Statement autoformalization, a crucial first step in formal verification, aims to transform informal descriptions of math problems into machine-verifiable formal representations but remains a significant challenge.The core difficulty lies in the fact that existing language models hallucinate formal dependencies, including missing or incorrect definitions, lemmas, and theorems.Current dependency retrieval approaches exhibit poor precision and recall, and lack the scalability to leverage ever-growing public datasets.To bridge this gap, we propose a novel retrieval-augmented framework based on Direct Dependency Retrieval (DDR).DDR directly generates candidate formal dependencies from natural-language mathematical descriptions and verifies their existence in the formal library via an efficient Suffix Array Check (SAC).Built on a SAC-constructed dependency retrieval dataset of over 500,000 samples, a high-precision DDR model is fine-tuned and shown to significantly outperform state-ofthe-art methods in both retrieval precision and recall, leading to superior advantage in the autoformalization tasks.SAC also contributes in assessing formalization difficulty and enabling explicit quantification of the hallucination in In-Context Learning (ICL).

High-quality data plays a critical role in the pretraining and fine-tuning of large language models (LLMs), even determining their performance ceiling to some degree. Consequently, numerous data selection methods have been proposed to identify subsets of data that can effectively and efficiently enhance model performance. However, most of these methods focus on general data selection and tend to overlook the specific nuances of domain-related data. In this paper, we introduce MASS, a Mathematical data Selection framework using the Skill graph for pretraining LLMs in the mathematical reasoning domain. By taking into account the unique characteristics of mathematics and reasoning, we construct a skill graph that captures the mathematical skills and their interrelations from a reference dataset. This skill graph guides us in assigning quality scores to the target dataset, enabling us to select the top-ranked subset which is further used to pretrain LLMs. Experimental results demonstrate the efficiency and effectiveness of MASS across different model sizes (1B and 7B) and pretraining datasets (web data and synthetic data). Specifically, in terms of efficiency, models trained on subsets selected by MASS can achieve similar performance to models trained on the original datasets, with a significant reduction in the number of trained tokens - ranging from 50% to 70% fewer tokens. In terms of effectiveness, when trained on the same amount of tokens, models trained on the data selected by MASS outperform those trained on the original datasets by 3.3% to 5.9%. These results underscore the potential of MASS to improve both the efficiency and effectiveness of pretraining LLMs.

Achieving balanced alignment of large language models (LLMs) in terms of Helpfulness, Honesty, and Harmlessness (3H optimization) constitutes a cornerstone of responsible AI. Existing methods like data mixture strategies face limitations, including heavy reliance on expert knowledge and conflicting optimization signals. While model merging offers parameter-level conflict-resolution strategies through integrating specialized models' parameters, its potential for 3H optimization remains underexplored. This paper systematically compares the effectiveness of model merging and data mixture methods in constructing 3H-aligned LLMs for the first time, revealing previously overlooked collaborative and conflict relationships among the 3H dimensions and discussing the advantages and drawbacks of data mixture (\textit{data-level}) and model merging (\textit{parameter-level}) methods in mitigating the conflict for balanced 3H optimization. Specially, we propose a novel \textbf{R}eweighting \textbf{E}nhanced task \textbf{S}ingular \textbf{M}erging method, \textbf{RESM}, through outlier weighting and sparsity-aware rank selection strategies to address the challenges of preference noise accumulation and layer sparsity adaptation inherent in 3H-aligned LLM merging. Extensive evaluations can verify the effectiveness and robustness of RESM compared to previous data mixture (2\%-5\% gain) and model merging (1\%-3\% gain) methods in achieving balanced LLM alignment.

In graph classification, the out-of-distribution (OOD) issue is attracting great attention. To address this issue, a prevailing idea is to learn stable features, on the assumption that they are substructures causally determining the label and that their relationship with the label is stable to the distributional uncertainty. In contrast, the complementary parts termed environmental features, fail to determine the label solely and hold varying relationships with the label, thus ascribed to the possible reason for the distribution shift. Existing generalization efforts mainly encourage the model’s insensitivity to environmental features. While the sensitivity to stable features is promising to distinguish the crucial clues from the distributional uncertainty but largely unexplored. A paradigm of simultaneously exploring the sensitivity to stable features and insensitivity to environmental features is until-now lacking to achieve the generalizable graph classification, to the best of our knowledge. In this work, we conjecture that generalizable models should be sensitive to stable features and insensitive to environmental features. To this end, we propose a simple yet effective augmentation strategy for graph classification: Equivariant and Invariant Cross-Data Augmentation (EI-CDA). By employing equivariance, given a pair of input graphs, we first estimate their stable and environmental features via masks. Then, we linearly mix the estimated stable features of two graphs and encourage the model predictions faithfully reflect their mixed semantics. Meanwhile, by using invariance, we swap the estimated environmental features of two graphs and keep the predictions invariant. This simple yet effective strategy endows the models with both sensitivity to stable features and insensitivity to environmental features. Extensive experiments show that EI-CDA significantly improves performance and outperforms leading baselines. Our codes are available at: https://github.com/yongduosui/EI-GNN .

Since artificial intelligence has seen tremendous recent successes in many areas, it has sparked great interest in its potential for trustworthy and interpretable risk prediction. However, most models lack causal reasoning and struggle with class imbalance, leading to poor precision and recall. To address this, we propose a Task-Driven Causal Feature Distillation model (TDCFD) to transform original feature values into causal feature attributions for the specific risk prediction task. The causal feature attribution helps describe how much contribution the value of this feature can make to the risk prediction result. After the causal feature distillation, a deep neural network is applied to produce trustworthy prediction results with causal interpretability and high precision/recall. We evaluate the performance of our TDCFD method on several synthetic and real datasets, and the results demonstrate its superiority over the state-of-the-art methods regarding precision, recall, interpretability, and causality.

Accurate prediction of coupon usage is crucial for promoting user consumption through targeted coupon recommendations. However, in real-world coupon recommendations, the coupon allocation process is not solely determined by the model trained with the history interaction data but is also interfered with by marketing tactics desired to fulfill specific commercial goals.This interference creates an imbalance in the interactions, which causes the data to deviate from the user's natural preferences. We refer to this deviation as the matching bias. Such biased interaction data affects the efficacy of the model, and thus it is necessary to employ debiasing techniques to prevent any negative impact. We investigate the mitigation of matching bias in coupon recommendations from a causal-effect perspective. By treating the attributes of users and coupons associated with marketing tactics as confounders, we find the confounders open the backdoor path between user-coupon matching and the conversion, which introduces spurious correlation. To remove the bad effect, we propose a novel training paradigm named Backdoor Adjustment via Group Adaptation (BAGA) for debiased coupon recommendations, which performs intervened training and inference, i.e., separately modeling each user-coupon group pair. However, modeling all possible group pairs greatly increases the computational complexity and cost. To address the efficiency challenge, we further present a simple but effective dual-tower multi-task framework and leverage the Customized Gate Control (CGC) model architecture, which separately models each user and coupon group with a separate expert module. We instantiate BAGA on five representative models: FM, DNN, NCF, MASKNET, and DEEPFM, and conduct comprehensive offline and online experiments to demonstrate the efficacy of our proposed paradigm.

Recommendation systems aim to provide users with relevant suggestions, but often lack interpretability and fail to capture higher-level semantic relationships between user behaviors and profiles. In this paper, we propose a novel approach that leverages large language models (LLMs) to construct personalized reasoning graphs. These graphs link a user's profile and behavioral sequences through causal and logical inferences, representing the user's interests in an interpretable way. Our approach, LLM reasoning graphs (LLMRG), has four components: chained graph reasoning, divergent extension, self-verification and scoring, and knowledge base self-improvement. The resulting reasoning graph is encoded using graph neural networks, which serves as additional input to improve conventional recommender systems, without requiring extra user or item information. Our approach demonstrates how LLMs can enable more logical and interpretable recommender systems through personalized reasoning graphs. LLMRG allows recommendations to benefit from both engineered recommendation systems and LLM-derived reasoning graphs. We demonstrate the effectiveness of LLMRG on benchmarks and real-world scenarios in enhancing base recommendation models.

Data from observational studies (OSs) is widely available and readily obtainable yet frequently contains confounding biases. On the other hand, data derived from randomized controlled trials (RCTs) helps to reduce these biases; however, it is expensive to gather, resulting in a tiny size of randomized data. For this reason, effectively fusing observational data and randomized data to better estimate heterogeneous treatment effects (HTEs) has gained increasing attention. However, existing methods for integrating observational data with randomized data must require complete observational data, meaning that both treated subjects and untreated subjects must be included in OSs. This prerequisite confines the applicability of such methods to very specific situations, given that including all subjects, whether treated or untreated, in observational studies is not consistently achievable. In our paper, we propose a resilient approach to Combine Incomplete Observational data and randomized data for HTE estimation, which we abbreviate as CIO. The CIO is capable of estimating HTEs efficiently regardless of the completeness of the observational data, be it full or partial. Concretely, a confounding bias function is first derived using the pseudo-experimental group from OSs, in conjunction with the pseudo-control group from RCTs, via an effect estimation procedure. This function is subsequently utilized as a corrective residual to rectify the observed outcomes of observational data during the HTE estimation by combining the available observational data and the all randomized data. To validate our approach, we have conducted experiments on a synthetic dataset and two semi-synthetic datasets.

Neural temporal point processes(TPPs) have shown promise for modeling continuous-time event sequences. However, capturing the interactions between events is challenging yet critical for performing inference tasks like forecasting on event sequence data. Existing TPP models have focused on parameterizing the conditional distribution of future events but struggle to model event interactions. In this paper, we propose a novel approach that leverages Neural Relational Inference (NRI) to learn a relation graph that infers interactions while simultaneously learning the dynamics patterns from observational data. Our approach, the Contrastive Relational Inference-based Hawkes Process (CRIHP), reasons about event interactions under a variational inference framework. It utilizes intensity-based learning to search for prototype paths to contrast relationship constraints. Extensive experiments on three real-world datasets demonstrate the effectiveness of our model in capturing event interactions for event sequence modeling tasks.

The importance of incorporating interpretability into machine learning models has been increasingly emphasized. While previous literature has typically focused on feature level interpretability, such as analyzing which features are important and how they influence the final decision, real-world applications often require domain level interpretability, which relates to a group of features. Domain-level interpretability holds the potential for enhanced informativeness and comprehensibility. Unfortunately, there has been limited research in this direction. In this paper, we address this issue and introduce our proposed method DIDE, which obtains domain-level interpretability from domain-specific latent embeddings. To enhance the effectiveness of the framework, we draw inspiration from the gradient smooth philosophy and propose noisy injection in the embedding space, resulting in smoothed interpretability. We conduct extensive experiments to validate the effectiveness of DIDE, and demonstrate its applications in assisting daily business tasks in Alipay.

Causal effect estimation from networked observational data encounters notable challenges, primarily hidden confounders arising from network structure, or spillover effects that influence unit's outcomes based on neighboring treatment assignments. Existing graph neural network (GNN)-based methods have endeavored to address these challenges, utilizing the GNN's message-passing mechanism to capture hidden confounders or model spillover effects. However, they mainly focus on transductive causal effect learning on a single networked data, limiting their efficacy in inductive settings for real-world applications where networked data often originates from multiple environments influenced by potentially varying time or geographical regions. In light of this, we introduce the principle of invariance to the task of causal effect estimation on networked data, culminating in our Invariant Graph Learning (IGL) framework. Specifically, it first generates multiple networked data to simulate diverse environments from a given observational data. Then it further encourages the model to learn environment-invariant representations for confounders and spillover effects. Such a design enables the model to extrapolate beyond a single observed environment, thereby improving the performance of causal effect estimation in potential new environments. Extensive experiments on two real-world datasets demonstrates the superiority of our approach.

This study considers the estimation of conditional causal effects in the presence of unmeasured confounding for a balanced panel with treatment imposed at the last time point. To address this, we combine Difference-in-differences (DiD) and tree-based methods and propose a new identification assumption that allows for the violation of the (conditional) parallel trends assumption adopted by most existing DiD methods. Under this new assumption, we prove partial identifiability of the conditional average treatment effect on the treated group (CATT). Our proposed method estimates CATT through a tree-based causal approach, guided by a novel splitting rule that avoids model misspecification and unnecessary auxiliary parameter estimation. The splitting rule measures both the error of fitting observed data and the violation of conditional parallel trends simultaneously. We also develop an ensemble of multiple trees via gradient boosting to further enhance performance. Experimental results on both synthetic and real-world datasets validate the effectiveness of our proposed method.

This paper proposes a novel strategy for estimating the heterogeneous treatment effect called the Fused and Accurate Shrinkage Tree ($\mathrm{FAST}$). Our approach utilizes both trial and observational data to improve the accuracy and robustness of the estimator. Inspired by the concept of shrinkage estimation in statistics, we develop an optimal weighting scheme and a corresponding estimator that balances the unbiased estimator based on the trial data with the potentially biased estimator based on the observational data. Specifically, combined with tree-based techniques, we introduce a new split criterion that utilizes both trial data and observational data to more accurately estimate the treatment effect. Furthermore, we confirm the consistency of our proposed tree-based estimator and demonstrate the effectiveness of our criterion in reducing prediction error through theoretical analysis. The advantageous finite sample performance of the $\mathrm{FAST}$ and its ensemble version over existing methods is demonstrated via simulations and real data analysis.

The issue of distribution shifts is emerging as a critical concern in graph representation learning. From the perspective of invariant learning and stable learning, a recently well-established paradigm for out-of-distribution generalization, stable features of the graph are assumed to causally determine labels, while environmental features tend to be unstable and can lead to the two primary types of distribution shifts. The correlation shift is often caused by the spurious correlation between environmental features and labels that differs between the training and test data; the covariate shift often stems from the presence of new environmental features in test data. However, most strategies, such as invariant learning or graph augmentation, typically struggle with limited training environments or perturbed stable features, thus exposing limitations in handling the problem of covariate shift. To address this challenge, we propose a simple-yet-effective data augmentation strategy, Adversarial Invariant Augmentation (AIA), to handle the covariate shift on graphs. Specifically, given the training data, AIA aims to extrapolate and generate new environments, while concurrently preserving the original stable features during the augmentation process. Such a design equips the graph classification model with an enhanced capability to identify stable features in new environments, thereby effectively tackling the covariate shift in data. Extensive experiments with in-depth empirical analysis demonstrate the superiority of our approach. The implementation codes are publicly available at https://github.com/yongduosui/AIA.

In marketing recommendations, the campaign organizers will distribute coupons to users to encourage consumption. In general, a series of strategies are employed to interfere with the coupon distribution process, leading to a growing imbalance between user-coupon interactions, resulting in a bias in the estimation of conversion probabilities. We refer to the estimation bias as the matching bias. In this paper, we explore how to alleviate the matching bias from the causal-effect perspective. We regard the historical distributions of users and coupons over each other as confounders and characterize the matching bias as a confounding effect to reveal and eliminate the spurious correlations between user-coupon representations and conversion probabilities. Then we propose a new training paradigm named De-Matching Bias Recommendation (DMBR) to remove the confounding effects during model training via the backdoor adjustment. We instantiate DMBR on two representative models: DNN and MMOE, and conduct extensive offline and online experiments to demonstrate the effectiveness of our proposed paradigm.

In recent years, many methods have been proposed to alleviate the biases in recommender systems by combining biased data and unbiased data. Among these methods, data imputation method is effective, but previous works only employ a straightforward model to generate imputed data, which can not fully characterize the data. In this paper, we propose a novel data imputation approach that combines an unbiased model and a debiasing model with adaptively learnt weights. We conduct extensive experiments on two public recommendation datasets and one production dataset to demonstrate the effectiveness and robustness of the proposed method.

To improve the marketing performance in the financial scenario, it is necessary to develop a trustworthy model to analyze and select promotion-sensitive customers. Bayesian Network (BN) is suitable for this task because of its interpretability and flexibility, but it usually suffers the exponentially growing computation complexity as the number of nodes grows. To tackle this problem, we present a comprehensive distributed platform named DistriBayes, which can efficiently learn, infer and attribute on a large-scale BN all-in-one platform. It implements several score-based structure learning methods, loopy belief propagation with backdoor adjustment for inference, and a carefully optimized search procedure for attribution. Leveraging the distributed cluster, DistriBayes can finish the learning and attribution on Bayesian Network with hundreds of nodes and millions of samples in hours.

Aspect-level sentiment analysis is a granular emotional classification task that refers to identifying sentiment polarities towards aspects in a sentence. Although previous research has reached a great achievement, this task remains very challenging. First, previous approaches only focus on one specific domain, which lacks the capability of transferring to other domains. Moreover, the majority of prior studies ignore the direct relationship between aspects and the corresponding sentiment words. To this end, in this paper, we propose a novel model named Efficient Adaptive Transfer Network (EATN) for aspect-level sentiment analysis which emphasizes the need of incorporating the correlation among multiple domains. The proposed EATN provides a Domain Adaptation Module (DAM) to learn common features from the sufficiently labeled source domain and to guide the classification performance in the target domain. Specifically, DAM comprises two special tasks, with one sentiment classification task aiming to learn sentiment knowledge and the other domain classification task focusing on learning domain-invariant features. Moreover, we design aspect-aware multi-head attention mechanism to capture the direct associations between the aspects and the contextual sentiment words, which is beneficial to learn the aspect-aware semantic knowledge. Extensive experiments demonstrate the effectiveness and universality of our method.

Data augmentation-based semi-supervised learning (SSL) methods have made great progress in computer vision and natural language processing areas. One of the most important factors is that the semantic structure invariance of these data allows the augmentation procedure (e.g., rotating images or masking words) to thoroughly utilize the enormous amount of unlabeled data. However, the tabular data does not possess an obvious invariant structure, and therefore similar data augmentation methods do not apply to it. To fill this gap, we present a simple yet efficient data augmentation method particular designed for tabular data and apply it to the SSL algorithm: SDAT (Semi-supervised learning with Data Augmentation for Tabular data). We adopt a multi-task learning framework that consists of two components: the data augmentation procedure and the consistency training procedure. The data augmentation procedure which perturbs in latent space employs a variational auto-encoder (VAE) to generate the reconstructed samples as augmented samples. The consistency training procedure constrains the predictions to be invariant between the augmented samples and the corresponding original samples. By sharing a representation network (encoder), we jointly train the two components to improve effectiveness and efficiency. Extensive experimental studies validate the effectiveness of the proposed method on the tabular datasets.

Decision rules have been successfully used in various classification applications because of their interpretability and efficiency. In many real-world scenarios, especially in industrial applications, it is necessary to generate rule sets under certain constraints, such as confidence constraints. However, most previous rule mining methods only emphasize the accuracy of the rule set but take no consideration of these constraints. In this paper, we propose a Confidence-constraint Rule Set Learning (CRSL) framework consisting of three main components, i.e. rule miner, rule ranker, and rule subset selector. Our method not only considers the trade-off between confidence and coverage of the rule set but also considers the trade-off between interpretability and performance. Experiments on benchmark data and large-scale industrial data demonstrate that the proposed method is able to achieve better performance (6.7% and 8.8% improvements) and competitive interpretability when compared with other rule set learning methods.

Unmeasured confounding poses a significant threat to the validity of causal inference. Despite that various ad hoc methods are developed to remove confounding effects, they are subject to certain fairly strong assumptions. In this work, we consider the estimation of conditional causal effects in the presence of unmeasured confounding using observational data and historical controls. Under an interpretable transportability condition, we prove the partial identifiability of conditional average treatment effect on the treated group (CATT). For tree-based models, a new notion, \emph{confounding entropy}, is proposed to measure the discrepancy introduced by unobserved confounders between the conditional outcome distribution of the treated and control groups. The confounding entropy generalizes conventional confounding bias, and can be estimated effectively using historical controls. We develop a new method, debiased causal tree, whose splitting rule is to minimize the empirical risk regularized by the confounding entropy. Notably, our method integrates current observational data (for empirical risk) and their historical controls (for confounding entropy) harmoniously. We highlight that, debiased causal tree can not only estimate CATT well in the presence of unmeasured confounding, but also is a robust estimator of conditional average treatment effect (CATE) against the imbalance of the treated and control populations when all confounders are observed. An extension of combining multiple debiased causal trees to further reduce biases by gradient boosting is considered. The computational feasibility and statistical power of our method are evidenced by simulations and a study of a credit card balance dataset.

In recent years, machine learning models have achieved magnificent success in many industrial applications, but most of them are black boxes. It is crucial to understand why such predictions are made in many critical areas such as medicine, financial markets, and auto driving. In this paper, we propose Coco, a novel interpretation method which can interpret any binary classifier by assigning each feature an importance value for a particular prediction. We first adopt MixUp method to generate reasonable perturbations, then apply these perturbations with constraints to obtain counterfactual instances and finally compute a comprehensive metric on these instances to estimate the importance of each feature. To demonstrate the effectiveness of Coco, we conduct extensive experiments on several datasets. The results show our method achieves better performance in identifying the most important features compared with the state-of-the-art interpretation methods, including Shap and Lime.

In the Click-Through Rate (CTR) prediction scenario, user's sequential behaviors are well utilized to capture the user interest in the recent literature. However, despite being extensively studied, these sequential methods still suffer from three limitations. First, existing methods mostly utilize attention on the behavior of users, which is not always suitable for CTR prediction, because users often click on new products that are irrelevant to any historical behaviors. Second, in the real scenario, there are numerous users that have operations a long time ago, but turn relatively inactive in recent times. Thus, it is hard to precisely capture user's current preferences through early behaviors. Third, multiple representations of user's historical behaviors in different feature subspaces are largely ignored. To remedy these issues, we propose a Multi-Interactive Attention Network (MIAN) to comprehensively extract the latent relationship among all kinds of fine-grained features (e.g., gender, age and occupation in user-profile). Specifically, MIAN contains a Multi-Interactive Layer (MIL) that integrates three local interaction modules to capture multiple representations of user preference through sequential behaviors and simultaneously utilize the fine-grained user-specific as well as context information. In addition, we design a Global Interaction Module (GIM) to learn the high-order interactions and balance the different impacts of multiple features. Finally, Offline experiment results from three datasets, together with an Online A/B test in a large-scale recommendation system, demonstrate the effectiveness of our proposed approach.

For any positive integer $k$, define $f(k)$ (respectively, $f_3(k)$) to be the minimal integer $\ge k$ such that every 3-connected planar graph $G$ (respectively, 3-connected cubic planar graph $G$) of circumference $\ge k$ has a cycle whose length is in the interval $[k, f(k)]$ (respectively, $[k, f_3(k)]$). Merker showed that $f_3(k) \le 2k + 9$ for any $k \ge 2$, and $f_3(k) \ge 2k + 2$ for any even $k \ge 4$. He conjectured that $f_3(k) \le 2k + 2$ for any $k \ge 2$. This conjecture was disproved by Zamfirescu, who gave an infinite family of counterexamples for every even $k \ge 6$ whose graphs have no cycle length in $[k, 2k + 2]$, i.e., $f_3(k) \ge 2k + 3$ for any even $k \ge 6$. However, the exact value of $f_3(k)$ was only known for $k \le 4$, and it is a natural problem to determine $f_3(k)$ for $k \ge 5$. In this paper, we improve Merker's upper bound, and give the exact value of $f_3(k)$ for every $k \ge 5$. We show that $f_3(5) = 10$, $f_3(7) = 15$, $f_3(9) = 20$, and $f_3(k) = 2k + 3$ for any $k = 6, 8$ or $\ge 10$. For general 3-connected planar graphs, Merker conjectured that there exists some positive integer $c$ such that $f(k) \le 2k + c$ for any positive integer $k$. We give a complete positive answer to this conjecture. We prove that $f(k) = 5$ for any $k \le 3$, $f(4) = 10$, and $f(k) = 2k + 3$ for any $k \ge 5$.

Existing recommendation algorithms mostly focus on optimizing traditional recommendation measures, such as the accuracy of rating prediction in terms of RMSE or the quality of top-k recommendation lists in terms of precision, recall, MAP, etc. However, an important expectation for commercial recommendation systems is to improve the final revenue/profit of the system. Traditional recommendation targets such as rating prediction and top-k recommendation are not directly related to this goal.

Tooth-marked tongue or crenated tongue can provide valuable diagnostic information for traditional Chinese Medicine doctors. However, tooth-marked tongue recognition is challenging. The characteristics of different tongues are multiform and have a great amount of variations, such as different colors, different shapes, and different types of teeth marks. The regions of teeth mark only appear along the lateral borders. Most existing methods make use of concave regions information to classify the tooth-marked tongue which leads to inconstant performance when the region of teeth mark is not concave. In this paper, we try to solve these problems by proposing a three-stage approach which first makes use of concavity information to propose the suspected regions, then use a convolutional neural network to extract deep features and at last use a multiple-instance classifier to make the final decision. Experimental results demonstrate the effectiveness of the proposed method.

In recent years, due to the emergence of Big Data (terabytes or petabytes) and Big Model (tens of billions of parameters), there has been an ever-increasing need of parallelizing machine learning (ML) algorithms in both academia and industry. Although there are some existing distributed computing systems, such as Hadoop and Spark, for parallelizing ML algorithms, they only provide synchronous and coarse-grained operators (e.g., Map, Reduce, and Join, etc.), which may hinder developers from implementing more efficient algorithms. This motivated us to design a universal distributed platform termed KunPeng, that combines both distributed systems and parallel optimization algorithms to deal with the complexities that arise from large-scale ML. Specifically, KunPeng not only encapsulates the characteristics of data/model parallelism, load balancing, model sync-up, sparse representation, industrial fault-tolerance, etc., but also provides easy-to-use interface to empower users to focus on the core ML logics. Empirical results on terabytes of real datasets with billions of samples and features demonstrate that, such a design brings compelling performance improvements on ML programs ranging from Follow-the-Regularized-Leader Proximal algorithm to Sparse Logistic Regression and Multiple Additive Regression Trees. Furthermore, KunPeng's encouraging performance is also shown for several real-world applications including the Alibaba's Double 11 Online Shopping Festival and Ant Financial's transaction risk estimation.

Neural network techniques are widely applied to obtain high-quality distributed representations of words (i.e., word embeddings) to address text mining, information retrieval, and natural language processing tasks. Most recent efforts have proposed several efficient methods to learn word embeddings from context such that they can encode both semantic and syntactic relationships between words. However, it is quite challenging to handle unseen or rare words with insufficient context. Inspired by the study on the word recognition process in cognitive psychology, in this article, we propose to take advantage of seemingly less obvious but essentially important morphological knowledge to address these challenges. In particular, we introduce a novel neural network architecture called KNET that leverages both words’ contextual information and morphological knowledge to learn word embeddings. Meanwhile, this new learning architecture is also able to benefit from noisy knowledge and balance between contextual information and morphological knowledge. Experiments on an analogical reasoning task and a word similarity task both demonstrate that the proposed KNET framework can greatly enhance the effectiveness of word embeddings.

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.