bluuebunny/crossref_metadata_2025 huggingface dataset:
A Strategy-Proof Online Auction with Time Discounting Values
Authors: Wu Fan , Liu Junming , Zheng Zhenzhe , Chen Guihai
Published: June 2022

Online mechanism design has been widely applied to various practical applications. However, designing a strategy-proof online mechanism is much more challenging than that in a static scenario due to short of knowledge of future information. In this paper, we investigate online auctions with time discounting values, in contrast to the flat values studied in most of existing work. We present a strategy-proof 2-competitive online auction mechanism despite of time discounting values. We also implement our design and compare it with off-line optimal solution. Our numerical results show that our design achieves good performance in terms of social welfare, revenue, average winning delay, and average valuation loss.
Simultaneous Cake Cutting
Authors: Balkanski Eric , Brânzei Simina , Kurokawa David , Procaccia Ariel
Published: June 2022

We introduce the simultaneous model for cake cutting (the fair allocation of a divisible good), in which agents simultaneously send messages containing a sketch of their preferences over the cake. We show that this model enables the computation of divisions that satisfy proportionality -- a popular fairness notion -- using a protocol that circumvents a standard lower bound via parallel information elicitation. Cake divisions satisfying another prominent fairness notion, envy-freeness, are impossible to compute in the simultaneous model, but admit arbitrarily good approximations.
Betting Strategies, Market Selection, and the Wisdom of Crowds
Authors: Kets Willemien , Pennock David , Sethi Rajiv , Shah Nisarg
Published: June 2022

We investigate the limiting behavior of trader wealth and prices in a simple prediction market with a finite set of participants having heterogeneous beliefs. Traders bet repeatedly on the outcome of a binary event with fixed Bernoulli success probability. A class of strategies, including (fractional) Kelly betting and constant relative risk aversion (CRRA) are considered. We show that when traders are willing to risk only a small fraction of their wealth in any period, belief heterogeneity can persist indefinitely; if bets are large in proportion to wealth then only the most accurate belief type survives. The market price is more accurate in the long run when traders with less accurate beliefs also survive. That is, the survival of traders with heterogeneous beliefs, some less accurate than others, allows the market price to better reflect the objective probability of the event in the long run.
Solving Imperfect Information Games Using Decomposition
Authors: Burch Neil , Johanson Michael , Bowling Michael
Published: June 2022

Decomposition, i.e. independently analyzing possible subgames, has proven to be an essential principle for effective decision-making in perfect information games. However, in imperfect information games, decomposition has proven to be problematic. To date, all proposed techniques for decomposition in imperfect information games have abandoned theoretical guarantees. This work presents the first technique for decomposing an imperfect information game into subgames that can be solved independently, while retaining optimality guarantees on the full-game solution. We can use this technique to construct theoretically justified algorithms that make better use of information available at run-time, overcome memory or disk limitations at run-time, or make a time/space trade-off to overcome memory or disk limitations while solving a game. In particular, we present an algorithm for subgame solving which guarantees performance in the whole game, in contrast to existing methods which may have unbounded error. In addition, we present an offline game solving algorithm, CFR-D, which can produce a Nash equilibrium for a game that is larger than available storage.
Modal Ranking: A Uniquely Robust Voting Rule
Authors: Caragiannis Ioannis , Procaccia Ariel , Shah Nisarg
Published: June 2022

Motivated by applications to crowdsourcing, we study voting rules that output a correct ranking of alternatives by quality from a large collection of noisy input rankings. We seek voting rules that are supremely robust to noise, in the sense of being correct in the face of any "reasonable" type of noise. We show that there is such a voting rule, which we call the modal ranking rule. Moreover, we establish that the modal ranking rule is the unique rule with the preceding robustness property within a large family of voting rules, which includes a slew of well-studied rules.
Envy-Free Division of Sellable Goods
Authors: Karp Jeremy , Kazachkov Aleksandr , Procaccia Ariel
Published: June 2022

We study the envy-free allocation of indivisible goods between two players. Our novel setting includes an option to sell each good for a fraction of the minimum value any player has for the good. To rigorously quantify the efficiency gain from selling, we reason about the price of envy-freeness of allocations of sellable goods — the ratio between the maximum social welfare and the social welfare of the best envy-free allocation. We show that envy-free allocations of sellable goods are significantly more efficient than their unsellable counterparts.
Acquiring Commonsense Knowledge for Sentiment Analysis through Human Computation
Authors: Boia Marina , Musat Claudiu , Faltings Boi
Published: June 2022

Many Artificial Intelligence tasks need large amounts of commonsense knowledge. Because obtaining this knowledge through machine learning would require a huge amount of data, a better alternative is to elicit it from people through human computation. We consider the sentiment classification task, where knowledge about the contexts that impact word polarities is crucial, but hard to acquire from data. We describe a novel task design that allows us to crowdsource this knowledge through Amazon Mechanical Turk with high quality. We show that the commonsense knowledge acquired in this way dramatically improves the performance of established sentiment classification methods.
Sparse Learning for Stochastic Composite Optimization
Authors: Zhang Weizhong , Zhang Lijun , Hu Yao , Jin Rong , Cai Deng , He Xiaofei
Published: June 2022

In this paper, we focus on Stochastic Composite Optimization (SCO) for sparse learning that aims to learn a sparse solution. Although many SCO algorithms have been developed for sparse learning with an optimal convergence rate $O(1/T)$, they often fail to deliver sparse solutions at the end either because of the limited sparsity regularization during stochastic optimization or due to the limitation in online-to-batch conversion. To improve the sparsity of solutions obtained by SCO, we propose a simple but effective stochastic optimization scheme that adds a novel sparse online-to-batch conversion to the traditional SCO algorithms. The theoretical analysis shows that our scheme can find a solution with better sparse patterns without affecting the convergence rate. Experimental results on both synthetic and real-world data sets show that the proposed methods are more effective in recovering the sparse solution and have comparable convergence rate as the state-of-the-art SCO algorithms for sparse learning.
Simpler Bounded Suboptimal Search
Authors: Hatem Matthew , Ruml Wheeler
Published: June 2022

It is commonly appreciated that solving search problems optimally can take too long. Bounded suboptimal search algorithms trade increased solution cost for reduced solving time. Explicit Estimation Search (EES) is a recent state-of-the-art algorithm specifically designed for bounded suboptimal search. Although it tends to expand fewer nodes than alternative algorithms, such as weighted A* (WA*), its per-node expansion overhead is higher, causing it to sometimes take longer. In this paper, we present simplified variants of EES (SEES) and an earlier algorithm, A*epsilon (SA*epsilon), that use different implementations of the same motivating ideas to significantly reduce search overhead and implementation complexity. In an empirical evaluation, we find that SEES, like EES, outperforms classic bounded suboptimal search algorithms, such as WA*, on domains tested where distance-to-go estimates enable better search guidance. We also confirm that, while SEES and SA*epsilon expand roughly the same number of nodes as their progenitors, they solve problems significantly faster and are much easier to implement. This work widens the applicability of state-of the-art bounded suboptimal search by making it easier to deploy.
Parallel Restarted Search
Authors: Cire Andre , Kadioglu Serdar , Sellmann Meinolf
Published: June 2022

We consider the problem of parallelizing restarted backtrack search. With few notable exceptions, most commercial and academic constraint programming solvers do not learn no-goods during search. Depending on the branching heuristics used, this means that there are little to no side-effects between restarts, making them an excellent target for parallelization. We develop a simple technique for parallelizing restarted search deterministically and demonstrate experimentally that we can achieve near-linear speed-ups in practice.
The Computational Complexity of Structure-Based Causality
Authors: Aleksandrowicz Gadi , Chockler Hana , Halpern Joseph , Ivrii Alexander
Published: June 2022

Halpern and Pearl introduced a definition of actual causality; Eiter and Lukasiewicz showed that computing whether X = x is a cause of Y = y is NP-complete in binary models (where all variables can take on only two values) and \Sigma^P_2-complete in general models. In the final version of their paper, Halpern and Pearl slightly modified the definition of actual cause, in order to deal with problems pointed by Hopkins and Pearl. As we show, this modification has a nontrivial impact on the complexity of computing actual cause. To characterize the complexity, a new family D_k^P , k = 1,2,3,..., of complexity classes is introduced, which generalizes the class D^P introduced by Papadimitriou and Yannakakis (DP is just D^P_1). We show that the complexity of computing causality under the updated definition is D^P_2 -complete. Chockler and Halpern extended the definition of causality by introducing notions of responsibility and blame. The complexity of determining the degree of responsibility and blame using the original definition of causality was completely characterized. Again, we show that changing the definition of causality affects the complexity, and completely characterize it using the updated definition.
Capturing Relational Schemas and Functional Dependencies in RDFS
Authors: Calvanese Diego , Fischl Wolfgang , Pichler Reinhard , Sallinger Emanuel , Simkus Mantas
Published: June 2022

Mapping relational data to RDF is an important task for the development of the Semantic Web. To this end, the W3C has recently released a Recommendation for the so-called direct mapping of relational data to RDF. In this work, we propose an enrichment of the direct mapping to make it more faithful by transferring also semantic information present in the relational schema from the relational world to the RDF world. We thus introduce expressive identification constraints to capture functional dependencies and define an RDF Normal Form, which precisely captures the classical Boyce-Codd Normal Form of relational schemas.
Knowledge Graph Embedding by Translating on Hyperplanes
Authors: Wang Zhen , Zhang Jianwen , Feng Jianlin , Chen Zheng
Published: June 2022

We deal with embedding a large scale knowledge graph composed of entities and relations into a continuous vector space. TransE is a promising method proposed recently, which is very efficient while achieving state-of-the-art predictive performance. We discuss some mapping properties of relations which should be considered in embedding, such as reflexive, one-to-many, many-to-one, and many-to-many. We note that TransE does not do well in dealing with these properties. Some complex models are capable of preserving these mapping properties but sacrifice efficiency in the process. To make a good trade-off between model capacity and efficiency, in this paper we propose TransH which models a relation as a hyperplane together with a translation operation on it. In this way, we can well preserve the above mapping properties of relations with almost the same model complexity of TransE. Additionally, as a practical knowledge graph is often far from completed, how to construct negative examples to reduce false negative labels in training is very important. Utilizing the one-to-many/many-to-one mapping property of a relation, we propose a simple trick to reduce the possibility of false negative labeling. We conduct extensive experiments on link prediction, triplet classification and fact extraction on benchmark datasets like WordNet and Freebase. Experiments show TransH delivers significant improvements over TransE on predictive accuracy with comparable capability to scale up.
Exploring the Boundaries of Decidable Verification of Non-Terminating Golog Programs
Authors: Classen Jens , Liebenberg Martin , Lakemeyer Gerhard , Zarriess Benjamin
Published: June 2022

The action programming language GOLOG has been found useful for the control of autonomous agents such as mobile robots. In scenarios like these, tasks are often open-ended so that the respective control programs are non-terminating. Before deploying such programs on a robot, it is often desirable to verify that they meet certain requirements. For this purpose, Claßen and Lakemeyer recently introduced algorithms for the verification of temporal properties of GOLOG programs. However, given the expressiveness of GOLOG, their verification procedures are not guaranteed to terminate. In this paper, we show how decidability can be obtained by suitably restricting the underlying base logic, the effect axioms for primitive actions, and the use of actions within GOLOG programs. Moreover, we show that dropping any of these restrictions immediately leads to undecidability of the verification problem.
Give a Hard Problem to a Diverse Team: Exploring Large Action Spaces
Authors: Soriano Marcolino Leandro , Xu Haifeng , Xin Jiang Albert , Tambe Milind , Bowring Emma
Published: June 2022

Recent work has shown that diverse teams can outperform a uniform team made of copies of the best agent. However, there are fundamental questions that were not asked before. When should we use diverse or uniform teams? How does the performance change as the action space or the teams get larger? Hence, we present a new model of diversity for teams, that is more general than previous models. We prove that the performance of a diverse team improves as the size of the action space gets larger. Concerning the size of the diverse team, we show that the performance converges exponentially fast to the optimal one as we increase the number of agents. We present synthetic experiments that allow us to gain further insights: even though a diverse team outperforms a uniform team when the size of the action space increases, the uniform team will eventually again play better than the diverse team for a large enough action space. We verify our predictions in a system of Go playing agents, where we show a diverse team that improves in performance as the board size increases, and eventually overcomes a uniform team.
Latent Low-Rank Transfer Subspace Learning for Missing Modality Recognition
Authors: Ding Zhengming , Ming Shao , Fu Yun
Published: June 2022

We consider an interesting problem in this paper that uses transfer learning in two directions to compensate missing knowledge from the target domain. Transfer learning tends to be exploited as a powerful tool that mitigates the discrepancy between different databases used for knowledge transfer. It can also be used for knowledge transfer between different modalities within one database. However, in either case, transfer learning will fail if the target data are missing. To overcome this, we consider knowledge transfer between different databases and modalities simultaneously in a single framework, where missing target data from one database are recovered to facilitate recognition task. We referred to this framework as Latent Low-rank Transfer Subspace Learning method (L2TSL). We first propose to use a low-rank constraint as well as dictionary learning in a learned subspace to guide the knowledge transfer between and within different databases. We then introduce a latent factor to uncover the underlying structure of the missing target data. Next, transfer learning in two directions is proposed to integrate auxiliary database for transfer learning with missing target data. Experimental results of multi-modalities knowledge transfer with missing target data demonstrate that our method can successfully inherit knowledge from the auxiliary database to complete the target domain, and therefore enhance the performance when recognizing data from the modality without any training data.
Globally and Locally Consistent Unsupervised Projection
Authors: Wang Hua , Nie Feiping , Huang Heng
Published: June 2022

In this paper, we propose an unsupervised projection method for feature extraction to preserve both global and local consistencies of the input data in the projected space. Traditional unsupervised feature extraction methods, such as principal component analysis (PCA) and locality preserving projections (LPP), can only explore either the global or local geometric structures of the input data, but not the both at the same time. In our new method, we introduce a new measurement using the neighborhood data variances to assess the data locality, by which we propose to learn an optimal projection by rewarding both the global and local structures of the input data. The formulated optimization problem is challenging to solve, because it ends up a trace ratio minimization problem. In this paper, as an important theoretical contribution, we propose a simple yet efficient optimization algorithm to solve the trace ratio problem with theoretically proved convergence. Extensive experiments have been performed on six benchmark data sets, where the promising results validate the proposed method.
Learning Deep Representations for Graph Clustering
Authors: Tian Fei , Gao Bin , Cui Qing , Chen Enhong , Liu Tie-Yan
Published: June 2022

Recently deep learning has been successfully adopted in many applications such as speech recognition and image classification. In this work, we explore the possibility of employing deep learning in graph clustering. We propose a simple method, which first learns a nonlinear embedding of the original graph by stacked autoencoder, and then runs $k$-means algorithm on the embedding to obtain the clustering result. We show that this simple method has solid theoretical foundation, due to the similarity between autoencoder and spectral clustering in terms of what they actually optimize. Then, we demonstrate that the proposed method is more efficient and flexible than spectral clustering. First, the computational complexity of autoencoder is much lower than spectral clustering: the former can be linear to the number of nodes in a sparse graph while the latter is super quadratic due to eigenvalue decomposition. Second, when additional sparsity constraint is imposed, we can simply employ the sparse autoencoder developed in the literature of deep learning; however, it is non-straightforward to implement a sparse spectral method. The experimental results on various graph datasets show that the proposed method significantly outperforms conventional spectral clustering which clearly indicates the effectiveness of deep learning in graph clustering.
Privacy and Regression Model Preserved Learning
Authors: Yi Jinfeng , Wang Jun , Jin Rong
Published: June 2022

Sensitive data such as medical records and business reports usually contains valuable information that can be used to build prediction models. However, designing learning models by directly using sensitive data might result in severe privacy and copyright issues. In this paper, we propose a novel matrix completion based framework that aims to tackle two challenging issues simultaneously: i) handling missing and noisy sensitive data, and ii) preserving the privacy of the sensitive data during the learning process. In particular, the proposed framework is able to mask the sensitive data while ensuring that the transformed data are still usable for training regression models. We show that two key properties, namely model preserving and privacy preserving, are satisfied by the transformed data obtained from the proposed framework. In model preserving, we guarantee that the linear regression model built from the masked data approximates the regression model learned from the original data in a perfect way. In privacy preserving, we ensure that the original sensitive data cannot be recovered since the transformation procedure is irreversible. Given these two characteristics, the transformed data can be safely released to any learners for designing prediction models without revealing any private content. Our empirical studies with a synthesized dataset and multiple sensitive benchmark datasets verify our theoretical claim as well as the effectiveness of the proposed framework.
Adaptive Multi-Compositionality for Recursive Neural Models with Applications to Sentiment Analysis
Authors: Dong Li , Wei Furu , Zhou Ming , Xu Ke
Published: June 2022

Recursive neural models have achieved promising results in many natural language processing tasks. The main difference among these models lies in the composition function, i.e., how to obtain the vector representation for a phrase or sentence using the representations of words it contains. This paper introduces a novel Adaptive Multi-Compositionality (AdaMC) layer to recursive neural models. The basic idea is to use more than one composition functions and adaptively select them depending on the input vectors. We present a general framework to model each semantic composition as a distribution over these composition functions. The composition functions and parameters used for adaptive selection are learned jointly from data. We integrate AdaMC into existing recursive neural models and conduct extensive experiments on the Stanford Sentiment Treebank. The results illustrate that AdaMC significantly outperforms state-of-the-art sentiment classification methods. It helps push the best accuracy of sentence-level negative/positive classification from 85.4% up to 88.5%.
Semi-Supervised Matrix Completion for Cross-Lingual Text Classiﬁcation
Authors: Xiao Min , Guo Yuhong
Published: June 2022

Cross-lingual text classiﬁcation is the task of assigning labels to observed documents in a label-scarce target language domain by using a prediction model trained with labeled documents from a label-rich source language domain. Cross-lingual text classiﬁcation is popularly studied in natural language processing area to reduce the expensive manual annotation effort required in the target language domain. In this work, we propose a novel semi-supervised representation learning approach to address this challenging task by inducing interlingual features via semi-supervised matrix completion. To evaluate the proposed learning technique, we conduct extensive experiments on eighteen cross language sentiment classiﬁcation tasks with four different languages. The empirical results demonstrate the efﬁcacy of the proposed approach, and show it outperforms a number of related cross-lingual learning methods.
Learning Scripts as Hidden Markov Models
Authors: Orr John , Tadepalli Prasad , Doppa Janardhan , Fern Xiaoli , Dietterich Thomas
Published: June 2022

Scripts have been proposed to model the stereotypical event sequences found in narratives. They can be applied to make a variety of inferences including fillinggaps in the narratives and resolving ambiguous references. This paper proposes the first formal frameworkfor scripts based on Hidden Markov Models (HMMs). Our framework supports robust inference and learning algorithms, which are lacking in previous clustering models. We develop an algorithm for structure andparameter learning based on Expectation Maximizationand evaluate it on a number of natural datasets. The results show that our algorithm is superior to several informed baselines for predicting missing events in partialobservation sequences.
Robust Multi-View Spectral Clustering via Low-Rank and Sparse Decomposition
Authors: Xia Rongkai , Pan Yan , Du Lei , Yin Jian
Published: June 2022

Multi-view clustering, which seeks a partition of the data inmultiple views that often provide complementary information to eachother, has received considerable attention in recent years. In reallife clustering problems, the data in each view may haveconsiderable noise. However, existing clustering methods blindlycombine the information from multi-view data with possiblyconsiderable noise, which often degrades their performance. In thispaper, we propose a novel Markov chain method for RobustMulti-view Spectral Clustering (RMSC). Our method has a flavor oflow-rank and sparse decomposition, where we firstly construct atransition probability matrix from each single view, and then usethese matrices to recover a shared low-rank transition probabilitymatrix as a crucial input to the standard Markov chain methodfor clustering. The optimization problem of RMSC has a low-rankconstraint on the transition probability matrix, and simultaneouslya probabilistic simplex constraint on each of its rows. To solvethis challenging optimization problem, we propose an optimization procedurebased on the Augmented Lagrangian Multiplier scheme. Experimentalresults on various real world datasets show that theproposed method has superior performance over severalstate-of-the-art methods for multi-view clustering.
Pre-Trained Multi-View Word Embedding Using Two-Side Neural Network
Authors: Luo Yong , Tang Jian , Yan Jun , Xu Chao , Chen Zheng
Published: June 2022

Word embedding aims to learn a continuous representation for each word. It attracts increasing attention due to its effectiveness in various tasks such as named entity recognition and language modeling. Most existing word embedding results are generally trained on one individual data source such as news pages or Wikipedia articles. However, when we apply them to other tasks such as web search, the performance suffers. To obtain a robust word embedding for different applications, multiple data sources could be leveraged. In this paper, we proposed a two-side multimodal neural network to learn a robust word embedding from multiple data sources including free text, user search queries and search click-through data. This framework takes the word embeddings learned from different data sources as pre-train, and then uses a two-side neural network to unify these embeddings. The pre-trained embeddings are obtained by adapting the recently proposed CBOW algorithm. Since the proposed neural network does not need to re-train word embeddings for a new task, it is highly scalable in real world problem solving. Besides, the network allows weighting different sources differently when applied to different application tasks. Experiments on two real-world applications including web search ranking and word similarity measuring show that our neural network with multiple sources outperforms state-of-the-art word embedding algorithm with each individual source. It also outperforms other competitive baselines using multiple sources.
Hybrid Heterogeneous Transfer Learning through Deep Learning
Authors: Zhou Joey , Pan Sinno , Tsang Ivor , Yan Yan
Published: June 2022

Most previous heterogeneous transfer learning methods learn a cross-domain feature mapping between heterogeneous feature spaces based on a few cross-domain instance-correspondences, and these corresponding instances are assumed to be representative in the source and target domains respectively. However, in many real-world scenarios, this assumption may not hold. As a result, the constructed feature mapping may not be precisely due to the bias issue of the correspondences in the target or (and) source domain(s). In this case, a classifier trained on the labeled transformed-source-domain data may not be useful for the target domain. In this paper, we present a new transfer learning framework called Hybrid Heterogeneous Transfer Learning (HHTL), which allows the corresponding instances across domains to be biased in either the source or target domain. Specifically, we propose a deep learning approach to learn a feature mapping between cross-domain heterogeneous features as well as a better feature representation for mapped data to reduce the bias issue caused by the cross-domain correspondences. Extensive experiments on several multilingual sentiment classification tasks verify the effectiveness of our proposed approach compared with some baseline methods.
Multi-Instance Learning with Distribution Change
Authors: Zhang Wei-Jia , Zhou Zhi-Hua
Published: June 2022

Multi-instance learning deals with tasks where each example is a bag of instances, and the bag labels of training data are known whereas instance labels are unknown. Most previous studies on multi-instance learning assumed that the training and testing data are from the same distribution; however, this assumption is often violated in real tasks. In this paper, we present possibly the first study on multi-instance learning with distribution change. We propose the MICS approach by considering both bag-level and instance-level distribution change. Experiments show that MICS is almost always significantly better than many state-of-the-art multi-instance learning algorithms when distribution change occurs; and even when there is no distribution change, their performances are still comparable.
PAC Rank Elicitation through Adaptive Sampling of Stochastic Pairwise Preferences
Authors: Busa-Fekete Róbert , Szörényi Balázs , Hüllermeier Eyke
Published: June 2022

We introduce the problem of PAC rank elicitation, which consists of sorting a given set of options based on adaptive sampling of stochastic pairwise preferences. More specifically, we assume the existence of a ranking procedure, such as Copeland's method, that determines an underlying target order of the options. The goal is to predict a ranking that is sufficiently close to this target order with high probability, where closeness is measured in terms of a suitable distance measure. We instantiate this setting with combinations of two different distance measures and ranking procedures. For these instantiations, we devise efficient strategies for sampling pairwise preferences and analyze the corresponding sample complexity. We also present first experiments to illustrate the practical performance of our methods.
Monte Carlo Filtering Using Kernel Embedding of Distributions
Authors: Kanagawa Motonobu , Nishiyama Yu , Gretton Arthur , Fukumizu Kenji
Published: June 2022

Recent advances of kernel methods have yielded a framework for representing probabilities using a reproducing kernel Hilbert space, called kernel embedding of distributions. In this paper, we propose a Monte Carlo filtering algorithm based on kernel embeddings. The proposed method is applied to state-space models where sampling from the transition model is possible, while the observation model is to be learned from training samples without assuming a parametric model. As a theoretical basis of the proposed method, we prove consistency of the Monte Carlo method combined with kernel embeddings. Experimental results on synthetic models and real vision-based robot localization confirm the effectiveness of the proposed approach.
Learning with Augmented Class by Exploiting Unlabeled Data
Authors: Da Qing , Yu Yang , Zhou Zhi-Hua
Published: June 2022

In many real-world applications of learning, the environment is open and changes gradually, which requires the learning system to have the ability of detecting and adapting to the changes. Class-incremental learning (C-IL) is an important and practical problem where data from unseen augmented classes are fed, but has not been studied well in the past. In C-IL, the system should beware of predicting instances from augmented classes as a seen class, and thus faces the challenge that no such instances were observed during training stage. In this paper, we tackle the challenge by using unlabeled data, which can be cheaply collected in many real-world applications. We propose the LACU framework as well as the LACU-SVM approach to learn the concept of seen classes while incorporating the structure presented in the unlabeled data, so that the misclassification risks among the seen classes as well as between the augmented and the seen classes are minimized simultaneously. Experiments on diverse datasets show the effectiveness of the proposed approach.
Labeling Complicated Objects: Multi-View Multi-Instance Multi-Label Learning
Authors: Nguyen Cam-Tu , Wang Xiaoliang , Liu Jing , Zhou Zhi-Hua
Published: June 2022

Multi-Instance Multi-Label (MIML) is a learning framework where an example is associated with multiple labels and represented by a set of feature vectors (multiple instances). In the formalization of MIML learning, instances come from a single source (single view). To leverage multiple information sources (multi-view), we develop a multi-view MIML framework based on hierarchical Bayesian Network, and derive an effective learning algorithm based on variational inference. The model can naturally deal with examples in which some views could be absent (partial examples). On multi-view datasets, it is shown that our method is better than other multi-view and single-view approaches particularly in the presence of partial examples. On single-view benchmarks, extensive evaluation shows that our method is highly competitive or better than other MIML approaches on labeling examples and instances. Moreover, our method can effectively handle datasets with a large number of labels.
Wormhole Hamiltonian Monte Carlo
Authors: Lan Shiwei , Streets Jeffrey , Shahbaba Babak
Published: June 2022

In machine learning and statistics, probabilistic inference involving multimodal distributions is quite difficult. This is especially true in high dimensional problems, where most existing algorithms cannot easily move from one mode to another. To address this issue, we propose a novel Bayesian inference approach based on Markov Chain Monte Carlo. Our method can effectively sample from multimodal distributions, especially when the dimension is high and the modes are isolated. To this end, it exploits and modifies the Riemannian geometric properties of the target distribution to create \emph{wormholes} connecting modes in order to facilitate moving between them. Further, our proposed method uses the regeneration technique in order to adapt the algorithm by identifying new modes and updating the network of wormholes without affecting the stationary distribution. To find new modes, as opposed to rediscovering those previously identified, we employ a novel mode searching algorithm that explores a \emph{residual energy} function obtained by subtracting an approximate Gaussian mixture density (based on previously discovered modes) from the target density function.
Deep Modeling of Group Preferences for Group-Based Recommendation
Authors: Hu Liang , Cao Jian , Xu Guandong , Cao Longbing , Gu Zhiping , Cao Wei
Published: June 2022

Nowadays, most recommender systems (RSs) mainly aim to suggest appropriate items for individuals. Due to the social nature of human beings, group activities have become an integral part of our daily life, thus motivating the study on group RS (GRS). However, most existing methods used by GRS make recommendations through aggregating individual ratings or individual predictive results rather than considering the collective features that govern user choices made within a group. As a result, such methods are heavily sensitive to data, hence they often fail to learn group preferences when the data are slightly inconsistent with predefined aggregation assumptions. To this end, we devise a novel GRS approach which accommodates both individual choices and group decisions in a joint model. More specifically, we propose a deep-architecture model built with collective deep belief networks and dual-wing restricted Boltzmann machines. With such a deep model, we can use high-level features, which are induced from lower-level features, to represent group preference so as to relieve the vulnerability of data. Finally, the experiments conducted on a real-world dataset prove the superiority of our deep model over other state-of-the-art methods.
Pairwise-Covariance Linear Discriminant Analysis
Authors: Kong Deguang , Ding Chris
Published: June 2022

In machine learning, linear discriminant analysis (LDA) is a popular dimension reduction method. In this paper, we first provide a new perspective of LDA from an information theory perspective. From this new perspective, we propose a new formulation of LDA, which uses the pairwise averaged class covariance instead of theglobally averaged class covariance used in standard LDA. This pairwise (averaged) covariance describes data distribution more accurately. The new perspective also provides a natural way to properly weigh different pairwise distances, which emphasizes the pairs of class with small distances, and this leads to the proposed pairwise covariance properly weighted LDA (pcLDA). The kernel version of pcLDA is presented to handle nonlinear projections. Efficient algorithms are presented to efficiently compute the proposed models.
Delivering Guaranteed Display Ads under Reach and Frequency Requirements
Authors: Hojjat Ali , Turner John , Cetintas Suleyman , Yang Jian
Published: June 2022

We propose a novel idea in the allocation and serving of online advertising. We show that by using predetermined fixed-length streams of ads (which we call patterns) to serve advertising, we can incorporate a variety of interesting features into the ad allocation optimization problem. In particular, our formulation optimizes for representativeness as well as user-level diversity and pacing of ads, under reach and frequency requirements. We show how the problem can be solved efficiently using a column generation scheme in which only a small set of best patterns are kept in the optimization problem. Our numerical tests suggest that with parallelization of the pattern generation process, the algorithm has a promising run time and memory usage.
Parametrized Families of Hard Planning Problems from Phase Transitions
Authors: Rieffel Eleanor , Venturelli Davide , Do Minh , Hen Itay , Frank Jeremy
Published: June 2022

There are two complementary ways to evaluate planning algorithms: performance on benchmark problems derived from real applications and analysis of performance on parametrized families of problems with known properties. Prior to this work, few means of generating parametrized families of hard planning problems were known. We generate hard planning problems from the solvable/unsolvable phase transition region of well-studied NP-complete problems that map naturally to navigation and scheduling, aspects common to many planning domains. We observe significant differences between state-of-the-art planners on these problem families, enabling us to gain insight into the relative strengths and weaknesses of these planners. Our results confirm exponential scaling of hardness with problem size, even at very small problem sizes. These families provide complementary test sets exhibiting properties not found in existing benchmarks.
Chance-Constrained Probabilistic Simple Temporal Problems
Authors: Fang Cheng , Yu Peng , Williams Brian
Published: June 2022

Scheduling under uncertainty is essential to many autonomous systems and logistics tasks. Probabilistic methods for solving temporal problems exist which quantify and attempt to minimize the probability of schedule failure. These methods are overly conservative, resulting in a loss in schedule utility. Chance constrained formalism address over-conservatism by imposing bounds on risk, while maximizing utility subject to these risk bounds. In this paper we present the probabilistic Simple Temporal Network (pSTN), a probabilistic formalism for representing temporal problems with bounded risk and a utility over event timing. We introduce a constrained optimisation algorithm for pSTNs that achieves compactness and efficiency through a problem encoding in terms of a parameterised STNU and its reformulation as a parameterised STN. We demonstrate through a car sharing application that our chance-constrained approach runs in the same time as the previous probabilistic approach, yields solutions with utility improvements of at least 5% over previous arts, while guaranteeing operation within the specified risk bound.
R2: An Efficient MCMC Sampler for Probabilistic Programs
Authors: Nori Aditya , Hur Chung-Kil , Rajamani Sriram , Samuel Selva
Published: June 2022

We present a new Markov Chain Monte Carlo (MCMC) sampling algorithm for probabilistic programs. Our approach and tool, called R2, has the unique feature of employing program analysis in order to improve the efficiencyof MCMC sampling. Given an input program P, R2 propagates observations in P backwards to obtaina semantically equivalent program P' in which every probabilistic assignment is immediately followed by an observe statement. Inference is performed by a suitably modified version of the Metropolis-Hastings algorithm that exploits the structure of the program P'. This has the overall effect of preventing rejections due to program executions that fail to satisfy observations in P. We formalize the semantics of probabilistic programs and rigorously prove the correctness of R2. We also empirically demonstrate the effectiveness of R2—in particular, we show that R2 is able to produce results of similar quality as the CHURCH and STAN probabilistic programming tools with much shorter execution time.
Tree-Based On-Line Reinforcement Learning
Authors: Barreto Andre
Published: June 2022

Fitted Q-iteration (FQI) stands out among reinforcement learning algorithms for its flexibility and ease of use. FQI can be combined with any regression method, and this choice determines the algorithm's statistical and computational properties. The combination of FQI with an ensemble of regression trees gives rise to an algorithm, FQIT, that is computationally efficient, scalable to high dimensional spaces, and robust to noise. Despite its nice properties and good performance in practice, FQIT also has some limitations: the fact that an ensemble of trees must be constructed (or updated) at each iteration confines the algorithm to the batch scenario. This paper aims to address this specific issue. Based on a strategy recently proposed in the literature, called the stochastic-factorization trick, we propose a modification of FQIT that makes it fully incremental, and thus suitable for on-line learning. We call the resulting method tree-based stochastic factorization (TBSF). We derive upper bounds for the difference between the value functions computed by FQIT and TBSF, and also show in which circumstances the approximations coincide. A series of computational experiments is presented to illustrate the properties of TBSF and to show its usefulness in practice, including a medical problem involving the treatment of patients infected with HIV.
An Adversarial Interpretation of Information-Theoretic Bounded Rationality
Authors: Ortega Pedro , Lee Daniel
Published: June 2022

Recently, there has been a growing interest in modeling planning with information constraints. Accordingly, an agent maximizes a regularized expected utility known as the free energy, where the regularizer is given by the information divergence from a prior to a posterior policy. While this approach can be justified in various ways, including from statistical mechanics and information theory, it is still unclear how it relates to decision-making against adversarial environments. This connection has previously been suggested in work relating the free energy to risk-sensitive control and to extensive form games. Here, we show that a single-agent free energy optimization is equivalent to a game between the agent and an imaginary adversary. The adversary can, by paying an exponential penalty, generate costs that diminish the decision maker's payoffs. It turns out that the optimal strategy of the adversary consists in choosing costs so as to render the decision maker indifferent among its choices, which is a definining property of a Nash equilibrium, thus tightening the connection between free energy optimization and game theory.
Recovering from Selection Bias in Causal and Statistical Inference
Authors: Bareinboim Elias , Tian Jin , Pearl Judea
Published: June 2022

Selection bias is caused by preferential exclusion of units from the samples and represents a major obstacle to valid causal and statistical inferences; it cannot be removed by randomized experiments and can rarely be detected in either experimental or observational studies. In this paper, we provide complete graphical and algorithmic conditions for recovering conditional probabilities from selection biased data. We also provide graphical conditions for recoverability when unbiased data is available over a subset of the variables. Finally, we provide a graphical condition that generalizes the backdoor criterion and serves to recover causal effects when the data is collected under preferential selection.
Representing Words as Lymphocytes
Authors: Yang Jinfeng , Guan Yi , Dong Xishuang , He Bin
Published: June 2022

Similarity between words is becoming a generic problem for many applications of computational linguistics, and computing word similarities is determined by word representations. Inspired by the analogies between words and lymphocytes, a lymphocyte-style word representation is proposed. The word representation is built on the basis of dependency syntax of sentences and represent word context as head properties and dependent properties of the word. Lymphocyte-style word representations are evaluated by computing the similarities between words, and experiments are conducted on the Penn Chinese Treebank 5.1. Experimental results indicate that the proposed word representations are effective.
Semantical Clustering of Morphologically Related Chinese Words
Authors: Lee Chia-Ling , Chang Ya-Ning , Liu Chao-Lin , Lee Chia-Ying , Hsu Jane Yung-jen
Published: June 2022

A Chinese character embedded in different compound words may carry different meanings. In this paper, we aim at semantical clustering of a given family of morphologically related Chinese words. In Experiment 1, we employed linguistic features at the word, syntactic, semantic, and contextual levels in aggregated computational linguistics methods to handle the clustering task. In Experiment 2, we recruited adults and children to perform the clustering task. Experimental results indicate that our computational model achieved a similar level of performance as children.
A Knowledge Representation that Models Memory in Narrative Comprehension
Authors: Cardona-Rivera Rogelio , Young Robert
Published: June 2022

We present work toward computationally defining a model of narrative comprehension vis-à-vis memory of narrative events, via an automated planning knowledge representation, capable of being used in a narrative generation context.
Online Multi-Task Gradient Temporal-Difference Learning
Authors: Sreenivasan Vishnu , Bou Ammar Haitham , Eaton Eric
Published: June 2022

We develop an online multi-task formulation of model-based gradient temporal-difference (GTD) reinforcement learning. Our approach enables an autonomous RL agent to accumulate knowledge over its lifetime and efficiently share this knowledge between tasks to accelerate learning. Rather than learning a policy for a reinforcement learning task tabula rasa, as in standard GTD, our approach rapidly learns a high performance policy by building upon the agent's previously learned knowledge. Our preliminary results on controlling different mountain car tasks demonstrates that GTD-ELLA significantly improves learning over standard GTD(0).