Publications

Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization for Heterogeneous Representational Coarseness
Dianbo Liu
Alex Lamb
Xu Ji
Pascal Junior Tikeng Notsawo
Michael Mozer
Yoshua Bengio
Kenji Kawaguchi
Vector Quantization (VQ) is a method for discretizing latent representations and has become a major part of the deep learning toolkit. It ha… (see more)s been theoretically and empirically shown that discretization of representations leads to improved generalization, including in reinforcement learning where discretization can be used to bottleneck multi-agent communication to promote agent specialization and robustness. The discretization tightness of most VQ-based methods is defined by the number of discrete codes in the representation vector and the codebook size, which are fixed as hyperparameters. In this work, we propose learning to dynamically select discretization tightness conditioned on inputs, based on the hypothesis that data naturally contains variations in complexity that call for different levels of representational coarseness which is observed in many heterogeneous data sets. We show that dynamically varying tightness in communication bottlenecks can improve model performance on visual reasoning and reinforcement learning tasks with heterogeneity in representations.
2023-06-25
AAAI Conference on Artificial Intelligence (published)
doi.org
arxiv.org
Flaky Performances When Pretraining on Relational Databases (Student Abstract)
Shengchao Liu
David Vázquez
Jian Tang
Pierre-Andre Noel
We explore the downstream task performances for graph neural network (GNN) self-supervised learning (SSL) methods trained on subgraphs extra… (see more)cted from relational databases (RDBs). Intuitively, this joint use of SSL and GNNs should allow to leverage more of the available data, which could translate to better results. However, we found that naively porting contrastive SSL techniques can cause ``negative transfer'': linear evaluation on fixed representation from a pretrained model performs worse than on representations from the randomly-initialized model. Based on the conjecture that contrastive SSL conflicts with the message passing layers of the GNN, we propose InfoNode: a contrastive loss aiming to maximize the mutual information between a node's initial- and final-layer representation. The primary empirical results support our conjecture and the effectiveness of InfoNode.
2023-06-25
AAAI Conference on Artificial Intelligence (published)
doi.org
Hypernetworks for Zero-shot Transfer in Reinforcement Learning
Sahand Rezaei-Shoshtari
Charlotte Morissette
Francois R. Hogan
Gregory Dudek
David Meger
In this paper, hypernetworks are trained to generate behaviors across a range of unseen task conditions, via a novel TD-based training objec… (see more)tive and data from a set of near-optimal RL solutions for training tasks. This work relates to meta RL, contextual RL, and transfer learning, with a particular focus on zero-shot performance at test time, enabled by knowledge of the task parameters (also known as context). Our technical approach is based upon viewing each RL algorithm as a mapping from the MDP specifics to the near-optimal value function and policy and seek to approximate it with a hypernetwork that can generate near-optimal value functions and policies, given the parameters of the MDP. We show that, under certain conditions, this mapping can be considered as a supervised learning problem. We empirically evaluate the effectiveness of our method for zero-shot transfer to new reward and transition dynamics on a series of continuous control tasks from DeepMind Control Suite. Our method demonstrates significant improvements over baselines from multitask and meta RL approaches.
2023-06-25
Proceedings of the AAAI Conference on Artificial Intelligence (published)
doi.org
arxiv.org
Latent Space Evolution under Incremental Learning with Concept Drift (Student Abstract)
Charles Bourbeau
Audrey Durand
This work investigates the evolution of latent space when deep learning models are trained incrementally in non-stationary environments that… (see more) stem from concept drift. We propose a methodology for visualizing the incurred change in latent representations. We further show that classes not targeted by concept drift can be negatively affected, suggesting that the observation of all classes during learning may regularize the latent space.
2023-06-25
Proceedings of the AAAI Conference on Artificial Intelligence (published)
doi.org
Signed Laplacian Graph Neural Networks
Yu Li
Meng Qu
Jian Tang
Yi Chang
This paper studies learning meaningful node representations for signed graphs, where both positive and negative links exist. This problem ha… (see more)s been widely studied by meticulously designing expressive signed graph neural networks, as well as capturing the structural information of the signed graph through traditional structure decomposition methods, e.g., spectral graph theory. In this paper, we propose a novel signed graph representation learning framework, called Signed Laplacian Graph Neural Network (SLGNN), which combines the advantages of both. Specifically, based on spectral graph theory and graph signal processing, we first design different low-pass and high-pass graph convolution filters to extract low-frequency and high-frequency information on positive and negative links, respectively, and then combine them into a unified message passing framework. To effectively model signed graphs, we further propose a self-gating mechanism to estimate the impacts of low-frequency and high-frequency information during message passing. We mathematically establish the relationship between the aggregation process in SLGNN and signed Laplacian regularization in signed graphs, and theoretically analyze the expressiveness of SLGNN. Experimental results demonstrate that SLGNN outperforms various competitive baselines and achieves state-of-the-art performance.
2023-06-25
Proceedings of the AAAI Conference on Artificial Intelligence (published)
doi.org
The Effect of Diversity in Meta-Learning
Ramnath Kumar
Tristan Deleu
Yoshua Bengio
Recent studies show that task distribution plays a vital role in the meta-learner's performance. Conventional wisdom is that task diversity … (see more)should improve the performance of meta-learning. In this work, we find evidence to the contrary; (i) our experiments draw into question the efficacy of our learned models: similar manifolds can be learned with a subset of the data (lower task diversity). This finding questions the advantage of providing more data to the model, and (ii) adding diversity to the task distribution (higher task diversity) sometimes hinders the model and does not lead to a significant improvement in performance as previously believed. To strengthen our findings, we provide both empirical and theoretical evidence.
2023-06-25
Proceedings of the AAAI Conference on Artificial Intelligence (published)
doi.org
openreview.net
Partial Ordered Statistics Decoding with Enhanced Error Patterns
Marwan Jalaleddine
Huayi Zhou
Jiajie Li
Warren J. Gross
Guessing Random Additive Noise Decoding (GRAND) excels at decoding high-rate codes but struggles to decode low-rate codes with reasonable co… (see more)mplexity. Ordered Statistics Decoding (OSD) specifically excels in decoding short codes irrespective of rates; however, OSD necessitates the use of Gaussian elimination which introduces additional time, space and computational complexity. Partial Ordered Statistics Decoding (POSD) was proposed to reduce the time, space, and computational complexity of OSD; however, the current partition-based POSD has poor decoding performance since it does not generate test error patterns across partitions. In this paper, we propose to improve the decoding performance of POSD by incorporating test error patterns inspired by GRAND methods. This work offers a trade-off between performance and complexity compared to existing decoders such as GRAND and OSD. We enhance POSD by optimizing the scheduling of Test Error Patterns (TEPs) and show that our technique can be applied to any code in a standard form. At a target BER 10−4 with eBCH (128,64) the enhanced error patterns achieve more than 0.6 dB gain in performance compared to the POSD with partition-based error patterns. Moreover, at a target frame error rate of 10−5, POSD uses 10× less binary operations compared to GRAND when decoding eBCH (128,64) and RLC(128,64) codes. With BCH (127,29) and RLC(128,32), at a target frame error rate of 10−2, POSD with enhanced error patterns with a maximum number of queries (MQ) of 104 achieves up to a 2 dB gain to its GRAND equivalent which is using 107 maximum number of queries.
2023-06-24
2023 IEEE International Symposium on Information Theory (ISIT) (published)
doi.org
CeBed: A Benchmark for Deep Data-Driven OFDM Channel Estimation
Amal Feriani
Di Wu
Steve Liu
Gregory Dudek
2023-06-22
ArXiv (preprint)
doi.org
arxiv.org
Goal-conditioned GFlowNets for Controllable Multi-Objective Molecular Design
Julien Roy
Pierre-Luc Bacon
Christopher Pal
Emmanuel Bengio
In recent years, in-silico molecular design has received much attention from the machine learning community. When designing a new compound f… (see more)or pharmaceutical applications, there are usually multiple properties of such molecules that need to be optimised: binding energy to the target, synthesizability, toxicity, EC50, and so on. While previous approaches have employed a scalarization scheme to turn the multi-objective problem into a preference-conditioned single objective, it has been established that this kind of reduction may produce solutions that tend to slide towards the extreme points of the objective space when presented with a problem that exhibits a concave Pareto front. In this work we experiment with an alternative formulation of goal-conditioned molecular generation to obtain a more controllable conditional model that can uniformly explore solutions along the entire Pareto front.
2023-06-22
ICML.cc/2023/Workshop/DeployableGenerativeAI (accepted)
doi.org
openreview.net
Using modular connectome-based predictive modeling to reveal brain-behavior relationships of individual differences in working memory
Huayi Yang
Junjun Zhang
Zhenlan Jin
Pouya Bashivan
Ling Li
2023-06-21
Brain Structure and Function (published)
doi.org
Accelerating exploration and representation learning with offline pre-training
Bogdan Mazoure
Jacob Bruce
Doina Precup
Rob Fergus
Ankit Anand
Sequential decision-making agents struggle with long horizon tasks, since solving them requires multi-step reasoning. Most reinforcement lea… (see more)rning (RL) algorithms address this challenge by improved credit assignment, introducing memory capability, altering the agent's intrinsic motivation (i.e. exploration) or its worldview (i.e. knowledge representation). Many of these components could be learned from offline data. In this work, we follow the hypothesis that exploration and representation learning can be improved by separately learning two different models from a single offline dataset. We show that learning a state representation using noise-contrastive estimation and a model of auxiliary reward separately from a single collection of human demonstrations can significantly improve the sample efficiency on the challenging NetHack benchmark. We also ablate various components of our experimental setting and highlight crucial insights.
2023-06-19
ICML.cc/2023/Workshop/ILHF (accepted)
doi.org
openreview.net
Cognitive Models as Simulators: Using Cognitive Models to Tap into Implicit Human Feedback
Ardavan S. Nobandegani
Thomas Shultz
Irina Rish
2023-06-19
ICML.cc/2023/Workshop/ILHF (accepted)
openreview.net
openreview.net