Defending against Whitebox Adversarial Attacks via Randomized Discretization

Mar 25, 2019

Yuchen Zhang, Percy Liang

Mar 25, 2019

Yuchen Zhang, Percy Liang

* In proceedings of the 22nd International Conference on Artificial Intelligence and Statistics

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A Non-asymptotic, Sharp, and User-friendly Reverse Chernoff-Cramèr Bound

Oct 21, 2018

Anru Zhang, Yuchen Zhou

The Chernoff-Cram\`er bound is a widely used technique to analyze the upper tail bound of random variable based on its moment generating function. By elementary proofs, we develop a user-friendly reverse Chernoff-Cram\`er bound that yields non-asymptotic lower tail bounds for generic random variables. The new reverse Chernoff-Cram\`er bound is used to derive a series of results, including the sharp lower tail bounds for the sum of independent sub-Gaussian and sub-exponential random variables, which matches the classic Hoefflding-type and Bernstein-type concentration inequalities, respectively. We also provide non-asymptotic matching upper and lower tail bounds for a suite of distributions, including gamma, beta, (regular, weighted, and noncentral) chi-squared, binomial, Poisson, Irwin-Hall, etc. We apply the result to develop matching upper and lower bounds for extreme value expectation of the sum of independent sub-Gaussian and sub-exponential random variables. A statistical application of sparse signal identification is finally studied.
Oct 21, 2018

Anru Zhang, Yuchen Zhou

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Neural Ranking Models for Temporal Dependency Structure Parsing

Sep 02, 2018

Yuchen Zhang, Nianwen Xue

Sep 02, 2018

Yuchen Zhang, Nianwen Xue

* 11 pages, 2 figures, 7 tables, to appear at EMNLP 2018, Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2018

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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. This often results in inconsistent and incomplete annotation and computational modeling. We propose a novel annotation approach where events and time expressions in a document form a dependency tree in which each dependency relation corresponds to an instance of temporal anaphora where the antecedent is the parent and the anaphor is the child. We annotate a corpus of 235 documents using this approach in the two genres of news and narratives, with 48 documents doubly annotated. We report a stable and high inter-annotator agreement on the doubly annotated subset, validating our approach, and perform a quantitative comparison between the two genres of the entire corpus. We make this corpus publicly available.

* Yuchen Zhang and Nianwen Xue. 2018. Structured Interpretation of Temporal Relations. In Proceedings of the 11th Language Resources and Evaluation Conference (LREC-2018), Miyazaki, Japan

* 9 pages, 2 figures, 8 tables, LREC-2018

* Yuchen Zhang and Nianwen Xue. 2018. Structured Interpretation of Temporal Relations. In Proceedings of the 11th Language Resources and Evaluation Conference (LREC-2018), Miyazaki, Japan

* 9 pages, 2 figures, 8 tables, LREC-2018

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Splash: User-friendly Programming Interface for Parallelizing Stochastic Algorithms

Sep 23, 2015

Yuchen Zhang, Michael I. Jordan

Sep 23, 2015

Yuchen Zhang, Michael I. Jordan

* redo experiments to learn bigger models; compare Splash with state-of-the-art implementations on Spark

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Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization

Sep 09, 2015

Yuchen Zhang, Lin Xiao

Sep 09, 2015

Yuchen Zhang, Lin Xiao

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Communication-Efficient Distributed Optimization of Self-Concordant Empirical Loss

Jan 01, 2015

Yuchen Zhang, Lin Xiao

Jan 01, 2015

Yuchen Zhang, Lin Xiao

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A Hitting Time Analysis of Stochastic Gradient Langevin Dynamics

Apr 09, 2018

Yuchen Zhang, Percy Liang, Moses Charikar

Apr 09, 2018

Yuchen Zhang, Percy Liang, Moses Charikar

* Correct two mistakes in the proofs of Lemma 3 and Lemma 5

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Macro Grammars and Holistic Triggering for Efficient Semantic Parsing

Aug 31, 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. We propose a new online learning algorithm that searches faster as training progresses. The two key ideas are using macro grammars to cache the abstract patterns of useful logical forms found thus far, and holistic triggering to efficiently retrieve the most relevant patterns based on sentence similarity. On the WikiTableQuestions dataset, we first expand the search space of an existing model to improve the state-of-the-art accuracy from 38.7% to 42.7%, and then use macro grammars and holistic triggering to achieve an 11x speedup and an accuracy of 43.7%.
Aug 31, 2017

Yuchen Zhang, Panupong Pasupat, Percy Liang

* EMNLP 2017

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Convexified Convolutional Neural Networks

Sep 04, 2016

Yuchen Zhang, Percy Liang, Martin J. Wainwright

Sep 04, 2016

Yuchen Zhang, Percy Liang, Martin J. Wainwright

* 29 pages

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Optimal prediction for sparse linear models? Lower bounds for coordinate-separable M-estimators

Nov 30, 2015

Yuchen Zhang, Martin J. Wainwright, Michael I. Jordan

Nov 30, 2015

Yuchen Zhang, Martin J. Wainwright, Michael I. Jordan

* Add more coverage on related work; add a new lower bound for design matrices satisfying the restricted eigenvalue condition

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$\ell_1$-regularized Neural Networks are Improperly Learnable in Polynomial Time

Oct 13, 2015

Yuchen Zhang, Jason D. Lee, Michael I. Jordan

Oct 13, 2015

Yuchen Zhang, Jason D. Lee, Michael I. Jordan

* 16 pages

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Distributed Estimation of Generalized Matrix Rank: Efficient Algorithms and Lower Bounds

Feb 06, 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 $c$. In the distributed setting, the matrix of interest is the sum of $m$ matrices held by separate machines. We show that any deterministic algorithm solving this problem must communicate $\Omega(n^2)$ bits, which is order-equivalent to transmitting the whole matrix. In contrast, we propose a randomized algorithm that communicates only $\widetilde O(n)$ bits. The upper bound is matched by an $\Omega(n)$ lower bound on the randomized communication complexity. We demonstrate the practical effectiveness of the proposed algorithm with some numerical experiments.
Feb 06, 2015

Yuchen Zhang, Martin J. Wainwright, Michael I. Jordan

* 23 pages, 5 figures

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Comunication-Efficient Algorithms for Statistical Optimization

Oct 11, 2013

Yuchen Zhang, John C. Duchi, Martin Wainwright

Oct 11, 2013

Yuchen Zhang, John C. Duchi, Martin Wainwright

* 44 pages, to appear in Journal of Machine Learning Research (JMLR)

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Bridging Theory and Algorithm for Domain Adaptation

Apr 11, 2019

Yuchen Zhang, Tianle Liu, Mingsheng Long, Michael I. Jordan

This paper addresses the problem of unsupervised domain adaption from theoretical and algorithmic perspectives. Existing domain adaptation theories naturally imply minimax optimization algorithms, which connect well with the adversarial-learning based domain adaptation methods. However, several disconnections still form the gap between theory and algorithm. We extend previous theories (Ben-David et al., 2010; Mansour et al., 2009c) to multiclass classification in domain adaptation, where classifiers based on scoring functions and margin loss are standard algorithmic choices. We introduce a novel measurement, margin disparity discrepancy, that is tailored both to distribution comparison with asymmetric margin loss, and to minimax optimization for easier training. Using this discrepancy, we derive new generalization bounds in terms of Rademacher complexity. Our theory can be seamlessly transformed into an adversarial learning algorithm for domain adaptation, successfully bridging the gap between theory and algorithm. A series of empirical studies show that our algorithm achieves the state-of-the-art accuracies on challenging domain adaptation tasks.
Apr 11, 2019

Yuchen Zhang, Tianle Liu, Mingsheng Long, Michael I. Jordan

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Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing

Nov 01, 2014

Yuchen Zhang, Xi Chen, Dengyong Zhou, Michael I. Jordan

Nov 01, 2014

Yuchen Zhang, Xi Chen, Dengyong Zhou, Michael I. Jordan

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Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with Minimax Optimal Rates

Apr 29, 2014

Yuchen Zhang, John C. Duchi, Martin J. Wainwright

Apr 29, 2014

Yuchen Zhang, John C. Duchi, Martin J. Wainwright

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Language-Independent Representor for Neural Machine Translation

Nov 01, 2018

Long Zhou, Yuchen Liu, Jiajun Zhang, Chengqing Zong, Guoping Huang

Nov 01, 2018

Long Zhou, Yuchen Liu, Jiajun Zhang, Chengqing Zong, Guoping Huang

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Learning Halfspaces and Neural Networks with Random Initialization

Nov 25, 2015

Yuchen Zhang, Jason D. Lee, Martin J. Wainwright, Michael I. Jordan

Nov 25, 2015

Yuchen Zhang, Jason D. Lee, Martin J. Wainwright, Michael I. Jordan

* 31 pages

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Optimality guarantees for distributed statistical estimation

Jun 21, 2014

John C. Duchi, Michael I. Jordan, Martin J. Wainwright, Yuchen Zhang

Jun 21, 2014

John C. Duchi, Michael I. Jordan, Martin J. Wainwright, Yuchen Zhang

* 34 pages, 1 figure. Preliminary version appearing in Neural Information Processing Systems 2013 (http://papers.nips.cc/paper/4902-information-theoretic-lower-bounds-for-distributed-statistical-estimation-with-communication-constraints)

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