methodologies. However, hard atte… (voir plus)ntion mechanisms can be difficult and slow to train, which is especially costly for applications like neural architecture search where multiple networks must be trained. We introduce a method to amortise the cost of training by generating an extra supervision signal for a subset of the training data. This supervision is in the form of sequences of ‘good’ locations to attend to for each image. We find that the best method to generate supervision sequences comes from framing hard attention for image classification as a Bayesian optimal experimental design (BOED) problem. From this perspective, the optimal locations to attend to are those which provide the greatest expected reduction in the entropy of the classification distribution. We introduce methodology from the BOED literature to approximate this optimal behaviour and generate ‘near-optimal’ supervision sequences. We then present a hard attention network training objective that makes use of these sequences and show that it allows faster training than prior work. We finally demonstrate the utility of faster hard attention training by incorporating supervision sequences in a neural architecture search, resulting in hard attention architectures which can outperform networks with access to the entire image.

2022-07-17

2022 International Joint Conference on Neural Networks (IJCNN) (publié)

doi.org

arxiv.org

A Novel Neural Network-Based Malware Severity Classification System

Miles Q. Li

Benjamin C. M. Fung

2022-07-17

Communications in Computer and Information Science (publié)

doi.org

On the Effectiveness of Interpretable Feedforward Neural Network

Miles Q. Li

Benjamin C. M. Fung

Adel Abusitta

Deep learning models have achieved state-of-the-art performance in many classification tasks. However, most of them cannot provide an explan… (voir plus)ation for their classification results. Machine learning models that are interpretable are usually linear or piecewise linear and yield inferior performance. Non-linear models achieve much better classification performance, but it is usually hard to explain their classification results. As a counter-example, an interpretable feedforward neural network (IFFNN) is proposed to achieve both high classification performance and interpretability for malware detection. If the IFFNN can perform well in a more flexible and general form for other classification tasks while providing meaningful explanations, it may be of great interest to the applied machine learning community. In this paper, we propose a way to generalize the interpretable feedforward neural network to multi-class classification scenarios and any type of feedforward neural networks, and evaluate its classification performance and interpretability on interpretable datasets. We conclude by finding that the generalized IFFNNs achieve comparable classification performance to their normal feedforward neural network counterparts and provide meaningful explanations. Thus, this kind of neural network architecture has great practical use.

2022-07-17

2022 International Joint Conference on Neural Networks (IJCNN) (publié)

doi.org

arxiv.org

Unified gene expression signature of novel NPM1 exon 5 mutations in acute myeloid leukemia

Véronique Lisi

Ève Blanchard

Michael Vladovsky

Éric Audemard

Albert Ferghaly

Sébastien Lemieux

Josée Hébert