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no code implementations • 3 Dec 2021 • Xinwei Du, Kailun Dong, Yuchen Zhang, Yongsheng Li, Ruei-Yu Tsay

Long document summarization is an important and hard task in the field of natural language processing.

no code implementations • 27 Oct 2021 • Zhongyao Hu, Bo Chen, Yuchen Zhang, Li Yu

When considering linear dynamic systems, a conservative estimation error covariance with adjustable parameters is constructed by matrix inequality, and then an optimal filter gain is derived by minimizing its trace.

no code implementations • 29 Sep 2021 • Chieko Sarah Imai, Minghao Zhang, Yuchen Zhang, Marcin Kierebinski, Ruihan Yang, Yuzhe Qin, Xiaolong Wang

While Reinforcement Learning (RL) provides a promising paradigm for agile locomotion skills with vision inputs in simulation, it is still very challenging to deploy the RL policy in the real world.

no code implementations • 27 Aug 2021 • Zhen Huang, Xiaodan Zhuang, Daben Liu, Xiaoqiang Xiao, Yuchen Zhang, Sabato Marco Siniscalchi

To achieve such an ambitious goal, new mechanisms for foreign pronunciation generation and language model (LM) enrichment have been devised.

no code implementations • ACL 2021 • Emmanouil Antonios Platanios, Adam Pauls, Subhro Roy, Yuchen Zhang, Alexander Kyte, Alan Guo, Sam Thomson, Jayant Krishnamurthy, Jason Wolfe, Jacob Andreas, Dan Klein

Conversational semantic parsers map user utterances to executable programs given dialogue histories composed of previous utterances, programs, and system responses.

no code implementations • 7 Feb 2021 • Bo Yang, Hengwei Zhang, Yuchen Zhang, Kaiyong Xu, Jindong Wang

ABI-FGM and CIM can be readily integrated to build a strong gradient-based attack to further boost the success rates of adversarial examples for black-box attacks.

no code implementations • 24 Sep 2020 • Semantic Machines, Jacob Andreas, John Bufe, David Burkett, Charles Chen, Josh Clausman, Jean Crawford, Kate Crim, Jordan DeLoach, Leah Dorner, Jason Eisner, Hao Fang, Alan Guo, David Hall, Kristin Hayes, Kellie Hill, Diana Ho, Wendy Iwaszuk, Smriti Jha, Dan Klein, Jayant Krishnamurthy, Theo Lanman, Percy Liang, Christopher H Lin, Ilya Lintsbakh, Andy McGovern, Aleksandr Nisnevich, Adam Pauls, Dmitrij Petters, Brent Read, Dan Roth, Subhro Roy, Jesse Rusak, Beth Short, Div Slomin, Ben Snyder, Stephon Striplin, Yu Su, Zachary Tellman, Sam Thomson, Andrei Vorobev, Izabela Witoszko, Jason Wolfe, Abby Wray, Yuchen Zhang, Alexander Zotov

We describe an approach to task-oriented dialogue in which dialogue state is represented as a dataflow graph.

no code implementations • 23 Sep 2020 • Zhijun Zhao, Haijing Zhou, Yuchen Zhang, Yun Ling, Fangyu Xu

In order to evaluate the ground-based infrared telescope sensitivity affected by the noise from the atmosphere, instruments and detectors, we construct a sensitivity model that can calculate limiting magnitudes and signal-to-noise ratio ($S/N$).

Instrumentation and Methods for Astrophysics

no code implementations • 14 Aug 2020 • Yuchen Zhang, Mingsheng Long, Jian-Min Wang, Michael. I. Jordan

Finally, we further extend the localized discrepancies for achieving super transfer and derive generalization bounds that could be even more sample-efficient on source domain.

no code implementations • 27 Jul 2019 • Zihan Jiang, Wanling Gao, Lei Wang, Xingwang Xiong, Yuchen Zhang, Xu Wen, Chunjie Luo, Hainan Ye, Yunquan Zhang, Shengzhong Feng, Kenli Li, Weijia Xu, Jianfeng Zhan

In this paper, we propose HPC AI500 --- a benchmark suite for evaluating HPC systems that running scientific DL workloads.

no code implementations • SEMEVAL 2019 • Yuchen Zhang, Nianwen Xue

Temporal Dependency Trees are a structured temporal representation that represents temporal relations among time expressions and events in a text as a dependency tree structure.

4 code implementations • 11 Apr 2019 • Yuchen Zhang, Tianle Liu, Mingsheng Long, Michael. I. Jordan

We introduce Margin Disparity Discrepancy, a novel measurement with rigorous generalization bounds, tailored to the distribution comparison with the asymmetric margin loss, and to the minimax optimization for easier training.

1 code implementation • 25 Mar 2019 • Yuchen Zhang, Percy Liang

Adversarial perturbations dramatically decrease the accuracy of state-of-the-art image classifiers.

2 code implementations • EMNLP 2018 • Yuchen Zhang, Nianwen Xue

In a parsing-only evaluation setup where gold time expressions and events are provided, our parser reaches 0. 81 and 0. 70 f-score on unlabeled and labeled parsing respectively, a result that is very competitive against alternative approaches.

2 code implementations • LREC 2018 • Yuchen Zhang, Nianwen Xue

Temporal relations between events and time expressions in a document are often modeled in an unstructured manner where relations between individual pairs of time expressions and events are considered in isolation.

2 code implementations • EMNLP 2017 • Yuchen Zhang, Panupong Pasupat, Percy Liang

To learn a semantic parser from denotations, a learning algorithm must search over a combinatorially large space of logical forms for ones consistent with the annotated denotations.

no code implementations • 18 Feb 2017 • Yuchen Zhang, Percy Liang, Moses Charikar

We study the Stochastic Gradient Langevin Dynamics (SGLD) algorithm for non-convex optimization.

1 code implementation • ICML 2017 • Yuchen Zhang, Percy Liang, Martin J. Wainwright

For learning two-layer convolutional neural networks, we prove that the generalization error obtained by a convexified CNN converges to that of the best possible CNN.

no code implementations • NeurIPS 2016 • Chi Jin, Yuchen Zhang, Sivaraman Balakrishnan, Martin J. Wainwright, Michael Jordan

Our first main result shows that the population likelihood function has bad local maxima even in the special case of equally-weighted mixtures of well-separated and spherical Gaussians.

no code implementations • 25 Nov 2015 • Yuchen Zhang, Jason D. Lee, Martin J. Wainwright, Michael. I. Jordan

For loss functions that are $L$-Lipschitz continuous, we present algorithms to learn halfspaces and multi-layer neural networks that achieve arbitrarily small excess risk $\epsilon>0$.

no code implementations • 13 Oct 2015 • Yuchen Zhang, Jason D. Lee, Michael. I. Jordan

The sample complexity and the time complexity of the presented method are polynomial in the input dimension and in $(1/\epsilon,\log(1/\delta), F(k, L))$, where $F(k, L)$ is a function depending on $(k, L)$ and on the activation function, independent of the number of neurons.

no code implementations • 24 Jun 2015 • Yuchen Zhang, Michael. I. Jordan

Splash consists of a programming interface and an execution engine.

no code implementations • 11 Mar 2015 • Yuchen Zhang, Martin J. Wainwright, Michael. I. Jordan

In this paper, we show that the slow rate is intrinsic to a broad class of M-estimators.

no code implementations • 5 Feb 2015 • Yuchen Zhang, Martin J. Wainwright, Michael. I. Jordan

We study the following generalized matrix rank estimation problem: given an $n \times n$ matrix and a constant $c \geq 0$, estimate the number of eigenvalues that are greater than $c$.

no code implementations • 1 Jan 2015 • Yuchen Zhang, Lin Xiao

We consider distributed convex optimization problems originated from sample average approximation of stochastic optimization, or empirical risk minimization in machine learning.

no code implementations • 10 Sep 2014 • Yuchen Zhang, Lin Xiao

We consider a generic convex optimization problem associated with regularized empirical risk minimization of linear predictors.

no code implementations • NeurIPS 2014 • Yuchen Zhang, Xi Chen, Dengyong Zhou, Michael. I. Jordan

Crowdsourcing is a popular paradigm for effectively collecting labels at low cost.

no code implementations • 5 May 2014 • John C. Duchi, Michael. I. Jordan, Martin J. Wainwright, Yuchen Zhang

Large data sets often require performing distributed statistical estimation, with a full data set split across multiple machines and limited communication between machines.

no code implementations • LREC 2014 • Nianwen Xue, Yuchen Zhang

We describe a {``}distant annotation{''} method where we mark up the semantic tense, event type, and modality of Chinese events via a word-aligned parallel corpus.

no code implementations • NeurIPS 2013 • Yuchen Zhang, John Duchi, Michael. I. Jordan, Martin J. Wainwright

We establish minimax risk lower bounds for distributed statistical estimation given a budget $B$ of the total number of bits that may be communicated.

no code implementations • 22 May 2013 • Yuchen Zhang, John C. Duchi, Martin J. Wainwright

We establish optimal convergence rates for a decomposition-based scalable approach to kernel ridge regression.

no code implementations • NeurIPS 2012 • Yuchen Zhang, Martin J. Wainwright, John C. Duchi

The first algorithm is an averaging method that distributes the $N$ data samples evenly to $m$ machines, performs separate minimization on each subset, and then averages the estimates.

no code implementations • 19 Sep 2012 • Yuchen Zhang, John C. Duchi, Martin Wainwright

We analyze two communication-efficient algorithms for distributed statistical optimization on large-scale data sets.

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