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Distributed parallel stochastic gradient descent algorithms are workhorses for large scale machine learning tasks. Among them, local stochastic gradient descent (Local SGD) has attracted significant attention due to its low communication complexity. Previous studies prove that the communication complexity of Local SGD with a fixed or an adaptive communication period  is in the order of O (N3/2 T1/2) and O (N3/4 T3/4) when the data distributions on clients are identical (IID) or otherwise (Non-IID), where N is the number of clients and T is the number of iterations. In this paper, to accelerate the convergence by reducing the communication complexity, we propose STagewise Local SGD (STL-SGD), which increases the communication period gradually along with decreasing learning rate. We prove that STL-SGD can keep the same convergence rate and linear speedup as mini-batch SGD. In addition, as the benefit of increasing the communication period, when the objective is strongly convex or satisfies the Polyak-Lojasiewicz condition, the communication complexity of STL-SGD is O (N log T ) and O (N1/2 T1/2) for the IID case and the Non-IID case respectively, achieving significant improvements over Local SGD. Experiments on both convex and non-convex problems demonstrate the superior performance of STL-SGD.

AAAI 2021

STL-SGD: Speeding Up Local SGD with Stagewise Communication Period

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

technical paper

Enabling autonomous systems to comply with an ethical theory is critical given their accelerating deployment in domains that impact society. While many ethical theories have been studied extensively in moral philosophy, they are still challenging to implement by developers who build autonomous systems. This paper proposes a novel approach for building ethically compliant autonomous systems that optimize completing a task while following an ethical framework. First, we introduce a definition of an ethically compliant autonomous system and its properties. Next, we offer a range of ethical frameworks for divine command theory, prima facie duties, and virtue ethics. Finally, we demonstrate the accuracy and usability of our approach in a set of autonomous driving simulations and a user study of planning and robotics experts.

Ethically Compliant Sequential Decision Making

The Right to be Forgotten is part of the recently enacted General Data Protection Regulation (GDPR) law that affects any data holder that has data on European Union residents. It gives EU residents the ability to request deletion of their personal data, including training records used to train machine learning models. Unfortunately, Deep Neural Network models are vulnerable to information leaking attacks such as model inversion attacks which extract class information from a trained model and membership inference attacks which determine the presence of an example in a model's training data. If a malicious party can mount an attack and learn private information that was meant to be removed, then it implies that the model owner has not properly protected their user's rights and their models may not be compliant with the GDPR law. In this paper, we present two efficient methods that address this question of how a model owner or data holder may delete personal data from models in such a way that they may not be vulnerable to model inversion and membership inference attacks while maintaining model efficacy. We start by presenting a real-world threat model that shows that simply removing training data is insufficient to protect users. We follow that up with two data removal methods, namely Unlearning and Amnesiac Unlearning, that enable model owners to protect themselves against such attacks while being compliant with regulations. We provide extensive empirical analysis that show that these methods are indeed efficient, safe to apply, effectively remove learned information about sensitive data from trained models while maintaining model efficacy.

Amnesiac Machine Learning

Motivated by the Bike Angels Program in New York's Citi Bike and Boston's Blue Bikes, we study the use of (registered) volunteers to re-position empty bikes for riders in a bike sharing system. We propose a method that can be used to deploy the volunteers in the system, based on the real time distribution of the bikes in different stations. To account for (random) route demand in the network, we solve a related transshipment network design model and construct a sparse structure to restrict the re-balancing activities of the volunteers (concentrating re-balancing activities on essential routes). We also develop a comprehensive simulation model using a threshold-based policy to deploy the volunteers in real time,  to test the effect of choice restriction on volunteers (suitably deployed) to re-position bikes.  We use the Hubway system in Boston (with 60 stations) to demonstrate that using a sparse structure to concentrate the re-balancing activities of the volunteers, instead of allowing all admissible flows in the system (as in current practice),  can reduce the number of re-balancing moves by a huge amount, losing only a small proportion of demand satisfied.

Bike-Repositioning Using Volunteers: Crowd Sourcing with Choice Restriction

We study the problem of object detection when training and test objects are disjoint, i.e. no training examples of the target classes are available. Existing unseen object detection approaches usually combine generic detection frameworks with a single-path unseen classifier, by aligning object regions with semantic class embeddings. In this paper, inspired from human cognitive experience, we propose a simple but effective dual-path detection model that further explores associative semantics to supplement the basic visual-semantic knowledge transfer. We use a novel target-centric multiple-association strategy to establish concept associations, to ensure that the predictor generalized to unseen domain can be learned during training. In this way, through a reasonable inference fusion mechanism, those two parallel reasoning paths can strengthen the correlation between seen and unseen objects, thus improving detection performance. Experiments show that our inductive method can significantly boost the performance by 7.42% over inductive models, and even 5.25% over transductive models on MSCOCO dataset.

Inference Fusion with Associative Semantics for Unseen Object Detection

Point cloud semantic segmentation is a crucial task in 3D scene understanding. Existing methods mainly focus on employing a large number of annotated labels for supervised semantic segmentation. Nonetheless, manually labeling such large point clouds for the supervised segmentation task is time-consuming. In order to reduce the number of annotated labels, we propose a semi-supervised semantic point cloud segmentation network, named SSPC-Net, where we train the semantic segmentation network by inferring the labels of unlabeled points from the few annotated 3D points. In our method, we first partition the whole point cloud into superpoints and build superpoint graphs to mine the long-range dependencies in point clouds. Based on the constructed superpoint graph, we then develop a dynamic label propagation method to generate the pseudo labels for the unsupervised superpoints. Particularly, we adopt a superpoint dropout strategy to dynamically select the generated pseudo labels. In order to fully exploit the generated pseudo labels of the unsupervised superpoints, we furthermore propose a coupled attention mechanism for superpoint feature embedding. Finally, we employ the cross-entropy loss to train the semantic segmentation network with the labels of the supervised superpoints and the pseudo labels of the unsupervised superpoints. Experiments on various datasets demonstrate that our semisupervised segmentation method can achieve better performance than the current semi-supervised segmentation method with fewer annotated 3D points.

SSPC-Net: Semi-Supervised Semantic 3D Point Cloud Segmentation Network

Learning complicated value functions in high dimensional state space by function approximation is a challenging task, partially due to that the max-operator used in temporal difference updates can theoretically cause instability for most linear or non-linear approximation schemes. Mellowmax is a recently proposed differentiable and non-expansion softmax operator that allows a convergent behavior in learning and planning. Unfortunately, the performance bound for the fixed point it converges to remains unclear, and in practice, its parameter is sensitive to various domains and has to be tuned case by case. Finally, the Mellowmax operator may suffer from oversmoothing as it ignores the probability being taken for each action when aggregating them. In this paper we address all the above issues with an enhanced Mellowmax operator, named SM2 (Soft Mellowmax). Particularly, the proposed operator is reliable, easy to implement, and has provable performance guarantee,  while preserving all the advantages of Mellowmax. Furthermore, we show that our SM2 operator can be applied to the challenging multi-agent reinforcement learning scenarios, leading to stable value function approximation and state of the art performance.

Stabilizing Q Learning via Soft Mellowmax Operator

Traffic state estimation (TSE) reconstructs the traffic variables (e.g., density or average velocity) on road segments using partially observed data, which is important for traffic managements. Traditional TSE approaches mainly bifurcate into two categories: model-driven and data-driven, and each of them has shortcomings. To mitigate these limitations, hybrid TSE methods, which combine both model-driven and data-driven, are becoming a promising solution. This paper introduces a hybrid framework, physics-informed deep learning (PIDL), to combine second-order traffic flow models and neural networks to solve the TSE problem. PIDL can encode traffic flow models into deep neural networks to regularize the learning process to achieve improved data efficiency and estimation accuracy. We focus on highway TSE with observed data from loop detectors and probe vehicles, using both density and average velocity as the traffic variables. With numerical examples, we show the use of PIDL to solve a popular second-order traffic flow model, i.e., a Greenshields-based Aw-Rascle-Zhang (ARZ) model, and discover the model parameters. We then evaluate the PIDL-based TSE method using the Next Generation SIMulation (NGSIM) dataset. Experimental results demonstrate the proposed PIDL-based approach to outperform advanced baseline methods in terms of data efficiency and estimation accuracy.

Physics-Informed Deep Learning for Traffic State Estimation: A Hybrid Paradigm Informed by Second-Order Traffic Models

Multi-exit architectures, in which a sequence of intermediate classifiers are introduced at different depths of the feature layers, perform adaptive computation by early exiting ``easy" samples to speed up the inference. In this paper, a novel Harmonized Dense Knowledge Distillation (HDKD) training method for multi-exit architecture is designed to encourage each exit to flexibly learn from all its later exits. In particular, a general dense knowledge distillation training objective is proposed to incorporate all possible beneficial supervision information for multi-exit learning, where a harmonized weighting scheme is designed for the multi-objective optimization problem consisting of multi-exit classification loss and dense distillation loss. A bilevel optimization algorithm is introduced for alternatively updating the weights of multiple objectives and the multi-exit network parameters. Specifically, the loss weighting parameters are optimized with respect to its performance on validation set by gradient descent. Experiments on CIFAR100 and ImageNet show that the HDKD strategy harmoniously improves the performance of the state-of-the-art multi-exit neural networks. Moreover, this method does not require within architecture modifications and can be effectively combined with other previously-proposed training techniques and further boosts the performance.

Harmonized Dense Knowledge Distillation Training for Multi-Exit Architectures

Image captioning is a challenging computer vision task, which aims to generate a natural language description of an image. Most recent researches follow the encoder-decoder framework which depends heavily on the previous generated words for the current prediction. Such methods can not effectively take advantage of the future predicted information to learn complete semantics. In this paper, we propose  Context-Aware Auxiliary Guidance (CAAG) mechanism that can guide the captioning model to perceive global contexts. Upon the captioning model, CAAG performs semantic attention that selectively concentrates on useful information of the global predictions to reproduce the current generation. To validate the adaptability of the method, we apply CAAG to three popular captioners and our proposal achieves competitive performance on the challenging Microsoft COCO image captioning benchmark, e.g. 132.2 CIDEr-D score on Karpathy split and 130.7 CIDEr-D (c40) score on official online evaluation server.

Image Captioning with Context-Aware Auxiliary Guidance

Point-of-interest (POI) recommendation has become an important research topic in these days. The user check-in history used as the input to POI recommendation is very imbalanced and noisy because of sparse and missing check-ins. Although sample reweighting is commonly adopted for addressing this challenge with the input data, its fixed weighting scheme is often inappropriate to deal with different characteristics of users or POIs. Thus, in this paper, we propose PREMERE, an adaptive weighting scheme based on meta-learning. Because meta-data is typically required by meta-learning but is inherently hard to obtain in POI recommendation, we self-generate the meta-data via self-ensembling. Furthermore, the meta-model architecture is extended to deal with the scarcity of check-ins. Thorough experiments show that replacing a weighting scheme with PREMERE boosts the performance of the state-of-the-art recommender algorithms by 2.36â€“26.9% on three benchmark datasets.

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