b'Xiang Zhou'

Sublinear Regret for Learning POMDPs

We study the model-based undiscounted reinforcement learning for partial...
07/08/2021 ∙ by Yi Xiong, et al. ∙ 7 ∙ share

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Verifying Verified Code

A recent case study from AWS by Chong et al. proposes an effective metho...
07/01/2021 ∙ by Siddharth Priya, et al. ∙ 0 ∙ share

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Hidden Biases in Unreliable News Detection Datasets

Automatic unreliable news detection is a research problem with great pot...
04/20/2021 ∙ by Xiang Zhou, et al. ∙ 7 ∙ share

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Distributed NLI: Learning to Predict Human Opinion Distributions for Language Reasoning

We introduce distributed NLI, a new NLU task with a goal to predict the ...
04/18/2021 ∙ by Xiang Zhou, et al. ∙ 10 ∙ share

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Understanding Loss Landscapes of Neural Network Models in Solving Partial Differential Equations

Solving partial differential equations (PDEs) by parametrizing its solut...
03/20/2021 ∙ by Keke Wu, et al. ∙ 0 ∙ share

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Semiparametric Estimation for Causal Mediation Analysis with Multiple Causally Ordered Mediators

Causal mediation analysis concerns the pathways through which a treatmen...
11/25/2020 ∙ by Xiang Zhou, et al. ∙ 0 ∙ share

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Some Doubly and Multiply Robust Estimators of Controlled Direct Effects

This letter introduces several doubly, triply, and quadruply robust esti...
11/18/2020 ∙ by Xiang Zhou, et al. ∙ 0 ∙ share

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Projection Method for Saddle Points of Energy Functional in H^-1 Metric

Saddle points play important roles as the transition states of activated...
11/10/2020 ∙ by Shuting Gu, et al. ∙ 0 ∙ share

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What Can We Learn from Collective Human Opinions on Natural Language Inference Data?

Despite the subjective nature of many NLP tasks, most NLU evaluations ha...
10/07/2020 ∙ by Yixin Nie, et al. ∙ 4 ∙ share

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Bayesian Hierarchical Models for High-Dimensional Mediation Analysis with Coordinated Selection of Correlated Mediators

We consider Bayesian high-dimensional mediation analysis to identify amo...
09/23/2020 ∙ by Yanyi Song, et al. ∙ 0 ∙ share

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Deep Reinforcement Learning for On-line Dialogue State Tracking

Dialogue state tracking (DST) is a crucial module in dialogue management...
09/22/2020 ∙ by Zhi Chen, et al. ∙ 0 ∙ share

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Filling the Gap of Utterance-aware and Speaker-aware Representation for Multi-turn Dialogue

A multi-turn dialogue is composed of multiple utterances from two or mor...
09/14/2020 ∙ by Longxiang Liu, et al. ∙ 4 ∙ share

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Composing Answer from Multi-spans for Reading Comprehension

This paper presents a novel method to generate answers for non-extractio...
09/14/2020 ∙ by Zhuosheng Zhang, et al. ∙ 8 ∙ share

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Task-specific Objectives of Pre-trained Language Models for Dialogue Adaptation

Pre-trained Language Models (PrLMs) have been widely used as backbones i...
09/10/2020 ∙ by Junlong Li, et al. ∙ 0 ∙ share

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Bayesian Sparse Mediation Analysis with Targeted Penalization of Natural Indirect Effects

Causal mediation analysis aims to characterize an exposure's effect on a...
08/14/2020 ∙ by Yanyi Song, et al. ∙ 0 ∙ share

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Machine Learning and Control Theory

We survey in this article the connections between Machine Learning and C...
06/10/2020 ∙ by Alain Bensoussan, et al. ∙ 0 ∙ share

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Towards Robustifying NLI Models Against Lexical Dataset Biases

While deep learning models are making fast progress on the task of Natur...
05/10/2020 ∙ by Xiang Zhou, et al. ∙ 0 ∙ share

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The Curse of Performance Instability in Analysis Datasets: Consequences, Source, and Suggestions

We find that the performance of state-of-the-art models on Natural Langu...
04/28/2020 ∙ by Xiang Zhou, et al. ∙ 0 ∙ share

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Enumerating Chemical Graphs with Two Disjoint Cycles Satisfying Given Path Frequency Specifications

Enumerating chemical graphs satisfying given constraints is a fundamenta...
04/18/2020 ∙ by Kyousuke Yamashita, et al. ∙ 0 ∙ share

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Explicit Estimation of Derivatives from Data and Differential Equations by Gaussian Process Regression

In this work, we employ the Bayesian inference framework to solve the pr...
04/13/2020 ∙ by Hongqiao Wang, et al. ∙ 0 ∙ share

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Stochastic Modified Equations for Continuous Limit of Stochastic ADMM

Stochastic version of alternating direction method of multiplier (ADMM) ...
03/07/2020 ∙ by Xiang Zhou, et al. ∙ 0 ∙ share

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Regime Switching Bandits

We study a multi-armed bandit problem where the rewards exhibit regime-s...
01/26/2020 ∙ by Xiang Zhou, et al. ∙ 6 ∙ share

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Fine-grained Attention and Feature-sharing Generative Adversarial Networks for Single Image Super-Resolution

The traditional super-resolution methods that aim to minimize the mean s...
11/25/2019 ∙ by Yitong Yan, et al. ∙ 20 ∙ share

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Semantics-aware BERT for Language Understanding

The latest work on language representations carefully integrates context...
09/05/2019 ∙ by Zhuosheng Zhang, et al. ∙ 0 ∙ share

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DCMN+: Dual Co-Matching Network for Multi-choice Reading Comprehension

Multi-choice reading comprehension is a challenging task to select an an...
08/30/2019 ∙ by Shuailiang Zhang, et al. ∙ 0 ∙ share

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Dual Co-Matching Network for Multi-choice Reading Comprehension

Multi-choice reading comprehension is a challenging task that requires c...
01/27/2019 ∙ by Shuailiang Zhang, et al. ∙ 0 ∙ share

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Dependency or Span, End-to-End Uniform Semantic Role Labeling

Semantic role labeling (SRL) aims to discover the predicateargument stru...
01/16/2019 ∙ by Zuchao Li, et al. ∙ 0 ∙ share

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I Know What You Want: Semantic Learning for Text Comprehension

Who did what to whom is a major focus in natural language understanding,...
09/08/2018 ∙ by Zhuosheng Zhang, et al. ∙ 0 ∙ share

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Regression-with-residuals Estimation of Marginal Effects: A Method of Adjusting for Treatment-induced Confounders that may also be Moderators

Treatment-induced confounders complicate analyses of time-varying treatm...
08/23/2018 ∙ by Geoffrey T. Wodtke, et al. ∙ 0 ∙ share

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Residual Balancing Weights for Marginal Structural Models: with Application to Analyses of Time-varying Treatments and Causal Mediation

Treatment-induced confounding arises in analyses of time-varying treatme...
07/28/2018 ∙ by Xiang Zhou, et al. ∙ 0 ∙ share

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Scalable Algorithms for Learning High-Dimensional Linear Mixed Models

Linear mixed models (LMMs) are used extensively to model dependecies of ...
03/12/2018 ∙ by Zilong Tan, et al. ∙ 0 ∙ share

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varbvs: Fast Variable Selection for Large-scale Regression

We introduce varbvs, a suite of functions written in R and MATLAB for re...
09/19/2017 ∙ by Peter Carbonetto, et al. ∙ 0 ∙ share

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Bayesian Approximate Kernel Regression with Variable Selection

Nonlinear kernel regression models are often used in statistics and mach...
08/05/2015 ∙ by Lorin Crawford, et al. ∙ 0 ∙ share

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Xiang Zhou

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