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Chen Liang

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28 papers
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28

IROS Conference 2025 Conference Paper

ad-trait: A Fast and Flexible Automatic Differentiation Library in Rust

  • Chen Liang
  • Qian Wang
  • Andy Xu
  • Daniel Rakita

The Rust programming language is an attractive choice for robotics and related fields, offering highly efficient and memory-safe code. However, a key limitation preventing its broader adoption in these domains is the lack of high-quality, well-supported Automatic Differentiation (AD)—a fundamental technique that enables convenient derivative computation by systematically accumulating data during function evaluation. In this work, we introduce ad-trait, a new Rust-based AD library. Our implementation overloads Rust’s standard floating-point type with a flexible trait that can efficiently accumulate necessary information for derivative computation. The library supports both forward-mode and reverse-mode automatic differentiation, making it the first operator-overloading AD implementation in Rust to offer both options. Additionally, ad-trait leverages Rust’s performance-oriented features, such as Single Instruction, Multiple Data acceleration in forward-mode AD, to enhance efficiency. Through benchmarking experiments, we show that our library is among the fastest AD implementations across several programming languages for computing derivatives. Moreover, it is already integrated into a Rust-based robotics library, where we showcase its ability to facilitate fast optimization procedures. We conclude with a discussion of the limitations and broader implications of our work.

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IROS Conference 2025 Conference Paper

IHGSL: Interpretable Heuristic Graph Structure Learning for Multi-Robot Autonomous Collaborative Systems

  • Yue Han
  • Hanqi Li
  • Cuiwei Liu
  • Chen Liang
  • Zhixiao Sun

In multi-robot systems, capturing the complex and dynamic interaction relationships is essential for enhancing autonomous collaboration. However, existing learning-based approaches usually overlook the understanding of these relationships, leading to reliability issues and hindering their application to real-world scenarios. This paper proposes a novel approach called Interpretable Heuristic Graph Structure Learning (IHGSL) to better comprehend the complex collaborative relationships in multi-robot systems. We first construct a predicate space to define diverse predicates that express fundamental relationships. Then we employ the variational information bottleneck technique to acquire a latent representation of the current observation by aligning it with the historical trajectory. On this basis, the predicates that the robot should currently focus on the most are learned, and some interaction relationships are established accordingly. Thereby an interpretable relationship graph is generated heuristically to guide the achievement of multi-robot autonomous collaborative decision-making. Through experimental evaluation, we demonstrate the process of relationship inference, thus validating the interpretability of IHGSL. Compared with existing methods, IHGSL also achieves superior collaboration performance, which highlights the effectiveness of the learned heuristic graph structure.

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NeurIPS Conference 2024 Conference Paper

Communication Efficient Distributed Training with Distributed Lion

  • Bo Liu
  • Lemeng Wu
  • Lizhang Chen
  • Kaizhao Liang
  • Jiaxu Zhu
  • Chen Liang
  • Raghuraman Krishnamoorthi
  • Qiang Liu

The Lion optimizer has been a promising competitor with the AdamW for training large AI models, with advantages in memory, computation, and sample efficiency. In this paper, we introduce Distributed Lion, an innovative adaptation of Lion for distributed training environments. Leveraging the sign operator in Lion, our Distributed Lion only requires to communicate binary or lower-precision vectorsbetween workers to the center server, significantly reducing the communication cost. Our theoretical analysis confirms Distributed Lion's convergence properties. Empirical results demonstrate its robustness across a range of tasks, worker counts, and batch sizes, on both vision and language problems. Notably, Distributed Lion attains comparable performance to standard Lion or AdamW optimizers applied on aggregated gradients, but with significantly reduced communication bandwidth. This feature is particularly advantageous for training large models. In addition, we also demonstrate that \mavolion{} presents a more favorable performance-bandwidth balance compared to existing efficient distributed methods such as deep gradient compression and ternary gradients.

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AAAI Conference 2024 Conference Paper

DR-Label: Label Deconstruction and Reconstruction of GNN Models for Catalysis Systems

  • Bowen Wang
  • Chen Liang
  • Jiaze Wang
  • Jiezhong Qiu
  • Furui Liu
  • Shaogang Hao
  • Dong Li
  • Guangyong Chen

Attaining the equilibrium geometry of a catalyst-adsorbate system is key to fundamentally assessing its effective properties, such as adsorption energy. While machine learning methods with advanced representation or supervision strategies have been applied to boost and guide the relaxation processes of catalysis systems, existing methods that produce linearly aggregated geometry predictions are susceptible to edge representations ambiguity, and are therefore vulnerable to graph variations. In this paper, we present a novel graph neural network (GNN) supervision and prediction strategy DR-Label. Our approach mitigates the multiplicity of solutions in edge representation and encourages model predictions that are independent of graph structural variations. DR-Label first Deconstructs finer-grained equilibrium state information to the model by projecting the node-level supervision signal to each edge. Reversely, the model Reconstructs a more robust equilibrium state prediction by converting edge-level predictions to node-level via a sphere-fitting algorithm. When applied to three fundamentally different models, DR-Label consistently enhanced performance. Leveraging the graph structure invariance of the DR-Label strategy, we further propose DRFormer, which applied explicit intermediate positional update and achieves a new state-of-the-art performance on the Open Catalyst 2020 (OC20) dataset and the Cu-based single-atom alloys CO adsorption (SAA) dataset. We expect our work to highlight vital principles for advancing geometric GNN models for catalysis systems and beyond. Our code is available at https://github.com/bowenwang77/DR-Label

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ICML Conference 2024 Conference Paper

Non-confusing Generation of Customized Concepts in Diffusion Models

  • Wang Lin
  • Jingyuan Chen
  • Jiaxin Shi
  • Yichen Zhu
  • Chen Liang
  • Junzhong Miao
  • Tao Jin 0004
  • Zhou Zhao 0001

We tackle the common challenge of inter-concept visual confusion in compositional concept generation using text-guided diffusion models (TGDMs). It becomes even more pronounced in the generation of customized concepts, due to the scarcity of user-provided concept visual examples. By revisiting the two major stages leading to the success of TGDMs—1) contrastive image-language pre-training (CLIP) for text encoder that encodes visual semantics, and 2) training TGDM that decodes the textual embeddings into pixels—we point that existing customized generation methods only focus on fine-tuning the second stage while overlooking the first one. To this end, we propose a simple yet effective solution called CLIF: contrastive image-language fine-tuning. Specifically, given a few samples of customized concepts, we obtain non-confusing textual embeddings of a concept by fine-tuning CLIP via contrasting a concept and the over-segmented visual regions of other concepts. Experimental results demonstrate the effectiveness of CLIF in preventing the confusion of multi-customized concept generation. Project page: https: //clif-official. github. io/clif.

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IROS Conference 2023 Conference Paper

Learning Reduced-Order Soft Robot Controller

  • Chen Liang
  • Xifeng Gao
  • Kui Wu 0003
  • Zherong Pan

Deformable robots are notoriously difficult to model or control due to its high-dimensional configuration spaces. Direct trajectory optimization suffers from the curse-of-dimensionality and incurs a high computational cost, while learning-based controller optimization methods are sensitive to hyper-parameter tuning. To overcome these limitations, we hypothesize that high fidelity soft robots can be both simulated and controlled by restricting to low-dimensional spaces. Under such assumption, we propose a two-stage algorithm to identify such simulation- and control-spaces. Our method first identifies the so-called simulation-space that captures the salient deformation modes, to which the robot's governing equation is restricted. We then identify the control-space, to which control signals are restricted. We propose a multi-fidelity Riemannian Bayesian bilevel optimization to identify task-specific control spaces. We show that the dimension of control-space can be less than 10 for a high-DOF soft robot to accomplish walking and swimming tasks, allowing low-dimensional MPC controllers to be applied to soft robots with tractable computational complexity.

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YNIMG Journal 2023 Journal Article

Mesoscale functional connectivity in macaque visual areas

  • Xingya Cai
  • Haoran Xu
  • Chao Han
  • Peichao Li
  • Jiayu Wang
  • Rui Zhang
  • Rendong Tang
  • Chen Fang

Studies of resting-state functional connectivity (rsFC) have provided rich insights into the structures and functions of the human brain. However, most rsFC studies have focused on large-scale brain connectivity. To explore rsFC at a finer scale, we used intrinsic signal optical imaging to image the ongoing activity of the anesthetized macaque visual cortex. Differential signals from functional domains were used to quantify network-specific fluctuations. In 30-60 min resting-state imaging, a series of coherent activation patterns were observed in all three visual areas we examined (V1, V2, and V4). These patterns matched the known functional maps (ocular dominance, orientation, color) obtained in visual stimulation conditions. These functional connectivity (FC) networks fluctuated independently over time and exhibited similar temporal characteristics. Coherent fluctuations, however, were observed from orientation FC networks in different areas and even across two hemispheres. Thus, FC in the macaque visual cortex was fully mapped both on a fine scale and over a long range. Hemodynamic signals can be used to explore mesoscale rsFC in a submillimeter resolution.

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NeurIPS Conference 2023 Conference Paper

Module-wise Adaptive Distillation for Multimodality Foundation Models

  • Chen Liang
  • Jiahui Yu
  • Ming-Hsuan Yang
  • Matthew Brown
  • Yin Cui
  • Tuo Zhao
  • Boqing Gong
  • Tianyi Zhou

Pre-trained multimodal foundation models have demonstrated remarkable generalizability but pose challenges for deployment due to their large sizes. One effective approach to reducing their sizes is layerwise distillation, wherein small student models are trained to match the hidden representations of large teacher models at each layer. Motivated by our observation that certain architecture components, referred to as modules, contribute more significantly to the student's performance than others, we propose to track the contributions of individual modules by recording the loss decrement after distillation each module and choose the module with a greater contribution to distill more frequently. Such an approach can be naturally formulated as a multi-armed bandit (MAB) problem, where modules and loss decrements are considered as arms and rewards, respectively. We then develop a modified-Thompson sampling algorithm named OPTIMA to address the nonstationarity of module contributions resulting from model updating. Specifically, we leverage the observed contributions in recent history to estimate the changing contribution of each module and select modules based on these estimations to maximize the cumulative contribution. We evaluate the effectiveness of OPTIMA through distillation experiments on various multimodal understanding and image captioning tasks, using the CoCa-Large model \citep{yu2022coca} as the teacher model.

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NeurIPS Conference 2023 Conference Paper

Symbolic Discovery of Optimization Algorithms

  • Xiangning Chen
  • Chen Liang
  • Da Huang
  • Esteban Real
  • Kaiyuan Wang
  • Hieu Pham
  • Xuanyi Dong
  • Thang Luong

We present a method to formulate algorithm discovery as program search, and apply it to discover optimization algorithms for deep neural network training. We leverage efficient search techniques to explore an infinite and sparse program space. To bridge the large generalization gap between proxy and target tasks, we also introduce program selection and simplification strategies. Our method discovers a simple and effective optimization algorithm, $\textbf{Lion}$ ($\textit{Evo$\textbf{L}$ved S$\textbf{i}$gn M$\textbf{o}$me$\textbf{n}$tum}$). It is more memory-efficient than Adam as it only keeps track of the momentum. Different from adaptive optimizers, its update has the same magnitude for each parameter calculated through the sign operation. We compare Lion with widely used optimizers, such as Adam and Adafactor, for training a variety of models on different tasks. On image classification, Lion boosts the accuracy of ViT by up to 2\% on ImageNet and saves up to 5x the pre-training compute on JFT. On vision-language contrastive learning, we achieve 88. 3\% $\textit{zero-shot}$ and 91. 1\% $\textit{fine-tuning}$ accuracy on ImageNet, surpassing the previous best results by 2\% and 0. 1\%, respectively. On diffusion models, Lion outperforms Adam by achieving a better FID score and reducing the training compute by up to 2. 3x. For autoregressive, masked language modeling, and fine-tuning, Lion exhibits a similar or better performance compared to Adam. Our analysis of Lion reveals that its performance gain grows with the training batch size. It also requires a smaller learning rate than Adam due to the larger norm of the update produced by the sign function. Additionally, we examine the limitations of Lion and identify scenarios where its improvements are small or not statistically significant.

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IJCAI Conference 2023 Conference Paper

The Effects of AI Biases and Explanations on Human Decision Fairness: A Case Study of Bidding in Rental Housing Markets

  • Xinru Wang
  • Chen Liang
  • Ming Yin

The use of AI-based decision aids in diverse domains has inspired many empirical investigations into how AI models’ decision recommendations impact humans’ decision accuracy in AI-assisted decision making, while explorations on the impacts on humans’ decision fairness are largely lacking despite their clear importance. In this paper, using a real-world business decision making scenario—bidding in rental housing markets—as our testbed, we present an experimental study on understanding how the bias level of the AI-based decision aid as well as the provision of AI explanations affect the fairness level of humans’ decisions, both during and after their usage of the decision aid. Our results suggest that when people are assisted by an AI-based decision aid, both the higher level of racial biases the decision aid exhibits and surprisingly, the presence of AI explanations, result in more unfair human decisions across racial groups. Moreover, these impacts are partly made through triggering humans’ “disparate interactions” with AI. However, regardless of the AI bias level and the presence of AI explanations, when people return to make independent decisions after their usage of the AI-based decision aid, their decisions no longer exhibit significant unfairness across racial groups.

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NeurIPS Conference 2022 Conference Paper

GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Models

  • Chen Liang
  • Wenguan Wang
  • Jiaxu Miao
  • Yi Yang

Prevalent semantic segmentation solutions are, in essence, a dense discriminative classifier of p(class|pixel feature). Though straightforward, this de facto paradigm neglects the underlying data distribution p(pixel feature|class), and struggles to identify out-of-distribution data. Going beyond this, we propose GMMSeg, a new family of segmentation models that rely on a dense generative classifier for the joint distribution p(pixel feature, class). For each class, GMMSeg builds Gaussian Mixture Models (GMMs) via Expectation-Maximization (EM), so as to capture class-conditional densities. Meanwhile, the deep dense representation is end-to-end trained in a discriminative manner, i. e. , maximizing p(class|pixel feature). This endows GMMSeg with the strengths of both generative and discriminative models. With a variety of segmentation architectures and backbones, GMMSeg outperforms the discriminative counterparts on three closed-set datasets. More impressively, without any modification, GMMSeg even performs well on open-world datasets. We believe this work brings fundamental insights into the related fields.

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AAAI Conference 2021 Conference Paper

A Primal-Dual Online Algorithm for Online Matching Problem in Dynamic Environments

  • Yu-Hang Zhou
  • Peng Hu
  • Chen Liang
  • Huan Xu
  • Guangda Huzhang
  • Yinfu Feng
  • Qing Da
  • Xinshang Wang

Recently, the online matching problem has attracted much attention due to its wide application on real-world decisionmaking scenarios. In stationary environments, by adopting the stochastic user arrival model, existing methods are proposed to learn dual optimal prices and are shown to achieve a fast regret bound. However, the stochastic model is no longer a proper assumption when the environment is changing, leading to an optimistic method that may suffer poor performance. In this paper, we study the online matching problem in dynamic environments in which the dual optimal prices are allowed to vary over time. We bound the dynamic regret of online matching problem by the sum of two quantities, including a regret of online max-min problem and a dynamic regret of online convex optimization (OCO) problem. Then we propose a novel online approach named Primal-Dual Online Algorithm (PDOA) to minimize both quantities. In particular, PDOA adopts the primal-dual framework by optimizing dual prices with the online gradient descent (OGD) algorithm to eliminate the online max-min problem’s regret. Moreover, it maintains a set of OGD experts and combines them via an expert-tracking algorithm, which gives a sublinear dynamic regret bound for the OCO problem. We show that PDOA achieves an O(K p T(1 + PT )) dynamic regret where K is the number of resources, T is the number of iterations and PT is the path-length of any potential dual price sequence that reflects the dynamic environment. Finally, experiments on real applications exhibit the superiority of our approach.

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AAAI Conference 2021 Short Paper

Global Fusion Attention for Vision and Language Understanding (Student Abstract)

  • Zixin Guo
  • Chen Liang
  • Ziyu Wan
  • Yang Bai

We extend the popular transformer architecture to a multimodal model, processing both visual and textual inputs. We propose a new attention mechanism on Transformer-based architecture for the joint vision and language understanding tasks. Our model fuses multi-level comprehension between images and texts in a weighted manner, which could better curve the internal relationships. Experiments on benchmark VQA dataset CLEVR demonstrate the effectiveness of the proposed attention mechanism. We also observe the improvements in sample efficiency of reinforcement learning through the experiments on grounded language understanding tasks of BabyAI platform.

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ICML Conference 2020 Conference Paper

AutoML-Zero: Evolving Machine Learning Algorithms From Scratch

  • Esteban Real
  • Chen Liang
  • David R. So
  • Quoc V. Le

Machine learning research has advanced in multiple aspects, including model structures and learning methods. The effort to automate such research, known as AutoML, has also made significant progress. However, this progress has largely focused on the architecture of neural networks, where it has relied on sophisticated expert-designed layers as building blocks—or similarly restrictive search spaces. Our goal is to show that AutoML can go further: it is possible today to automatically discover complete machine learning algorithms just using basic mathematical operations as building blocks. We demonstrate this by introducing a novel framework that significantly reduces human bias through a generic search space. Despite the vastness of this space, evolutionary search can still discover two-layer neural networks trained by backpropagation. These simple neural networks can then be surpassed by evolving directly on tasks of interest, e. g. CIFAR-10 variants, where modern techniques emerge in the top algorithms, such as bilinear interactions, normalized gradients, and weight averaging. Moreover, evolution adapts algorithms to different task types: e. g. , dropout-like techniques appear when little data is available. We believe these preliminary successes in discovering machine learning algorithms from scratch indicate a promising new direction for the field.

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NeurIPS Conference 2020 Conference Paper

Compositional Generalization via Neural-Symbolic Stack Machines

  • Xinyun Chen
  • Chen Liang
  • Adams Wei Yu
  • Dawn Song
  • Denny Zhou

Despite achieving tremendous success, existing deep learning models have exposed limitations in compositional generalization, the capability to learn compositional rules and apply them to unseen cases in a systematic manner. To tackle this issue, we propose the Neural-Symbolic Stack Machine (NeSS). It contains a neural network to generate traces, which are then executed by a symbolic stack machine enhanced with sequence manipulation operations. NeSS combines the expressive power of neural sequence models with the recursion supported by the symbolic stack machine. Without training supervision on execution traces, NeSS achieves 100% generalization performance in four domains: the SCAN benchmark of language-driven navigation tasks, the task of few-shot learning of compositional instructions, the compositional machine translation benchmark, and context-free grammar parsing tasks.

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ICLR Conference 2020 Conference Paper

Neural Symbolic Reader: Scalable Integration of Distributed and Symbolic Representations for Reading Comprehension

  • Xinyun Chen
  • Chen Liang
  • Adams Wei Yu
  • Denny Zhou
  • Dawn Song
  • Quoc V. Le

Integrating distributed representations with symbolic operations is essential for reading comprehension requiring complex reasoning, such as counting, sorting and arithmetics, but most existing approaches are hard to scale to more domains or more complex reasoning. In this work, we propose the Neural Symbolic Reader (NeRd), which includes a reader, e.g., BERT, to encode the passage and question, and a programmer, e.g., LSTM, to generate a program that is executed to produce the answer. Compared to previous works, NeRd is more scalable in two aspects: (1) domain-agnostic, i.e., the same neural architecture works for different domains; (2) compositional, i.e., when needed, complex programs can be generated by recursively applying the predefined operators, which become executable and interpretable representations for more complex reasoning. Furthermore, to overcome the challenge of training NeRd with weak supervision, we apply data augmentation techniques and hard Expectation-Maximization (EM) with thresholding. On DROP, a challenging reading comprehension dataset that requires discrete reasoning, NeRd achieves 1.37%/1.18% absolute improvement over the state-of-the-art on EM/F1 metrics. With the same architecture, NeRd significantly outperforms the baselines on MathQA, a math problem benchmark that requires multiple steps of reasoning, by 25.5% absolute increment on accuracy when trained on all the annotated programs. More importantly, NeRd still beats the baselines even when only 20% of the program annotations are given.

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NeurIPS Conference 2020 Conference Paper

PyGlove: Symbolic Programming for Automated Machine Learning

  • Daiyi Peng
  • Xuanyi Dong
  • Esteban Real
  • Mingxing Tan
  • Yifeng Lu
  • Gabriel Bender
  • Hanxiao Liu
  • Adam Kraft

Neural networks are sensitive to hyper-parameter and architecture choices. Automated Machine Learning (AutoML) is a promising paradigm for automating these choices. Current ML software libraries, however, are quite limited in handling the dynamic interactions among the components of AutoML. For example, efficient NAS algorithms, such as ENAS and DARTS, typically require an implementation coupling between the search space and search algorithm, the two key components in AutoML. Furthermore, implementing a complex search flow, such as searching architectures within a loop of searching hardware configurations, is difficult. To summarize, changing the search space, search algorithm, or search flow in current ML libraries usually requires a significant change in the program logic. In this paper, we introduce a new way of programming AutoML based on symbolic programming. Under this paradigm, ML programs are mutable, thus can be manipulated easily by another program. As a result, AutoML can be reformulated as an automated process of symbolic manipulation. With this formulation, we decouple the triangle of the search algorithm, the search space and the child program. This decoupling makes it easy to change the search space and search algorithm (without and with weight sharing), as well as to add search capabilities to existing code and implement complex search flows. We then introduce PyGlove, a new Python library that implements this paradigm. Through case studies on ImageNet and NAS-Bench-101, we show that with PyGlove users can easily convert a static program into a search space, quickly iterate on the search spaces and search algorithms, and craft complex search flows to achieve better results.

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AAAI Conference 2019 Conference Paper

DeepChannel: Salience Estimation by Contrastive Learning for Extractive Document Summarization

  • Jiaxin Shi
  • Chen Liang
  • Lei Hou
  • Juanzi Li
  • Zhiyuan Liu
  • Hanwang Zhang

We propose DeepChannel, a robust, data-efficient, and interpretable neural model for extractive document summarization. Given any document-summary pair, we estimate a salience score, which is modeled using an attention-based deep neural network, to represent the salience degree of the summary for yielding the document. We devise a contrastive training strategy to learn the salience estimation network, and then use the learned salience score as a guide and iteratively extract the most salient sentences from the document as our generated summary. In experiments, our model not only achieves state-of-the-art ROUGE scores on CNN/Daily Mail dataset, but also shows strong robustness in the out-of-domain test on DUC2007 test set. Moreover, our model reaches a ROUGE-1 F-1 score of 39. 41 on CNN/Daily Mail test set with merely 1/100 training set, demonstrating a tremendous data efficiency.

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ICML Conference 2019 Conference Paper

Learning to Generalize from Sparse and Underspecified Rewards

  • Rishabh Agarwal
  • Chen Liang
  • Dale Schuurmans
  • Mohammad Norouzi 0002

We consider the problem of learning from sparse and underspecified rewards, where an agent receives a complex input, such as a natural language instruction, and needs to generate a complex response, such as an action sequence, while only receiving binary success-failure feedback. Such success-failure rewards are often underspecified: they do not distinguish between purposeful and accidental success. Generalization from underspecified rewards hinges on discounting spurious trajectories that attain accidental success, while learning from sparse feedback requires effective exploration. We address exploration by using a mode covering direction of KL divergence to collect a diverse set of successful trajectories, followed by a mode seeking KL divergence to train a robust policy. We propose Meta Reward Learning (MeRL) to construct an auxiliary reward function that provides more refined feedback for learning. The parameters of the auxiliary reward function are optimized with respect to the validation performance of a trained policy. The MeRL approach outperforms an alternative method for reward learning based on Bayesian Optimization, and achieves the state-of-the-art on weakly-supervised semantic parsing. It improves previous work by 1. 2% and 2. 4% on WikiTableQuestions and WikiSQL datasets respectively.

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AAAI Conference 2019 Conference Paper

Robust Online Matching with User Arrival Distribution Drift

  • Yu-Hang Zhou
  • Chen Liang
  • Nan Li
  • Cheng Yang
  • Shenghuo Zhu
  • Rong Jin

Recently, online matching problems have attracted much attention due to its emerging applications in internet advertising. Most existing online matching methods have adopted either adversarial or stochastic user arrival assumption, while on both of them significant limitation exists. The adversarial model does not exploit existing knowledge of the user sequence, and thus can be pessimistic in practice. On other hands, the stochastic model assumes that users are drawn from a stationary distribution, which may not be true in real applications. In this paper, we consider a novel user arrival model where users are drawn from drifting distribution, which is a hybrid case between the adversarial and stochastic model, and propose a new approach RDLA to deal with such assumption. Instead of maximizing empirical total revenues on the revealed users, RDLA leverages distributionally robust optimization techniques to learn dual variables via a worst-case consideration over an ambiguity set on the underlying user distribution. Experiments on a real-world dataset exhibit the superiority of our approach.

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ICML Conference 2019 Conference Paper

The Evolved Transformer

  • David R. So
  • Quoc V. Le
  • Chen Liang

Recent works have highlighted the strength of the Transformer architecture on sequence tasks while, at the same time, neural architecture search (NAS) has begun to outperform human-designed models. Our goal is to apply NAS to search for a better alternative to the Transformer. We first construct a large search space inspired by the recent advances in feed-forward sequence models and then run evolutionary architecture search with warm starting by seeding our initial population with the Transformer. To directly search on the computationally expensive WMT 2014 English-German translation task, we develop the Progressive Dynamic Hurdles method, which allows us to dynamically allocate more resources to more promising candidate models. The architecture found in our experiments – the Evolved Transformer – demonstrates consistent improvement over the Transformer on four well-established language tasks: WMT 2014 English-German, WMT 2014 English-French, WMT 2014 English-Czech and LM1B. At a big model size, the Evolved Transformer establishes a new state-of-the-art BLEU score of 29. 8 on WMT’14 English-German; at smaller sizes, it achieves the same quality as the original "big" Transformer with 37. 6% less parameters and outperforms the Transformer by 0. 7 BLEU at a mobile-friendly model size of 7M parameters.

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AAAI Conference 2018 Conference Paper

Investigating Active Learning for Concept Prerequisite Learning

  • Chen Liang
  • Jianbo Ye
  • Shuting Wang
  • Bart Pursel
  • C. Lee Giles

Concept prerequisite learning focuses on machine learning methods for measuring the prerequisite relation among concepts. With the importance of prerequisites for education, it has recently become a promising research direction. A major obstacle to extracting prerequisites at scale is the lack of large scale labels which will enable effective data driven solutions. We investigate the applicability of active learning to concept prerequisite learning. We propose a novel set of features tailored for prerequisite classification and compare the effectiveness of four widely used query strategies. Experimental results for domains including data mining, geometry, physics, and precalculus show that active learning can be used to reduce the amount of training data required. Given the proposed features, the query-by-committee strategy outperforms other compared query strategies.

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NeurIPS Conference 2018 Conference Paper

Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing

  • Chen Liang
  • Mohammad Norouzi
  • Jonathan Berant
  • Quoc Le
  • Ni Lao

We present Memory Augmented Policy Optimization (MAPO), a simple and novel way to leverage a memory buffer of promising trajectories to reduce the variance of policy gradient estimate. MAPO is applicable to deterministic environments with discrete actions, such as structured prediction and combinatorial optimization tasks. We express the expected return objective as a weighted sum of two terms: an expectation over the high-reward trajectories inside the memory buffer, and a separate expectation over trajectories outside the buffer. To make an efficient algorithm of MAPO, we propose: (1) memory weight clipping to accelerate and stabilize training; (2) systematic exploration to discover high-reward trajectories; (3) distributed sampling from inside and outside of the memory buffer to scale up training. MAPO improves the sample efficiency and robustness of policy gradient, especially on tasks with sparse rewards. We evaluate MAPO on weakly supervised program synthesis from natural language (semantic parsing). On the WikiTableQuestions benchmark, we improve the state-of-the-art by 2. 6%, achieving an accuracy of 46. 3%. On the WikiSQL benchmark, MAPO achieves an accuracy of 74. 9% with only weak supervision, outperforming several strong baselines with full supervision. Our source code is available at https: //goo. gl/TXBp4e

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AAAI Conference 2017 Conference Paper

Definition Modeling: Learning to Define Word Embeddings in Natural Language

  • Thanapon Noraset
  • Chen Liang
  • Larry Birnbaum
  • Doug Downey

Distributed representations of words have been shown to capture lexical semantics, as demonstrated by their effectiveness in word similarity and analogical relation tasks. But, these tasks only evaluate lexical semantics indirectly. In this paper, we study whether it is possible to utilize distributed representations to generate dictionary definitions of words, as a more direct and transparent representation of the embeddings’ semantics. We introduce definition modeling, the task of generating a definition for a given word and its embedding. We present several definition model architectures based on recurrent neural networks, and experiment with the models over multiple data sets. Our results show that a model that controls dependencies between the word being defined and the definition words performs significantly better, and that a characterlevel convolution layer designed to leverage morphology can complement word-level embeddings. Finally, an error analysis suggests that the errors made by a definition model may provide insight into the shortcomings of word embeddings.

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AAAI Conference 2016 Conference Paper

BBookX: Building Online Open Books for Personalized Learning

  • Chen Liang
  • Shuting Wang
  • Zhaohui Wu
  • Kyle Williams
  • Bart Pursel
  • Benjamin Brautigam
  • Sherwyn Saul
  • Hannah Williams

We demonstrate BBookX1, a novel system that automatically builds in collaboration with a user online open books by searching open educational resources (OER). This system explores the use of retrieval technologies to dynamically generate zero-cost materials such as textbooks for personalized learning.

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IJCAI Conference 2016 Conference Paper

Learning Paraphrase Identification with Structural Alignment

  • Chen Liang
  • Praveen Paritosh
  • Vinodh Rajendran
  • Kenneth D. Forbus

Semantic similarity of text plays an important role in many NLP tasks. It requires using both local information like lexical semantics and structural information like syntactic structures. Recent progress in word representation provides good resources for lexical semantics, and advances in natural language analysis tools make it possible to efficiently generate syntactic and semantic annotations. However, how to combine them to capture the semantics of text is still an open question. Here, we propose a new alignment-based approach to learn semantic similarity. It uses a hybrid representation, attributed relational graphs, to encode lexical, syntactic and semantic information. Alignment of two such graphs combines local and structural information to support similarity estimation. To improve alignment, we introduced structural constraints inspired by a cognitive theory of similarity and analogy. Usually only similarity labels are given in training data and the true alignments are unknown, so we address the learning problem using two approaches: alignment as feature extraction and alignment as latent variable. Our approach is evaluated on the paraphrase identification task and achieved results competitive with the state-of-the-art.

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AAAI Conference 2015 Conference Paper

A Neural Probabilistic Model for Context Based Citation Recommendation

  • Wenyi Huang
  • Zhaohui Wu
  • Chen Liang
  • Prasenjit Mitra
  • C. Giles

Automatic citation recommendation can be very useful for authoring a paper and is an AI-complete problem due to the challenge of bridging the semantic gap between citation context and the cited paper. It is not always easy for knowledgeable researchers to give an accurate citation context for a cited paper or to find the right paper to cite given context. To help with this problem, we propose a novel neural probabilistic model that jointly learns the semantic representations of citation contexts and cited papers. The probability of citing a paper given a citation context is estimated by training a multi-layer neural network. We implement and evaluate our model on the entire CiteSeer dataset, which at the time of this work consists of 10, 760, 318 citation contexts from 1, 017, 457 papers. We show that the proposed model significantly outperforms other stateof-the-art models in recall, MAP, MRR, and nDCG.

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AAAI Conference 2015 Conference Paper

Learning Plausible Inferences from Semantic Web Knowledge by Combining Analogical Generalization with Structured Logistic Regression

  • Chen Liang
  • Kenneth Forbus

Fast and efficient learning over large bodies of commonsense knowledge is a key requirement for cognitive systems. Semantic web knowledge bases provide an important new resource of ground facts from which plausible inferences can be learned. This paper applies structured logistic regression with analogical generalization (SLogAn) to make use of structural as well as statistical information to achieve rapid and robust learning. SLogAn achieves state-of-the-art performance in a standard triplet classification task on two data sets and, in addition, can provide understandable explanations for its answers.

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