Publications

Deep Learning on a Healthy Data Diet: Finding Important Examples for Fairness

Hamid Palangi

Samira Shabanian

Data-driven predictive solutions predominant in commercial applications tend to suffer from biases and stereotypes, which raises equity conc… (voir plus)erns. Prediction models may discover, use, or amplify spurious correlations based on gender or other protected personal characteristics, thus discriminating against marginalized groups. Mitigating gender bias has become an important research focus in natural language processing (NLP) and is an area where annotated corpora are available. Data augmentation reduces gender bias by adding counterfactual examples to the training dataset. In this work, we show that some of the examples in the augmented dataset can be not important or even harmful for fairness. We hence propose a general method for pruning both the factual and counterfactual examples to maximize the model's fairness as measured by the demographic parity, equality of opportunity, and equality of odds. The fairness achieved by our method surpasses that of data augmentation on three text classification datasets, using no more than half of the examples in the augmented dataset. Our experiments are conducted using models of varying sizes and pre-training settings.

2023-06-25

AAAI Conference on Artificial Intelligence (publié)

doi.org

arxiv.org

Estimating Regression Predictive Distributions with Sample Networks

Ali Harakeh

Jordan Hu

Naiqing Guan

Steven L. Waslander

Liam Paull

Estimating the uncertainty in deep neural network predictions is crucial for many real-world applications. A common approach to model uncert… (voir plus)ainty is to choose a parametric distribution and fit the data to it using maximum likelihood estimation. The chosen parametric form can be a poor fit to the data-generating distribution, resulting in unreliable uncertainty estimates. In this work, we propose SampleNet, a flexible and scalable architecture for modeling uncertainty that avoids specifying a parametric form on the output distribution. SampleNets do so by defining an empirical distribution using samples that are learned with the Energy Score and regularized with the Sinkhorn Divergence. SampleNets are shown to be able to well-fit a wide range of distributions and to outperform baselines on large-scale real-world regression tasks.

2023-06-25

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

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… (voir plus)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 (publié)

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… (voir plus)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

AAAI Conference on Artificial Intelligence (publié)

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… (voir plus) 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 (publié)

doi.org

Neighbor Auto-Grouping Graph Neural Networks for Handover Parameter Configuration in Cellular Network

Mehrtash Mehrabi

Walid Masoudimansour

Yingxue Zhang

Jie Chuai

Zhitang Chen

Mark J. Coates

Jianye HAO

Yanhui. Geng

2023-06-25

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

doi.org

arxiv.org

Self-Supervised Learning for Anomalous Channel Detection in EEG Graphs: Application to Seizure Analysis

Thi Kieu Khanh Ho

Narges Armanfard

Electroencephalogram (EEG) signals are effective tools towards seizure analysis where one of the most important challenges is accurate detec… (voir plus)tion of seizure events and brain regions in which seizure happens or initiates. However, all existing machine learning-based algorithms for seizure analysis require access to the labeled seizure data while acquiring labeled data is very labor intensive, expensive, as well as clinicians dependent given the subjective nature of the visual qualitative interpretation of EEG signals. In this paper, we propose to detect seizure channels and clips in a self-supervised manner where no access to the seizure data is needed. The proposed method considers local structural and contextual information embedded in EEG graphs by employing positive and negative sub-graphs. We train our method through minimizing contrastive and generative losses. The employ of local EEG sub-graphs makes the algorithm an appropriate choice when accessing to the all EEG channels is impossible due to complications such as skull fractures. We conduct an extensive set of experiments on the largest seizure dataset and demonstrate that our proposed framework outperforms the state-of-the-art methods in the EEG-based seizure study. The proposed method is the only study that requires no access to the seizure data in its training phase, yet establishes a new state-of-the-art to the field, and outperforms all related supervised methods.

2023-06-25

AAAI Conference on Artificial Intelligence (publié)

doi.org

arxiv.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… (voir plus)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 (publié)

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 … (voir plus)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 (publié)

doi.org

openreview.net

Towards Safe Mechanical Ventilation Treatment Using Deep Offline Reinforcement Learning

Jacob Shkrob

My Duc Tran

Doina Precup

Sumana Basu

Mechanical ventilation is a key form of life support for patients with pulmonary impairment. Healthcare workers are required to continuously… (voir plus) adjust ventilator settings for each patient, a challenging and time consuming task. Hence, it would be beneficial to develop an automated decision support tool to optimize ventilation treatment. We present DeepVent, a Conservative Q-Learning (CQL) based offline Deep Reinforcement Learning (DRL) agent that learns to predict the optimal ventilator parameters for a patient to promote 90 day survival. We design a clinically relevant intermediate reward that encourages continuous improvement of the patient vitals as well as addresses the challenge of sparse reward in RL. We find that DeepVent recommends ventilation parameters within safe ranges, as outlined in recent clinical trials. The CQL algorithm offers additional safety by mitigating the overestimation of the value estimates of out-of-distribution states/actions. We evaluate our agent using Fitted Q Evaluation (FQE) and demonstrate that it outperforms physicians from the MIMIC-III dataset.

2023-06-25

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

doi.org

arxiv.org

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… (voir plus)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) (publié)

doi.org

CeBed: A Benchmark for Deep Data-Driven OFDM Channel Estimation

Amal Feriani

Di Wu

Steve Liu

Gregory Dudek

2023-06-22

ArXiv (prépublication)

doi.org

arxiv.org