bluuebunny/crossref_metadata_2025 huggingface dataset:
Improved Algorithms for Conservative Exploration in Bandits
Authors: Garcelon Evrard , Ghavamzadeh Mohammad , Lazaric Alessandro , Pirotta Matteo
Published: June 2020

In many fields such as digital marketing, healthcare, finance, and robotics, it is common to have a well-tested and reliable baseline policy running in production (e.g., a recommender system). Nonetheless, the baseline policy is often suboptimal. In this case, it is desirable to deploy online learning algorithms (e.g., a multi-armed bandit algorithm) that interact with the system to learn a better/optimal policy under the constraint that during the learning process the performance is almost never worse than the performance of the baseline itself. In this paper, we study the conservative learning problem in the contextual linear bandit setting and introduce a novel algorithm, the Conservative Constrained LinUCB (CLUCB2). We derive regret bounds for CLUCB2 that match existing results and empirically show that it outperforms state-of-the-art conservative bandit algorithms in a number of synthetic and real-world problems. Finally, we consider a more realistic constraint where the performance is verified only at predefined checkpoints (instead of at every step) and show how this relaxed constraint favorably impacts the regret and empirical performance of CLUCB2.
Modeling Dialogues with Hashcode Representations: A Nonparametric Approach
Authors: Garg Sahil , Rish Irina , Cecchi Guillermo , Goyal Palash , Ghazarian Sarik , Gao Shuyang , Ver Steeg Greg , Galstyan Aram
Published: June 2020

We propose a novel dialogue modeling framework, the first-ever nonparametric kernel functions based approach for dialogue modeling, which learns hashcodes as text representations; unlike traditional deep learning models, it handles well relatively small datasets, while also scaling to large ones. We also derive a novel lower bound on mutual information, used as a model-selection criterion favoring representations with better alignment between the utterances of participants in a collaborative dialogue setting, as well as higher predictability of the generated responses. As demonstrated on three real-life datasets, including prominently psychotherapy sessions, the proposed approach significantly outperforms several state-of-art neural network based dialogue systems, both in terms of computational efficiency, reducing training time from days or weeks to hours, and the response quality, achieving an order of magnitude improvement over competitors in frequency of being chosen as the best model by human evaluators.
Robust Gradient-Based Markov Subsampling
Authors: Gong Tieliang , Xi Quanhan , Xu Chen
Published: June 2020

Subsampling is a widely used and effective method to deal with the challenges brought by big data. Most subsampling procedures are designed based on the importance sampling framework, where samples with high importance measures are given corresponding sampling probabilities. However, in the highly noisy case, these samples may cause an unstable estimator which could lead to a misleading result. To tackle this issue, we propose a gradient-based Markov subsampling (GMS) algorithm to achieve robust estimation. The core idea is to construct a subset which allows us to conservatively correct a crude initial estimate towards the true signal. Specifically, GMS selects samples with small gradients via a probabilistic procedure, constructing a subset that is likely to exclude noisy samples and provide a safe improvement over the initial estimate. We show that the GMS estimator is statistically consistent at a rate which matches the optimal in the minimax sense. The promising performance of GMS is supported by simulation studies and real data examples.
EPOC: Efficient Perception via Optimal Communication
Authors: Heidari Kapourchali Masoumeh , Banerjee Bonny
Published: June 2020

We propose an agent model capable of actively and selectively communicating with other agents to predict its environmental state efficiently. Selecting whom to communicate with is a challenge when the internal model of other agents is unobservable. Our agent learns a communication policy as a mapping from its belief state to with whom to communicate in an online and unsupervised manner, without any reinforcement. Human activity recognition from multimodal, multisource and heterogeneous sensor data is used as a testbed to evaluate the proposed model where each sensor is assumed to be monitored by an agent. The recognition accuracy on benchmark datasets is comparable to the state-of-the-art even though our model uses significantly fewer parameters and infers the state in a localized manner. The learned policy reduces number of communications. The agent is tolerant to communication failures and can recognize unreliable agents through their communication messages. To the best of our knowledge, this is the first work on learning communication policies by an agent for predicting its environmental state.
SNEQ: Semi-Supervised Attributed Network Embedding with Attention-Based Quantisation
Authors: He Tao , Gao Lianli , Song Jingkuan , Wang Xin , Huang Kejie , Li Yuanfang
Published: June 2020

Learning accurate low-dimensional embeddings for a network is a crucial task as it facilitates many network analytics tasks. Moreover, the trained embeddings often require a significant amount of space to store, making storage and processing a challenge, especially as large-scale networks become more prevalent. In this paper, we present a novel semi-supervised network embedding and compression method, SNEQ, that is competitive with state-of-art embedding methods while being far more space- and time-efficient. SNEQ incorporates a novel quantisation method based on a self-attention layer that is trained in an end-to-end fashion, which is able to dramatically compress the size of the trained embeddings, thus reduces storage footprint and accelerates retrieval speed. Our evaluation on four real-world networks of diverse characteristics shows that SNEQ outperforms a number of state-of-the-art embedding methods in link prediction, node classification and node recommendation. Moreover, the quantised embedding shows a great advantage in terms of storage and time compared with continuous embeddings as well as hashing methods.
Semi-Supervised Learning for Maximizing the Partial AUC
Authors: Iwata Tomoharu , Fujino Akinori , Ueda Naonori
Published: June 2020

The partial area under a receiver operating characteristic curve (pAUC) is a performance measurement for binary classification problems that summarizes the true positive rate with the specific range of the false positive rate. Obtaining classifiers that achieve high pAUC is important in a wide variety of applications, such as cancer screening and spam filtering. Although many methods have been proposed for maximizing the pAUC, existing methods require many labeled data for training. In this paper, we propose a semi-supervised learning method for maximizing the pAUC, which trains a classifier with a small amount of labeled data and a large amount of unlabeled data. To exploit the unlabeled data, we derive two approximations of the pAUC: the first is calculated from positive and unlabeled data, and the second is calculated from negative and unlabeled data. A classifier is trained by maximizing the weighted sum of the two approximations of the pAUC and the pAUC that is calculated from positive and negative data. With experiments using various datasets, we demonstrate that the proposed method achieves higher test pAUCs than existing methods.
Bounding Regret in Empirical Games
Authors: Jecmen Steven , Sinha Arunesh , Li Zun , Tran-Thanh Long
Published: June 2020

Empirical game-theoretic analysis refers to a set of models and techniques for solving large-scale games. However, there is a lack of a quantitative guarantee about the quality of output approximate Nash equilibria (NE). A natural quantitative guarantee for such an approximate NE is the regret in the game (i.e. the best deviation gain). We formulate this deviation gain computation as a multi-armed bandit problem, with a new optimization goal unlike those studied in prior work. We propose an efficient algorithm Super-Arm UCB (SAUCB) for the problem and a number of variants. We present sample complexity results as well as extensive experiments that show the better performance of SAUCB compared to several baselines.
DefogGAN: Predicting Hidden Information in the StarCraft Fog of War with Generative Adversarial Nets
Authors: Jeong Yonghyun , Choi Hyunjin , Kim Byoungjip , Gwon Youngjune
Published: June 2020

We propose DefogGAN, a generative approach to the problem of inferring state information hidden in the fog of war for real-time strategy (RTS) games. Given a partially observed state, DefogGAN generates defogged images of a game as predictive information. Such information can lead to create a strategic agent for the game. DefogGAN is a conditional GAN variant featuring pyramidal reconstruction loss to optimize on multiple feature resolution scales. We have validated DefogGAN empirically using a large dataset of professional StarCraft replays. Our results indicate that DefogGAN can predict the enemy buildings and combat units as accurately as professional players do and achieves a superior performance among state-of-the-art defoggers.
Algorithmic Improvements for Deep Reinforcement Learning Applied to Interactive Fiction
Authors: Jain Vishal , Fedus William , Larochelle Hugo , Precup Doina , Bellemare Marc G.
Published: June 2020

Text-based games are a natural challenge domain for deep reinforcement learning algorithms. Their state and action spaces are combinatorially large, their reward function is sparse, and they are partially observable: the agent is informed of the consequences of its actions through textual feedback. In this paper we emphasize this latter point and consider the design of a deep reinforcement learning agent that can play from feedback alone. Our design recognizes and takes advantage of the structural characteristics of text-based games. We first propose a contextualisation mechanism, based on accumulated reward, which simplifies the learning problem and mitigates partial observability. We then study different methods that rely on the notion that most actions are ineffectual in any given situation, following Zahavy et al.'s idea of an admissible action. We evaluate these techniques in a series of text-based games of increasing difficulty based on the TextWorld framework, as well as the iconic game Zork. Empirically, we find that these techniques improve the performance of a baseline deep reinforcement learning agent applied to text-based games.
GraLSP: Graph Neural Networks with Local Structural Patterns
Authors: Jin Yilun , Song Guojie , Shi Chuan
Published: June 2020

It is not until recently that graph neural networks (GNNs) are adopted to perform graph representation learning, among which, those based on the aggregation of features within the neighborhood of a node achieved great success. However, despite such achievements, GNNs illustrate defects in identifying some common structural patterns which, unfortunately, play significant roles in various network phenomena. In this paper, we propose GraLSP, a GNN framework which explicitly incorporates local structural patterns into the neighborhood aggregation through random anonymous walks. Specifically, we capture local graph structures via random anonymous walks, powerful and flexible tools that represent structural patterns. The walks are then fed into the feature aggregation, where we design various mechanisms to address the impact of structural features, including adaptive receptive radius, attention and amplification. In addition, we design objectives that capture similarities between structures and are optimized jointly with node proximity objectives. With the adequate leverage of structural patterns, our model is able to outperform competitive counterparts in various prediction tasks in multiple datasets.
Large-Scale Multi-View Subspace Clustering in Linear Time
Authors: Kang Zhao , Zhou Wangtao , Zhao Zhitong , Shao Junming , Han Meng , Xu Zenglin
Published: June 2020

A plethora of multi-view subspace clustering (MVSC) methods have been proposed over the past few years. Researchers manage to boost clustering accuracy from different points of view. However, many state-of-the-art MVSC algorithms, typically have a quadratic or even cubic complexity, are inefficient and inherently difficult to apply at large scales. In the era of big data, the computational issue becomes critical. To fill this gap, we propose a large-scale MVSC (LMVSC) algorithm with linear order complexity. Inspired by the idea of anchor graph, we first learn a smaller graph for each view. Then, a novel approach is designed to integrate those graphs so that we can implement spectral clustering on a smaller graph. Interestingly, it turns out that our model also applies to single-view scenario. Extensive experiments on various large-scale benchmark data sets validate the effectiveness and efficiency of our approach with respect to state-of-the-art clustering methods.
Options of Interest: Temporal Abstraction with Interest Functions
Authors: Khetarpal Khimya , Klissarov Martin , Chevalier-Boisvert Maxime , Bacon Pierre-Luc , Precup Doina
Published: June 2020

Temporal abstraction refers to the ability of an agent to use behaviours of controllers which act for a limited, variable amount of time. The options framework describes such behaviours as consisting of a subset of states in which they can initiate, an internal policy and a stochastic termination condition. However, much of the subsequent work on option discovery has ignored the initiation set, because of difficulty in learning it from data. We provide a generalization of initiation sets suitable for general function approximation, by defining an interest function associated with an option. We derive a gradient-based learning algorithm for interest functions, leading to a new interest-option-critic architecture. We investigate how interest functions can be leveraged to learn interpretable and reusable temporal abstractions. We demonstrate the efficacy of the proposed approach through quantitative and qualitative results, in both discrete and continuous environments.
Correcting Predictions for Approximate Bayesian Inference
Authors: Kuśmierczyk Tomasz , Sakaya Joseph , Klami Arto
Published: June 2020

Bayesian models quantify uncertainty and facilitate optimal decision-making in downstream applications. For most models, however, practitioners are forced to use approximate inference techniques that lead to sub-optimal decisions due to incorrect posterior predictive distributions. We present a novel approach that corrects for inaccuracies in posterior inference by altering the decision-making process. We train a separate model to make optimal decisions under the approximate posterior, combining interpretable Bayesian modeling with optimization of direct predictive accuracy in a principled fashion. The solution is generally applicable as a plug-in module for predictive decision-making for arbitrary probabilistic programs, irrespective of the posterior inference strategy. We demonstrate the approach empirically in several problems, confirming its potential.
Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents
Authors: Lee Xian Yeow , Ghadai Sambit , Tan Kai Liang , Hegde Chinmay , Sarkar Soumik
Published: June 2020

Robustness of Deep Reinforcement Learning (DRL) algorithms towards adversarial attacks in real world applications such as those deployed in cyber-physical systems (CPS) are of increasing concern. Numerous studies have investigated the mechanisms of attacks on the RL agent's state space. Nonetheless, attacks on the RL agent's action space (corresponding to actuators in engineering systems) are equally perverse, but such attacks are relatively less studied in the ML literature. In this work, we first frame the problem as an optimization problem of minimizing the cumulative reward of an RL agent with decoupled constraints as the budget of attack. We propose the white-box Myopic Action Space (MAS) attack algorithm that distributes the attacks across the action space dimensions. Next, we reformulate the optimization problem above with the same objective function, but with a temporally coupled constraint on the attack budget to take into account the approximated dynamics of the agent. This leads to the white-box Look-ahead Action Space (LAS) attack algorithm that distributes the attacks across the action and temporal dimensions. Our results showed that using the same amount of resources, the LAS attack deteriorates the agent's performance significantly more than the MAS attack. This reveals the possibility that with limited resource, an adversary can utilize the agent's dynamics to malevolently craft attacks that causes the agent to fail. Additionally, we leverage these attack strategies as a possible tool to gain insights on the potential vulnerabilities of DRL agents.
New Efficient Multi-Spike Learning for Fast Processing and Robust Learning
Authors: Li Shenglan , Yu Qiang
Published: June 2020

Spiking neural networks (SNNs) are considered to be more biologically plausible and lower power consuming than traditional artificial neural networks (ANNs). SNNs use discrete spikes as input and output, but how to process and learn these discrete spikes efficiently and accurately still remains a challenging task. Moreover, most existing learning methods are inefficient with complicated neuron dynamics and learning procedures being involved. In this paper, we propose efficient alternatives by firstly introducing a simplified and efficient neuron model. Based on it, we develop two new multi-spike learning rules together with an event-driven scheme being presented to improve the processing efficiency. We show that, with the as-proposed rules, a single neuron can be trained to successfully perform challenging tasks such as multi-category classification and feature extraction. Our learning methods demonstrate a significant robustness against various strong noises. Moreover, experimental results on some real-world classification tasks show that our approaches yield higher efficiency with less requirement on computation resource, highlighting the advantages and potential of spike-based processing and driving more efforts towards neuromorphic computing.
Do Subsampled Newton Methods Work for High-Dimensional Data?
Authors: Li Xiang , Wang Shusen , Zhang Zhihua
Published: June 2020

Subsampled Newton methods approximate Hessian matrices through subsampling techniques to alleviate the per-iteration cost. Previous results require Ω (d) samples to approximate Hessians, where d is the dimension of data points, making it less practical for high-dimensional data. The situation is deteriorated when d is comparably as large as the number of data points n, which requires to take the whole dataset into account, making subsampling not useful. This paper theoretically justifies the effectiveness of subsampled Newton methods on strongly convex empirical risk minimization with high dimensional data. Specifically, we provably require only Θ˜(deffγ) samples for approximating the Hessian matrices, where deffγ is the γ-ridge leverage and can be much smaller than d as long as nγ ≫ 1. Our theories work for three types of Newton methods: subsampled Netwon, distributed Newton, and proximal Newton.
On the Learning Property of Logistic and Softmax Losses for Deep Neural Networks
Authors: Li Xiangrui , Li Xin , Pan Deng , Zhu Dongxiao
Published: June 2020

Deep convolutional neural networks (CNNs) trained with logistic and softmax losses have made significant advancement in visual recognition tasks in computer vision. When training data exhibit class imbalances, the class-wise reweighted version of logistic and softmax losses are often used to boost performance of the unweighted version. In this paper, motivated to explain the reweighting mechanism, we explicate the learning property of those two loss functions by analyzing the necessary condition (e.g., gradient equals to zero) after training CNNs to converge to a local minimum. The analysis immediately provides us explanations for understanding (1) quantitative effects of the class-wise reweighting mechanism: deterministic effectiveness for binary classification using logistic loss yet indeterministic for multi-class classification using softmax loss; (2) disadvantage of logistic loss for single-label multi-class classification via one-vs.-all approach, which is due to the averaging effect on predicted probabilities for the negative class (e.g., non-target classes) in the learning process. With the disadvantage and advantage of logistic loss disentangled, we thereafter propose a novel reweighted logistic loss for multi-class classification. Our simple yet effective formulation improves ordinary logistic loss by focusing on learning hard non-target classes (target vs. non-target class in one-vs.-all) and turned out to be competitive with softmax loss. We evaluate our method on several benchmark datasets to demonstrate its effectiveness.
A Forest from the Trees: Generation through Neighborhoods
Authors: Li Yang , Gao Tianxiang , Oliva Junier
Published: June 2020

In this work, we propose to learn a generative model using both learned features (through a latent space) and memories (through neighbors). Although human learning makes seamless use of both learned perceptual features and instance recall, current generative learning paradigms only make use of one of these two components. Take, for instance, flow models, which learn a latent space that follows a simple distribution. Conversely, kernel density techniques use instances to shift a simple distribution into an aggregate mixture model. Here we propose multiple methods to enhance the latent space of a flow model with neighborhood information. Not only does our proposed framework represent a more human-like approach by leveraging both learned features and memories, but it may also be viewed as a step forward in non-parametric methods. In addition, our proposed framework allows the user to easily control the properties of generated samples by targeting samples based on neighbors. The efficacy of our model is shown empirically with standard image datasets. We observe compelling results and a significant improvement over baselines. Combined further with a contrastive training mechanism, our proposed methods can effectively perform non-parametric novelty detection.
Efficient Automatic CASH via Rising Bandits
Authors: Li Yang , Jiang Jiawei , Gao Jinyang , Shao Yingxia , Zhang Ce , Cui Bin
Published: June 2020

The Combined Algorithm Selection and Hyperparameter optimization (CASH) is one of the most fundamental problems in Automatic Machine Learning (AutoML). The existing Bayesian optimization (BO) based solutions turn the CASH problem into a Hyperparameter Optimization (HPO) problem by combining the hyperparameters of all machine learning (ML) algorithms, and use BO methods to solve it. As a result, these methods suffer from the low-efficiency problem due to the huge hyperparameter space in CASH. To alleviate this issue, we propose the alternating optimization framework, where the HPO problem for each ML algorithm and the algorithm selection problem are optimized alternately. In this framework, the BO methods are used to solve the HPO problem for each ML algorithm separately, incorporating a much smaller hyperparameter space for BO methods. Furthermore, we introduce Rising Bandits, a CASH-oriented Multi-Armed Bandits (MAB) variant, to model the algorithm selection in CASH. This framework can take the advantages of both BO in solving the HPO problem with a relatively small hyperparameter space and the MABs in accelerating the algorithm selection. Moreover, we further develop an efficient online algorithm to solve the Rising Bandits with provably theoretical guarantees. The extensive experiments on 30 OpenML datasets demonstrate the superiority of the proposed approach over the competitive baselines.
Learning Signed Network Embedding via Graph Attention
Authors: Li Yu , Tian Yuan , Zhang Jiawei , Chang Yi
Published: June 2020

Learning the low-dimensional representations of graphs (i.e., network embedding) plays a critical role in network analysis and facilitates many downstream tasks. Recently graph convolutional networks (GCNs) have revolutionized the field of network embedding, and led to state-of-the-art performance in network analysis tasks such as link prediction and node classification. Nevertheless, most of the existing GCN-based network embedding methods are proposed for unsigned networks. However, in the real world, some of the networks are signed, where the links are annotated with different polarities, e.g., positive vs. negative. Since negative links may have different properties from the positive ones and can also significantly affect the quality of network embedding. Thus in this paper, we propose a novel network embedding framework SNEA to learn Signed Network Embedding via graph Attention. In particular, we propose a masked self-attentional layer, which leverages self-attention mechanism to estimate the importance coefficient for pair of nodes connected by different type of links during the embedding aggregation process. Then SNEA utilizes the masked self-attentional layers to aggregate more important information from neighboring nodes to generate the node embeddings based on balance theory. Experimental results demonstrate the effectiveness of the proposed framework through signed link prediction task on several real-world signed network datasets.
An ADMM Based Framework for AutoML Pipeline Configuration
Authors: Liu Sijia , Ram Parikshit , Vijaykeerthy Deepak , Bouneffouf Djallel , Bramble Gregory , Samulowitz Horst , Wang Dakuo , Conn Andrew , Gray Alexander
Published: June 2020

We study the AutoML problem of automatically configuring machine learning pipelines by jointly selecting algorithms and their appropriate hyper-parameters for all steps in supervised learning pipelines. This black-box (gradient-free) optimization with mixed integer & continuous variables is a challenging problem. We propose a novel AutoML scheme by leveraging the alternating direction method of multipliers (ADMM). The proposed framework is able to (i) decompose the optimization problem into easier sub-problems that have a reduced number of variables and circumvent the challenge of mixed variable categories, and (ii) incorporate black-box constraints alongside the black-box optimization objective. We empirically evaluate the flexibility (in utilizing existing AutoML techniques), effectiveness (against open source AutoML toolkits), and unique capability (of executing AutoML with practically motivated black-box constraints) of our proposed scheme on a collection of binary classification data sets from UCI ML & OpenML repositories. We observe that on an average our framework provides significant gains in comparison to other AutoML frameworks (Auto-sklearn & TPOT), highlighting the practical advantages of this framework.
Layerwise Sparse Coding for Pruned Deep Neural Networks with Extreme Compression Ratio
Authors: Liu Xiao , Li Wenbin , Huo Jing , Yao Lili , Gao Yang
Published: June 2020

Deep neural network compression is important and increasingly developed especially in resource-constrained environments, such as autonomous drones and wearable devices. Basically, we can easily and largely reduce the number of weights of a trained deep model by adopting a widely used model compression technique, e.g., pruning. In this way, two kinds of data are usually preserved for this compressed model, i.e., non-zero weights and meta-data, where meta-data is employed to help encode and decode these non-zero weights. Although we can obtain an ideally small number of non-zero weights through pruning, existing sparse matrix coding methods still need a much larger amount of meta-data (may several times larger than non-zero weights), which will be a severe bottleneck of the deploying of very deep models. To tackle this issue, we propose a layerwise sparse coding (LSC) method to maximize the compression ratio by extremely reducing the amount of meta-data. We first divide a sparse matrix into multiple small blocks and remove zero blocks, and then propose a novel signed relative index (SRI) algorithm to encode the remaining non-zero blocks (with much less meta-data). In addition, the proposed LSC performs parallel matrix multiplication without full decoding, while traditional methods cannot. Through extensive experiments, we demonstrate that LSC achieves substantial gains in pruned DNN compression (e.g., 51.03x compression ratio on ADMM-Lenet) and inference computation (i.e., time reduction and extremely less memory bandwidth), over state-of-the-art baselines.
Uncertainty Aware Graph Gaussian Process for Semi-Supervised Learning
Authors: Liu Zhao-Yang , Li Shao-Yuan , Chen Songcan , Hu Yao , Huang Sheng-Jun
Published: June 2020

Graph-based semi-supervised learning (GSSL) studies the problem where in addition to a set of data points with few available labels, there also exists a graph structure that describes the underlying relationship between data items. In practice, structure uncertainty often occurs in graphs when edges exist between data with different labels, which may further results in prediction uncertainty of labels. Considering that Gaussian process generalizes well with few labels and can naturally model uncertainty, in this paper, we propose an Uncertainty aware Graph Gaussian Process based approach (UaGGP) for GSSL. UaGGP exploits the prediction uncertainty and label smooth regularization to guide each other during learning. To further subdue the effect of irrelevant neighbors, UaGGP also aggregates the clean representation in the original space and the learned representation. Experiments on benchmarks demonstrate the effectiveness of the proposed approach.
Towards Fine-Grained Temporal Network Representation via Time-Reinforced Random Walk
Authors: Liu Zhining , Zhou Dawei , Zhu Yada , Gu Jinjie , He Jingrui
Published: June 2020

Encoding a large-scale network into a low-dimensional space is a fundamental step for various network analytic problems, such as node classification, link prediction, community detection, etc. Existing methods focus on learning the network representation from either the static graphs or time-aggregated graphs (e.g., time-evolving graphs). However, many real systems are not static or time-aggregated as the nodes and edges are timestamped and dynamically changing over time. For examples, in anti-money laundering analysis, cycles formed with time-ordered transactions might be red flags in online transaction networks; in novelty detection, a star-shaped structure appearing in a short burst might be an underlying hot topic in social networks. Existing embedding models might not be able to well preserve such fine-grained network dynamics due to the incapability of dealing with continuous-time and the negligence of fine-grained interactions. To bridge this gap, in this paper, we propose a fine-grained temporal network embedding framework named FiGTNE, which aims to learn a comprehensive network representation that preserves the rich and complex network context in the temporal network. In particular, we start from the notion of fine-grained temporal networks, where the temporal network can be represented as a series of timestamped nodes and edges. Then, we propose the time-reinforced random walk (TRRW) with a bi-level context sampling strategy to explore the essential structures and temporal contexts in temporal networks. Extensive experimental results on real graphs demonstrate the efficacy of our FiGTNE framework.
Structured Sparsification of Gated Recurrent Neural Networks
Authors: Lobacheva Ekaterina , Chirkova Nadezhda , Markovich Alexander , Vetrov Dmitry
Published: June 2020

One of the most popular approaches for neural network compression is sparsification — learning sparse weight matrices. In structured sparsification, weights are set to zero by groups corresponding to structure units, e. g. neurons. We further develop the structured sparsification approach for the gated recurrent neural networks, e. g. Long Short-Term Memory (LSTM). Specifically, in addition to the sparsification of individual weights and neurons, we propose sparsifying the preactivations of gates. This makes some gates constant and simplifies an LSTM structure. We test our approach on the text classification and language modeling tasks. Our method improves the neuron-wise compression of the model in most of the tasks. We also observe that the resulting structure of gate sparsity depends on the task and connect the learned structures to the specifics of the particular tasks.
Inefficiency of K-FAC for Large Batch Size Training
Authors: Ma Linjian , Montague Gabe , Ye Jiayu , Yao Zhewei , Gholami Amir , Keutzer Kurt , Mahoney Michael
Published: June 2020

There have been several recent work claiming record times for ImageNet training. This is achieved by using large batch sizes during training to leverage parallel resources to produce faster wall-clock training times per training epoch. However, often these solutions require massive hyper-parameter tuning, which is an important cost that is often ignored. In this work, we perform an extensive analysis of large batch size training for two popular methods that is Stochastic Gradient Descent (SGD) as well as Kronecker-Factored Approximate Curvature (K-FAC) method. We evaluate the performance of these methods in terms of both wall-clock time and aggregate computational cost, and study the hyper-parameter sensitivity by performing more than 512 experiments per batch size for each of these methods. We perform experiments on multiple different models on two datasets of CIFAR-10 and SVHN. The results show that beyond a critical batch size both K-FAC and SGD significantly deviate from ideal strong scaling behaviour, and that despite common belief K-FAC does not exhibit improved large-batch scalability behavior, as compared to SGD.
Temporal Pyramid Recurrent Neural Network
Authors: Ma Qianli , Lin Zhenxi , Chen Enhuan , Cottrell Garrison
Published: June 2020

Learning long-term and multi-scale dependencies in sequential data is a challenging task for recurrent neural networks (RNNs). In this paper, a novel RNN structure called temporal pyramid RNN (TP-RNN) is proposed to achieve these two goals. TP-RNN is a pyramid-like structure and generally has multiple layers. In each layer of the network, there are several sub-pyramids connected by a shortcut path to the output, which can efficiently aggregate historical information from hidden states and provide many gradient feedback short-paths. This avoids back-propagating through many hidden states as in usual RNNs. In particular, in the multi-layer structure of TP-RNN, the input sequence of the higher layer is a large-scale aggregated state sequence produced by the sub-pyramids in the previous layer, instead of the usual sequence of hidden states. In this way, TP-RNN can explicitly learn multi-scale dependencies with multi-scale input sequences of different layers, and shorten the input sequence and gradient feedback paths of each layer. This avoids the vanishing gradient problem in deep RNNs and allows the network to efficiently learn long-term dependencies. We evaluate TP-RNN on several sequence modeling tasks, including the masked addition problem, pixel-by-pixel image classification, signal recognition and speaker identification. Experimental results demonstrate that TP-RNN consistently outperforms existing RNNs for learning long-term and multi-scale dependencies in sequential data.
Adversarial Dynamic Shapelet Networks
Authors: Ma Qianli , Zhuang Wanqing , Li Sen , Huang Desen , Cottrell Garrison
Published: June 2020

Shapelets are discriminative subsequences for time series classification. Recently, learning time-series shapelets (LTS) was proposed to learn shapelets by gradient descent directly. Although learning-based shapelet methods achieve better results than previous methods, they still have two shortcomings. First, the learned shapelets are fixed after training and cannot adapt to time series with deformations at the testing phase. Second, the shapelets learned by back-propagation may not be similar to any real subsequences, which is contrary to the original intention of shapelets and reduces model interpretability. In this paper, we propose a novel shapelet learning model called Adversarial Dynamic Shapelet Networks (ADSNs). An adversarial training strategy is employed to prevent the generated shapelets from diverging from the actual subsequences of a time series. During inference, a shapelet generator produces sample-specific shapelets, and a dynamic shapelet transformation uses the generated shapelets to extract discriminative features. Thus, ADSN can dynamically generate shapelets that are similar to the real subsequences rather than having arbitrary shapes. The proposed model has high modeling flexibility while retaining the interpretability of shapelet-based methods. Experiments conducted on extensive time series data sets show that ADSN is state-of-the-art compared to existing shapelet-based methods. The visualization analysis also shows the effectiveness of dynamic shapelet generation and adversarial training.
Reinforcement Learning from Imperfect Demonstrations under Soft Expert Guidance
Authors: Jing Mingxuan , Ma Xiaojian , Huang Wenbing , Sun Fuchun , Yang Chao , Fang Bin , Liu Huaping
Published: June 2020

In this paper, we study Reinforcement Learning from Demonstrations (RLfD) that improves the exploration efficiency of Reinforcement Learning (RL) by providing expert demonstrations. Most of existing RLfD methods require demonstrations to be perfect and sufficient, which yet is unrealistic to meet in practice. To work on imperfect demonstrations, we first define an imperfect expert setting for RLfD in a formal way, and then point out that previous methods suffer from two issues in terms of optimality and convergence, respectively. Upon the theoretical findings we have derived, we tackle these two issues by regarding the expert guidance as a soft constraint on regulating the policy exploration of the agent, which eventually leads to a constrained optimization problem. We further demonstrate that such problem is able to be addressed efficiently by performing a local linear search on its dual form. Considerable empirical evaluations on a comprehensive collection of benchmarks indicate our method attains consistent improvement over other RLfD counterparts.
Regularized Wasserstein Means for Aligning Distributional Data
Authors: Mi Liang , Zhang Wen , Wang Yalin
Published: June 2020

We propose to align distributional data from the perspective of Wasserstein means. We raise the problem of regularizing Wasserstein means and propose several terms tailored to tackle different problems. Our formulation is based on the variational transportation to distribute a sparse discrete measure into the target domain. The resulting sparse representation well captures the desired property of the domain while reducing the mapping cost. We demonstrate the scalability and robustness of our method with examples in domain adaptation, point set registration, and skeleton layout.
An Intrinsically-Motivated Approach for Learning Highly Exploring and Fast Mixing Policies
Authors: Mutti Mirco , Restelli Marcello
Published: June 2020

What is a good exploration strategy for an agent that interacts with an environment in the absence of external rewards? Ideally, we would like to get a policy driving towards a uniform state-action visitation (highly exploring) in a minimum number of steps (fast mixing), in order to ease efficient learning of any goal-conditioned policy later on. Unfortunately, it is remarkably arduous to directly learn an optimal policy of this nature. In this paper, we propose a novel surrogate objective for learning highly exploring and fast mixing policies, which focuses on maximizing a lower bound to the entropy of the steady-state distribution induced by the policy. In particular, we introduce three novel lower bounds, that lead to as many optimization problems, that tradeoff the theoretical guarantees with computational complexity. Then, we present a model-based reinforcement learning algorithm, IDE3AL, to learn an optimal policy according to the introduced objective. Finally, we provide an empirical evaluation of this algorithm on a set of hard-exploration tasks.
Adversarial Localized Energy Network for Structured Prediction
Authors: Pan Pingbo , Liu Ping , Yan Yan , Yang Tianbao , Yang Yi
Published: June 2020

This paper focuses on energy model based structured output prediction. Though inheriting the benefits from energy-based models to handle the sophisticated cases, previous deep energy-based methods suffered from the substantial computation cost introduced by the enormous amounts of gradient steps in the inference process. To boost the efficiency and accuracy of the energy-based models on structured output prediction, we propose a novel method analogous to the adversarial learning framework. Specifically, in our proposed framework, the generator consists of an inference network while the discriminator is comprised of an energy network. The two sub-modules, i.e., the inference network and the energy network, can benefit each other mutually during the whole computation process. On the one hand, our modified inference network can boost the efficiency by predicting good initializations and reducing the searching space for the inference process; On the other hand, inheriting the benefits of the energy network, the energy module in our network can evaluate the quality of the generated output from the inference network and correspondingly provides a resourceful guide to the training of the inference network. In the ideal case, the adversarial learning strategy makes sure the two sub-modules can achieve an equilibrium state after steps. We conduct extensive experiments to verify the effectiveness and efficiency of our proposed method.
Scaling All-Goals Updates in Reinforcement Learning Using Convolutional Neural Networks
Authors: Pardo Fabio , Levdik Vitaly , Kormushev Petar
Published: June 2020

Being able to reach any desired location in the environment can be a valuable asset for an agent. Learning a policy to navigate between all pairs of states individually is often not feasible. An all-goals updating algorithm uses each transition to learn Q-values towards all goals simultaneously and off-policy. However the expensive numerous updates in parallel limited the approach to small tabular cases so far. To tackle this problem we propose to use convolutional network architectures to generate Q-values and updates for a large number of goals at once. We demonstrate the accuracy and generalization qualities of the proposed method on randomly generated mazes and Sokoban puzzles. In the case of on-screen goal coordinates the resulting mapping from frames to distance-maps directly informs the agent about which places are reachable and in how many steps. As an example of application we show that replacing the random actions in ε-greedy exploration by several actions towards feasible goals generates better exploratory trajectories on Montezuma's Revenge and Super Mario All-Stars games.
A Bayesian Approach for Estimating Causal Effects from Observational Data
Authors: Pensar Johan , Talvitie Topi , Hyttinen Antti , Koivisto Mikko
Published: June 2020

We present a novel Bayesian method for the challenging task of estimating causal effects from passively observed data when the underlying causal DAG structure is unknown. To rigorously capture the inherent uncertainty associated with the estimate, our method builds a Bayesian posterior distribution of the linear causal effect, by integrating Bayesian linear regression and averaging over DAGs. For computing the exact posterior for all cause-effect variable pairs, we give an algorithm that runs in time O(3d d) for d variables, being feasible up to 20 variables. We also give a variant that computes the posterior probabilities of all pairwise ancestor relations within the same time complexity, significantly improving the fastest previous algorithm. In simulations, our Bayesian method outperforms previous methods in estimation accuracy, especially for small sample sizes. We further show that our method for effect estimation is well-adapted for detecting strong causal effects markedly deviating from zero, while our variant for computing posteriors of ancestor relations is the method of choice for detecting the mere existence of a causal relation. Finally, we apply our method on observational flow cytometry data, detecting several causal relations that concur with previous findings from experimental data.
Generalized Hidden Parameter MDPs:Transferable Model-Based RL in a Handful of Trials
Authors: Perez Christian , Petroski Such Felipe , Karaletsos Theofanis
Published: June 2020

There is broad interest in creating RL agents that can solve many (related) tasks and adapt to new tasks and environments after initial training. Model-based RL leverages learned surrogate models that describe dynamics and rewards of individual tasks, such that planning in a good surrogate can lead to good control of the true system. Rather than solving each task individually from scratch, hierarchical models can exploit the fact that tasks are often related by (unobserved) causal factors of variation in order to achieve efficient generalization, as in learning how the mass of an item affects the force required to lift it can generalize to previously unobserved masses. We propose Generalized Hidden Parameter MDPs (GHP-MDPs) that describe a family of MDPs where both dynamics and reward can change as a function of hidden parameters that vary across tasks. The GHP-MDP augments model-based RL with latent variables that capture these hidden parameters, facilitating transfer across tasks. We also explore a variant of the model that incorporates explicit latent structure mirroring the causal factors of variation across tasks (for instance: agent properties, environmental factors, and goals). We experimentally demonstrate state-of-the-art performance and sample-efficiency on a new challenging MuJoCo task using reward and dynamics latent spaces, while beating a previous state-of-the-art baseline with > 10× less data. Using test-time inference of the latent variables, our approach generalizes in a single episode to novel combinations of dynamics and reward, and to novel rewards.