Effective code optimization in compilers is crucial for computer and software engineering. The success of these optimizations primarily depends on the selection and ordering of the optimization passes applied to the code. While most compilers rely on fixed pass sequences, current methods to find the optimal sequence for specific programs either employ impractically slow search algorithms or learning methods that struggle to generalize to code unseen during training. To address these challenges, we introduce CompilerDream, the first world-model-based approach for general code optimization. CompilerDream features a compiler world model with a reward smoothing technique, enabling accurate simulation of optimization processes. Built on this model, code optimization agents can then be constructed via value prediction or direct optimization sequence generation. Trained on a large-scale program dataset, these agents serve as versatile code optimizers across diverse application scenarios and source-code languages. Our extensive experiments highlight CompilerDream's strong optimization capabilities for autotuning, where it leads the CompilerGym leaderboard. More importantly, the zero-shot generalization ability of large-scale trained compiler world model and agent, excels across diverse datasets, surpassing LLVM's built-in optimizations and state-of-the-art methods in both settings of value prediction and end-to-end code optimization.

Hyperparameter optimization (HPO) is known to be costly in deep learning, especially when leveraging automated approaches. Most of the existing automated HPO methods are accuracy-based, i.e., accuracy metrics are used to guide the trials of different hyperparameter configurations amongst a specific search space. However, many trials may encounter severe training problems, such as vanishing gradients and insufficient convergence, which can hardly be reflected by accuracy metrics in the early stages of the training and often result in poor performance. This leads to an inefficient optimization trajectory because the bad trials occupy considerable computation resources and reduce the probability of finding excellent hyperparameter configurations within a time limitation. In this paper, we propose Bad Trial Tackler (BTTackler), a novel HPO framework that introduces training diagnosis to identify training problems automatically and hence tackles bad trials. BTTackler diagnoses each trial by calculating a set of carefully designed quantified indicators and triggers early termination if any training problems are detected. Evaluations are performed on representative HPO tasks consisting of three classical deep neural networks (DNN) and four widely used HPO methods. To better quantify the effectiveness of an automated HPO method, we propose two new measurements based on accuracy and time consumption. Results show the advantage of BTTackler on two-fold: (1) it reduces 40.33% of time consumption to achieve the same accuracy comparable to baseline methods on average and (2) it conducts 44.5% more top-10 trials than baseline methods on average within a given time budget. We also released an open-source Python library that allows users to easily apply BTTackler to automated HPO processes with minimal code changes\footnotehttps://github.com/thuml/BTTackler.

Sequential recommendation requires the recommender to capture the evolving behavior characteristics from logged user behavior data for accurate recommendations. Nevertheless, user behavior sequences are viewed as a script with multiple ongoing threads intertwined. We find that only a small set of pivotal behaviors can be evolved into the user's future action. As a result, the future behavior of the user is hard to predict. We conclude this characteristic for sequential behaviors of each user as thebehavior pathway. Different users have their unique behavior pathways. Among existing sequential models, transformers have shown great capacity in capturing global-dependent characteristics. However, these models mainly provide a dense distribution over all previous behaviors using the self-attention mechanism, making the final predictions overwhelmed by the trivial behaviors not adjusted to each user. In this paper, we build the Recommender Transformer (RETR) with a novel Pathway Attention mechanism. RETR can dynamically plan the behavior pathway specified for each user, and sparingly activate the network through this behavior pathway to effectively capture evolving patterns useful for recommendation. The key design is a learned binary route to prevent the behavior pathway from being overwhelmed by trivial behaviors. Pathway attention is model-agnostic and can be applied to a series of transformer-based models for sequential recommendation. We empirically evaluate RETR on seven intra-domain benchmarks and RETR yields state-of-the-art performance. On another five cross-domain benchmarks, RETR can capture more domain-invariant representations for sequential recommendation.

Due to the storage and retrieval efficiency, hashing has been widely applied to approximate nearest neighbor search for large-scale multimedia retrieval. Cross-modal hashing, which enables efficient retrieval of images in response to text queries or vice versa, has received increasing attention recently. Most existing work on cross-modal hashing does not capture the spatial dependency of images and temporal dynamics of text sentences for learning powerful feature representations and cross-modal embeddings that mitigate the heterogeneity of different modalities. This paper presents a new Deep Visual-Semantic Hashing (DVSH) model that generates compact hash codes of images and sentences in an end-to-end deep learning architecture, which capture the intrinsic cross-modal correspondences between visual data and natural language. DVSH is a hybrid deep architecture that constitutes a visual-semantic fusion network for learning joint embedding space of images and text sentences, and two modality-specific hashing networks for learning hash functions to generate compact binary codes. Our architecture effectively unifies joint multimodal embedding and cross-modal hashing, which is based on a novel combination of Convolutional Neural Networks over images, Recurrent Neural Networks over sentences, and a structured max-margin objective that integrates all things together to enable learning of similarity-preserving and high-quality hash codes. Extensive empirical evidence shows that our DVSH approach yields state of the art results in cross-modal retrieval experiments on image-sentences datasets, i.e. standard IAPR TC-12 and large-scale Microsoft COCO.

Due to its storage and query efficiency, hashing has been widely applied to approximate nearest neighbor search from large-scale datasets. While there is increasing interest in cross-modal hashing which facilitates cross-media retrieval by embedding data from different modalities into a common Hamming space, how to distill the cross-modal correlation structure effectively remains a challenging problem. In this paper, we propose a novel supervised cross-modal hashing method, Correlation Autoencoder Hashing (CAH), to learn discriminative and compact binary codes based on deep autoencoders. Specifically, CAH jointly maximizes the feature correlation revealed by bimodal data and the semantic correlation conveyed in similarity labels, while embeds them into hash codes by nonlinear deep autoencoders. Extensive experiments clearly show the superior effectiveness and efficiency of CAH against the state-of-the-art hashing methods on standard cross-modal retrieval benchmarks.

Efficient similarity retrieval from large-scale multimodal database is pervasive in modern search engines and social networks. To support queries across content modalities, the system should enable cross-modal correlation and computation-efficient indexing. While hashing methods have shown great potential in achieving this goal, current attempts generally fail to learn isomorphic hash codes in a seamless scheme, that is, they embed multiple modalities in a continuous isomorphic space and separately threshold embeddings into binary codes, which incurs substantial loss of retrieval accuracy. In this paper, we approach seamless multimodal hashing by proposing a novel Composite Correlation Quantization (CCQ) model. Specifically, CCQ jointly finds correlation-maximal mappings that transform different modalities into isomorphic latent space, and learns composite quantizers that convert the isomorphic latent features into compact binary codes. An optimization framework is devised to preserve both intra-modal similarity and inter-modal correlation through minimizing both reconstruction and quantization errors, which can be trained from both paired and partially paired data in linear time. A comprehensive set of experiments clearly show the superior effectiveness and efficiency of CCQ against the state of the art hashing methods for both unimodal and cross-modal retrieval.

Domain adaptation generalizes a learning model across source domain and target domain that are sampled from different distributions. It is widely applied to cross-domain data mining for reusing labeled information and mitigating labeling consumption. Recent studies reveal that deep neural networks can learn abstract feature representation, which can reduce, but not remove, the cross-domain discrepancy. To enhance the invariance of deep representation and make it more transferable across domains, we propose a unified deep adaptation framework for jointly learning transferable representation and classifier to enable scalable domain adaptation, by taking the advantages of both deep learning and optimal two-sample matching. The framework constitutes two inter-dependent paradigms, unsupervised pre-training for effective training of deep models using deep denoising autoencoders, and supervised fine-tuning for effective exploitation of discriminative information using deep neural networks, both learned by embedding the deep representations to reproducing kernel Hilbert spaces (RKHSs) and optimally matching different domain distributions. To enable scalable learning, we develop a linear-time algorithm using unbiased estimate that scales linearly to large samples. Extensive empirical results show that the proposed framework significantly outperforms state of the art methods on diverse adaptation tasks: sentiment polarity prediction, email spam filtering, newsgroup content categorization, and visual object recognition.

Domain transfer learning generalizes a learning model across training data and testing data with different distributions. A general principle to tackle this problem is reducing the distribution difference between training data and testing data such that the generalization error can be bounded. Current methods typically model the sample distributions in input feature space, which depends on nonlinear feature mapping to embody the distribution discrepancy. However, this nonlinear feature space may not be optimal for the kernel-based learning machines. To this end, we propose a transfer kernel learning (TKL) approach to learn a domain-invariant kernel by directly matching source and target distributions in the reproducing kernel Hilbert space (RKHS). Specifically, we design a family of spectral kernels by extrapolating target eigensystem on source samples with Mercer's theorem. The spectral kernel minimizing the approximation error to the ground truth kernel is selected to construct domain-invariant kernel machines. Comprehensive experimental evidence on a large number of text categorization, image classification, and video event recognition datasets verifies the effectiveness and efficiency of the proposed TKL approach over several state-of-the-art methods.

Transfer learning is established as an effective technology to leverage rich labeled data from some source domain to build an accurate classifier for the target domain. The basic assumption is that the input domains may share certain knowledge structure, which can be encoded into common latent factors and extracted by preserving important property of original data, e.g., statistical property and geometric structure. In this paper, we show that different properties of input data can be complementary to each other and exploring them simultaneously can make the learning model robust to the domain difference. We propose a general framework, referred to as Graph Co-Regularized Transfer Learning (GTL), where various matrix factorization models can be incorporated. Specifically, GTL aims to extract common latent factors for knowledge transfer by preserving the statistical property across domains, and simultaneously, refine the latent factors to alleviate negative transfer by preserving the geometric structure in each domain. Based on the framework, we propose two novel methods using NMF and NMTF, respectively. Extensive experiments verify that GTL can significantly outperform state-of-the-art learning methods on several public text and image datasets.

Domain transfer learning, which learns a target classifier using labeled data from a different distribution, has shown promising value in knowledge discovery yet still been a challenging problem. Most previous works designed adaptive classifiers by exploring two learning strategies independently: distribution adaptation and label propagation. In this paper, we propose a novel transfer learning framework, referred to as Adaptation Regularization based Transfer Learning (ARTL), to model them in a unified way based on the structural risk minimization principle and the regularization theory. Specifically, ARTL learns the adaptive classifier by simultaneously optimizing the structural risk functional, the joint distribution matching between domains, and the manifold consistency underlying marginal distribution. Based on the framework, we propose two novel methods using Regularized Least Squares (RLS) and Support Vector Machines (SVMs), respectively, and use the Representer theorem in reproducing kernel Hilbert space to derive corresponding solutions. Comprehensive experiments verify that ARTL can significantly outperform state-of-the-art learning methods on several public text and image datasets.

Transfer learning aims to leverage the knowledge in the source domain to facilitate the learning tasks in the target domain. It has attracted extensive research interests recently due to its effectiveness in a wide range of applications. The general idea of the existing methods is to utilize the common latent structure shared across domains as the bridge for knowledge transfer. These methods usually model the common latent structure by using either the marginal distribution or the conditional distribution. However, without exploring the duality between these two distributions, these single bridge methods may not achieve optimal capability of knowledge transfer. In this paper, we propose a novel approach, Dual Transfer Learning (DTL), which simultaneously learns the marginal and conditional distributions, and exploits the duality between them in a principled way. The key idea behind DTL is that learning one distribution can help to learn the other. This duality property leads to mutual reinforcement when adapting both distributions across domains to transfer knowledge. The proposed method is formulated as an optimization problem based on joint nonnegative matrix trifactorizations (NMTF). The two distributions are learned from the decomposed latent factors that exhibit the duality property. An efficient alternating minimization algorithm is developed to solve the optimization problem with convergence guarantee. Extensive experimental results demonstrate that DTL is more effective than alternative transfer learning methods.

Transfer learning targets to leverage knowledge from one domain for tasks in a new domain. It finds abundant applications, such as text/sentiment classification. Many previous works are based on cluster analysis, which assume some common clusters shared by both domains. They mainly focus on the one-to-one cluster correspondence to bridge different domains. However, such a correspondence scheme might be too strong for real applications where each cluster in one domain corresponds to many clusters in the other domain. In this paper, we propose a Cluster Correspondence Inference (CCI) method to iteratively infer many-to-many correspondence among clusters from different domains. Specifically, word clusters and document clusters are exploited for each domain using nonnegative matrix factorization, then the word clusters from different domains are corresponded in a many-to-many scheme, with the help of shared word space as a bridge. These two steps are run iteratively and label information is transferred from source domain to target domain through the inferred cluster correspondence. Experiments on various real data sets demonstrate that our method outperforms several state-of-the-art approaches for cross-domain text classification.