Search Results for author: Xinyun Chen

Found 41 papers, 17 papers with code

Learning to Progressively Plan

no code implementations ICLR 2019 Xinyun Chen, Yuandong Tian

For problem solving, making reactive decisions based on problem description is fast but inaccurate, while search-based planning using heuristics gives better solutions but could be exponentially slow.

reinforcement-learning

Measuring and Improving Compositional Generalization in Text-to-SQL via Component Alignment

no code implementations4 May 2022 Yujian Gan, Xinyun Chen, Qiuping Huang, Matthew Purver

To deal with this problem, we modify a number of state-of-the-art models to train on the segmented data of Spider-SS, and we show that this method improves the generalization performance.

Text-To-Sql

Learning Bounded Context-Free-Grammar via LSTM and the Transformer:Difference and Explanations

1 code implementation16 Dec 2021 Hui Shi, Sicun Gao, Yuandong Tian, Xinyun Chen, Jishen Zhao

With the forced decomposition, we show that the performance upper bounds of LSTM and Transformer in learning CFL are close: both of them can simulate a stack and perform stack operation along with state transitions.

SMORE: Knowledge Graph Completion and Multi-hop Reasoning in Massive Knowledge Graphs

1 code implementation28 Oct 2021 Hongyu Ren, Hanjun Dai, Bo Dai, Xinyun Chen, Denny Zhou, Jure Leskovec, Dale Schuurmans

There are two important reasoning tasks on KGs: (1) single-hop knowledge graph completion, which involves predicting individual links in the KG; and (2), multi-hop reasoning, where the goal is to predict which KG entities satisfy a given logical query.

Knowledge Graph Completion

Towards Defending Multiple $\ell_p$-Norm Bounded Adversarial Perturbations via Gated Batch Normalization

no code implementations29 Sep 2021 Aishan Liu, Shiyu Tang, Xianglong Liu, Xinyun Chen, Lei Huang, Haotong Qin, Dawn Song, DaCheng Tao

We observe that different $\ell_p$ bounded adversarial perturbations induce different statistical properties that can be separated and characterized by the statistics of Batch Normalization (BN).

Natural SQL: Making SQL Easier to Infer from Natural Language Specifications

1 code implementation Findings (EMNLP) 2021 Yujian Gan, Xinyun Chen, Jinxia Xie, Matthew Purver, John R. Woodward, John Drake, Qiaofu Zhang

Addressing the mismatch between natural language descriptions and the corresponding SQL queries is a key challenge for text-to-SQL translation.

Text-To-Sql Translation

Exploring Underexplored Limitations of Cross-Domain Text-to-SQL Generalization

1 code implementation EMNLP 2021 Yujian Gan, Xinyun Chen, Matthew Purver

Recently, there has been significant progress in studying neural networks for translating text descriptions into SQL queries under the zero-shot cross-domain setting.

Text-To-Sql Translation

RobustART: Benchmarking Robustness on Architecture Design and Training Techniques

1 code implementation11 Sep 2021 Shiyu Tang, Ruihao Gong, Yan Wang, Aishan Liu, Jiakai Wang, Xinyun Chen, Fengwei Yu, Xianglong Liu, Dawn Song, Alan Yuille, Philip H. S. Torr, DaCheng Tao

Thus, we propose RobustART, the first comprehensive Robustness investigation benchmark on ImageNet regarding ARchitecture design (49 human-designed off-the-shelf architectures and 1200+ networks from neural architecture search) and Training techniques (10+ techniques, e. g., data augmentation) towards diverse noises (adversarial, natural, and system noises).

Adversarial Robustness Data Augmentation +1

Latent Execution for Neural Program Synthesis

1 code implementation NeurIPS 2021 Xinyun Chen, Dawn Song, Yuandong Tian

While recent works demonstrated limited success on domain-specific languages (DSL), it remains highly challenging to apply them to real-world programming languages, such as C. Due to complicated syntax and token variation, there are three major challenges: (1) unlike many DSLs, programs in languages like C need to compile first and are not executed via interpreters; (2) the program search space grows exponentially when the syntax and semantics of the programming language become more complex; and (3) collecting a large-scale dataset of real-world programs is non-trivial.

Program Synthesis

SpreadsheetCoder: Formula Prediction from Semi-structured Context

1 code implementation26 Jun 2021 Xinyun Chen, Petros Maniatis, Rishabh Singh, Charles Sutton, Hanjun Dai, Max Lin, Denny Zhou

In this work, we present the first approach for synthesizing spreadsheet formulas from tabular context, which includes both headers and semi-structured tabular data.

Program Synthesis

Understanding the Spread of COVID-19 Epidemic: A Spatio-Temporal Point Process View

no code implementations24 Jun 2021 Shuang Li, Lu Wang, Xinyun Chen, Yixiang Fang, Yan Song

In this paper, we model the propagation of the COVID-19 as spatio-temporal point processes and propose a generative and intensity-free model to track the spread of the disease.

Imitation Learning Point Processes

Towards Robustness of Text-to-SQL Models against Synonym Substitution

1 code implementation ACL 2021 Yujian Gan, Xinyun Chen, Qiuping Huang, Matthew Purver, John R. Woodward, Jinxia Xie, Pengsheng Huang

We observe that the accuracy dramatically drops by eliminating such explicit correspondence between NL questions and table schemas, even if the synonyms are not adversarially selected to conduct worst-case adversarial attacks.

Text-To-Sql

Understanding Robustness in Teacher-Student Setting: A New Perspective

no code implementations25 Feb 2021 Zhuolin Yang, Zhaoxi Chen, Tiffany Cai, Xinyun Chen, Bo Li, Yuandong Tian

Extensive experiments show that student specialization correlates strongly with model robustness in different scenarios, including student trained via standard training, adversarial training, confidence-calibrated adversarial training, and training with robust feature dataset.

Data Augmentation

Perturbation Type Categorization for Multiple $\ell_p$ Bounded Adversarial Robustness

no code implementations1 Jan 2021 Pratyush Maini, Xinyun Chen, Bo Li, Dawn Song

In addition, we demonstrate the realization of this trade-off in deep networks by adding random noise to the model input at test time, enabling enhanced robustness against strong adaptive attacks.

Adversarial Robustness

Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses

no code implementations18 Dec 2020 Micah Goldblum, Dimitris Tsipras, Chulin Xie, Xinyun Chen, Avi Schwarzschild, Dawn Song, Aleksander Madry, Bo Li, Tom Goldstein

As machine learning systems grow in scale, so do their training data requirements, forcing practitioners to automate and outsource the curation of training data in order to achieve state-of-the-art performance.

Data Poisoning

Towards Defending Multiple Adversarial Perturbations via Gated Batch Normalization

no code implementations3 Dec 2020 Aishan Liu, Shiyu Tang, Xianglong Liu, Xinyun Chen, Lei Huang, Zhuozhuo Tu, Dawn Song, DaCheng Tao

To better understand this phenomenon, we propose the \emph{multi-domain} hypothesis, stating that different types of adversarial perturbations are drawn from different domains.

An online learning approach to dynamic pricing and capacity sizing in service systems

no code implementations7 Sep 2020 Xinyun Chen, Yunan Liu, Guiyu Hong

We study a dynamic pricing and capacity sizing problem in a GI/GI/1 queue, where the service provider's objective is to obtain the optimal service fee $p$ and service capacity $\mu$ so as to maximize cumulative expected profit (the service revenue minus the staffing cost and delay penalty).

online learning

Compositional Generalization via Neural-Symbolic Stack Machines

no code implementations NeurIPS 2020 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.

Few-Shot Learning Machine Translation +1

Synthesize, Execute and Debug: Learning to Repair for Neural Program Synthesis

1 code implementation NeurIPS 2020 Kavi Gupta, Peter Ebert Christensen, Xinyun Chen, Dawn Song

The use of deep learning techniques has achieved significant progress for program synthesis from input-output examples.

Program Synthesis

Spatiotemporal Attacks for Embodied Agents

1 code implementation ECCV 2020 Aishan Liu, Tairan Huang, Xianglong Liu, Yitao Xu, Yuqing Ma, Xinyun Chen, Stephen J. Maybank, DaCheng Tao

Adversarial attacks are valuable for providing insights into the blind-spots of deep learning models and help improve their robustness.

Deep Symbolic Superoptimization Without Human Knowledge

1 code implementation ICLR 2020 Hui Shi, Yang Zhang, Xinyun Chen, Yuandong Tian, Jishen Zhao

Deep symbolic superoptimization refers to the task of applying deep learning methods to simplify symbolic expressions.

reinforcement-learning

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

no code implementations ICLR 2020 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.

Data Augmentation Question Answering +1

Coda: An End-to-End Neural Program Decompiler

no code implementations NeurIPS 2019 Cheng Fu, Huili Chen, Haolan Liu, Xinyun Chen, Yuandong Tian, Farinaz Koushanfar, Jishen Zhao

Furthermore, Coda outperforms the sequence-to-sequence model with attention by a margin of 70% program accuracy.

Malware Detection

REFIT: A Unified Watermark Removal Framework For Deep Learning Systems With Limited Data

1 code implementation17 Nov 2019 Xinyun Chen, Wenxiao Wang, Chris Bender, Yiming Ding, Ruoxi Jia, Bo Li, Dawn Song

The experimental results demonstrate that our fine-tuning based watermark removal attacks could pose real threats to the copyright of pre-trained models, and thus highlight the importance of further investigating the watermarking problem and proposing more robust watermark embedding schemes against the attacks.

Infinite-horizon Off-Policy Policy Evaluation with Multiple Behavior Policies

no code implementations ICLR 2020 Xinyun Chen, Lu Wang, Yizhe Hang, Heng Ge, Hongyuan Zha

We consider off-policy policy evaluation when the trajectory data are generated by multiple behavior policies.

A Neural-based Program Decompiler

no code implementations28 Jun 2019 Cheng Fu, Huili Chen, Haolan Liu, Xinyun Chen, Yuandong Tian, Farinaz Koushanfar, Jishen Zhao

Reverse engineering of binary executables is a critical problem in the computer security domain.

Malware Detection

Execution-Guided Neural Program Synthesis

no code implementations ICLR 2019 Xinyun Chen, Chang Liu, Dawn Song

Most existing neural program synthesis approaches employ an encoder-decoder architecture, which uses an encoder to compute the embedding of the given input-output examples, as well as a decoder to generate the program from the embedding following a given syntax.

Program Synthesis

Tree-to-tree Neural Networks for Program Translation

no code implementations ICLR 2018 Xinyun Chen, Chang Liu, Dawn Song

We observe that program translation is a modular procedure, in which a sub-tree of the source tree is translated into the corresponding target sub-tree at each step.

Translation

Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning

no code implementations15 Dec 2017 Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, Dawn Song

In this work, we consider a new type of attacks, called backdoor attacks, where the attacker's goal is to create a backdoor into a learning-based authentication system, so that he can easily circumvent the system by leveraging the backdoor.

Data Poisoning Face Recognition

Fooling Vision and Language Models Despite Localization and Attention Mechanism

no code implementations CVPR 2018 Xiaojun Xu, Xinyun Chen, Chang Liu, Anna Rohrbach, Trevor Darrell, Dawn Song

Our work sheds new light on understanding adversarial attacks on vision systems which have a language component and shows that attention, bounding box localization, and compositional internal structures are vulnerable to adversarial attacks.

Natural Language Understanding Question Answering +2

Adversarial Example Defenses: Ensembles of Weak Defenses are not Strong

no code implementations15 Jun 2017 Warren He, James Wei, Xinyun Chen, Nicholas Carlini, Dawn Song

We ask whether a strong defense can be created by combining multiple (possibly weak) defenses.

Towards Synthesizing Complex Programs from Input-Output Examples

no code implementations ICLR 2018 Xinyun Chen, Chang Liu, Dawn Song

In our evaluation, we show that using our novel approach, neural parsing programs can be learned to achieve 100% test accuracy on test inputs that are 500x longer than the training samples.

Program Synthesis reinforcement-learning

Delving into Transferable Adversarial Examples and Black-box Attacks

1 code implementation8 Nov 2016 Yanpei Liu, Xinyun Chen, Chang Liu, Dawn Song

In this work, we are the first to conduct an extensive study of the transferability over large models and a large scale dataset, and we are also the first to study the transferability of targeted adversarial examples with their target labels.

Adversarial Attack Adversarial Defense +1

A General Retraining Framework for Scalable Adversarial Classification

no code implementations9 Apr 2016 Bo Li, Yevgeniy Vorobeychik, Xinyun Chen

We propose the first systematic and general-purpose retraining framework which can: a) boost robustness of an \emph{arbitrary} learning algorithm, in the face of b) a broader class of adversarial models than any prior methods.

Classification General Classification

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