Search Results for author: Yining Wang

Found 57 papers, 4 papers with code

A Knowledge-driven Generative Model for Multi-implication Chinese Medical Procedure Entity Normalization

no code implementations EMNLP 2020 Jinghui Yan, Yining Wang, Lu Xiang, Yu Zhou, Chengqing Zong

Medical entity normalization, which links medical mentions in the text to entities in knowledge bases, is an important research topic in medical natural language processing.

Medical Procedure

Differential Privacy in Personalized Pricing with Nonparametric Demand Models

no code implementations10 Sep 2021 Xi Chen, Sentao Miao, Yining Wang

In the recent decades, the advance of information technology and abundant personal data facilitate the application of algorithmic personalized pricing.

Adversarial Attack

Meta-Reinforcement Learning for Reliable Communication in THz/VLC Wireless VR Networks

1 code implementation29 Jan 2021 Yining Wang, Mingzhe Chen, Zhaohui Yang, Walid Saad, Tao Luo, Shuguang Cui, H. Vincent Poor

To control the energy consumption of the studied THz/VLC wireless VR network, VLC access points (VAPs) must be selectively turned on so as to ensure accurate and extensive positioning for VR users.

Meta Reinforcement Learning Virtual Reality

Adversarial Combinatorial Bandits with General Non-linear Reward Functions

no code implementations5 Jan 2021 Xi Chen, Yanjun Han, Yining Wang

{The adversarial combinatorial bandit with general non-linear reward is an important open problem in bandit literature, and it is still unclear whether there is a significant gap from the case of linear reward, stochastic bandit, or semi-bandit feedback.}

Smooth Bandit Optimization: Generalization to Hölder Space

no code implementations11 Dec 2020 Yusha Liu, Yining Wang, Aarti Singh

We also study adaptation to unknown function smoothness over a continuous scale of H\"older spaces indexed by $\alpha$, with a bandit model selection approach applied with our proposed two-layer algorithms.

Model Selection

Privacy-Preserving Dynamic Personalized Pricing with Demand Learning

no code implementations27 Sep 2020 Xi Chen, David Simchi-Levi, Yining Wang

In this paper, we consider a dynamic pricing problem over $T$ time periods with an \emph{unknown} demand function of posted price and personalized information.

Constant Regret Re-solving Heuristics for Price-based Revenue Management

no code implementations7 Sep 2020 Yining Wang, He Wang

First, we prove that a natural re-solving heuristic attains $O(1)$ regret compared to the value of the optimal policy.

Uncertainty Quantification for Demand Prediction in Contextual Dynamic Pricing

no code implementations16 Mar 2020 Yining Wang, Xi Chen, Xiangyu Chang, Dongdong Ge

In this paper, using the problem of demand function prediction in dynamic pricing as the motivating example, we study the problem of constructing accurate confidence intervals for the demand function.

Optimism in Reinforcement Learning with Generalized Linear Function Approximation

no code implementations ICLR 2021 Yining Wang, Ruosong Wang, Simon S. Du, Akshay Krishnamurthy

We design a new provably efficient algorithm for episodic reinforcement learning with generalized linear function approximation.

Deep Learning for Optimal Deployment of UAVs with Visible Light Communications

no code implementations28 Nov 2019 Yining Wang, Mingzhe Chen, Zhaohui Yang, Tao Luo, Walid Saad

Using GRUs and CNNs, the UAVs can model the long-term historical illumination distribution and predict the future illumination distribution.

Synchronously Generating Two Languages with Interactive Decoding

no code implementations IJCNLP 2019 Yining Wang, Jiajun Zhang, Long Zhou, Yuchen Liu, Cheng-qing Zong

In this paper, we introduce a novel interactive approach to translate a source language into two different languages simultaneously and interactively.

Machine Translation Translation

Robust Dynamic Assortment Optimization in the Presence of Outlier Customers

no code implementations9 Oct 2019 Xi Chen, Akshay Krishnamurthy, Yining Wang

The main question investigated in this paper is model mis-specification under the $\varepsilon$-contamination model, which is a fundamental model in robust statistics and machine learning.

Gated Recurrent Units Learning for Optimal Deployment of Visible Light Communications Enabled UAVs

no code implementations17 Sep 2019 Yining Wang, Mingzhe Chen, Zhaohui Yang, Xue Hao, Tao Luo, Walid Saad

This problem is formulated as an optimization problem whose goal is to minimize the total transmit power while meeting the illumination and communication requirements of users.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank

no code implementations5 Sep 2019 Kefan Dong, Jian Peng, Yining Wang, Yuan Zhou

Our learning algorithm, Adaptive Value-function Elimination (AVE), is inspired by the policy elimination algorithm proposed in (Jiang et al., 2017), known as OLIVE.

Efficient Exploration

NCLS: Neural Cross-Lingual Summarization

1 code implementation IJCNLP 2019 Junnan Zhu, Qian Wang, Yining Wang, Yu Zhou, Jiajun Zhang, Shaonan Wang, Cheng-qing Zong

Moreover, we propose to further improve NCLS by incorporating two related tasks, monolingual summarization and machine translation, into the training process of CLS under multi-task learning.

Machine Translation Multi-Task Learning +1

Tight Regret Bounds for Infinite-armed Linear Contextual Bandits

no code implementations4 May 2019 Yingkai Li, Yining Wang, Xi Chen, Yuan Zhou

Linear contextual bandit is an important class of sequential decision making problems with a wide range of applications to recommender systems, online advertising, healthcare, and many other machine learning related tasks.

Decision Making Multi-Armed Bandits +1

Near-Optimal Policies for Dynamic Multinomial Logit Assortment Selection Models

no code implementations NeurIPS 2018 Yining Wang, Xi Chen, Yuan Zhou

In this paper we consider the dynamic assortment selection problem under an uncapacitated multinomial-logit (MNL) model.

How Many Samples are Needed to Estimate a Convolutional Neural Network?

no code implementations NeurIPS 2018 Simon S. Du, Yining Wang, Xiyu Zhai, Sivaraman Balakrishnan, Ruslan R. Salakhutdinov, Aarti Singh

We show that for an $m$-dimensional convolutional filter with linear activation acting on a $d$-dimensional input, the sample complexity of achieving population prediction error of $\epsilon$ is $\widetilde{O(m/\epsilon^2)$, whereas the sample-complexity for its FNN counterpart is lower bounded by $\Omega(d/\epsilon^2)$ samples.

Dynamic Assortment Optimization with Changing Contextual Information

no code implementations31 Oct 2018 Xi Chen, Yining Wang, Yuan Zhou

To this end, we develop an upper confidence bound (UCB) based policy and establish the regret bound on the order of $\widetilde O(d\sqrt{T})$, where $d$ is the dimension of the feature and $\widetilde O$ suppresses logarithmic dependence.

Combinatorial Optimization

Efficient Load Sampling for Worst-Case Structural Analysis Under Force Location Uncertainty

no code implementations25 Oct 2018 Yining Wang, Erva Ulu, Aarti Singh, Levent Burak Kara

Our approach uses a computationally tractable experimental design method to select number of sample force locations based on geometry only, without inspecting the stress response that requires computationally expensive finite-element analysis.

Experimental Design

Three Strategies to Improve One-to-Many Multilingual Translation

no code implementations EMNLP 2018 Yining Wang, Jiajun Zhang, FeiFei Zhai, Jingfang Xu, Cheng-qing Zong

However, previous studies show that one-to-many translation based on this framework cannot perform on par with the individually trained models.

Machine Translation Multi-Task Learning +1

Dynamic Assortment Selection under the Nested Logit Models

no code implementations27 Jun 2018 Xi Chen, Chao Shi, Yining Wang, Yuan Zhou

One key challenge is that utilities of products are unknown to the seller and need to be learned.

Robust Nonparametric Regression under Huber's $ε$-contamination Model

no code implementations26 May 2018 Simon S. Du, Yining Wang, Sivaraman Balakrishnan, Pradeep Ravikumar, Aarti Singh

We first show that a simple local binning median step can effectively remove the adversary noise and this median estimator is minimax optimal up to absolute constants over the H\"{o}lder function class with smoothness parameters smaller than or equal to 1.

Phrase Table as Recommendation Memory for Neural Machine Translation

no code implementations25 May 2018 Yang Zhao, Yining Wang, Jiajun Zhang, Cheng-qing Zong

Neural Machine Translation (NMT) has drawn much attention due to its promising translation performance recently.

Machine Translation Translation

How Many Samples are Needed to Estimate a Convolutional or Recurrent Neural Network?

no code implementations NeurIPS 2018 Simon S. Du, Yining Wang, Xiyu Zhai, Sivaraman Balakrishnan, Ruslan Salakhutdinov, Aarti Singh

It is widely believed that the practical success of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) owes to the fact that CNNs and RNNs use a more compact parametric representation than their Fully-Connected Neural Network (FNN) counterparts, and consequently require fewer training examples to accurately estimate their parameters.

Optimization of Smooth Functions with Noisy Observations: Local Minimax Rates

no code implementations NeurIPS 2018 Yining Wang, Sivaraman Balakrishnan, Aarti Singh

In this setup, an algorithm is allowed to adaptively query the underlying function at different locations and receives noisy evaluations of function values at the queried points (i. e. the algorithm has access to zeroth-order information).

Global Optimization

Near-Linear Time Local Polynomial Nonparametric Estimation with Box Kernels

no code implementations26 Feb 2018 Yining Wang, Yi Wu, Simon S. Du

Local polynomial regression (Fan and Gijbels 1996) is an important class of methods for nonparametric density estimation and regression problems.

Density Estimation

Direct Learning to Rank and Rerank

no code implementations21 Feb 2018 Cynthia Rudin, Yining Wang

Learning-to-rank techniques have proven to be extremely useful for prioritization problems, where we rank items in order of their estimated probabilities, and dedicate our limited resources to the top-ranked items.


Near-Optimal Discrete Optimization for Experimental Design: A Regret Minimization Approach

no code implementations14 Nov 2017 Zeyuan Allen-Zhu, Yuanzhi Li, Aarti Singh, Yining Wang

The experimental design problem concerns the selection of k points from a potentially large design pool of p-dimensional vectors, so as to maximize the statistical efficiency regressed on the selected k design points.

Experimental Design

Word, Subword or Character? An Empirical Study of Granularity in Chinese-English NMT

1 code implementation13 Nov 2017 Yining Wang, Long Zhou, Jiajun Zhang, Cheng-qing Zong

Our experiments show that subword model performs best for Chinese-to-English translation with the vocabulary which is not so big while hybrid word-character model is most suitable for English-to-Chinese translation.

Machine Translation Translation

Towards Neural Machine Translation with Partially Aligned Corpora

no code implementations IJCNLP 2017 Yining Wang, Yang Zhao, Jiajun Zhang, Cheng-qing Zong, Zhengshan Xue

While neural machine translation (NMT) has become the new paradigm, the parameter optimization requires large-scale parallel data which is scarce in many domains and language pairs.

Machine Translation Translation

Convergence Rates of Latent Topic Models Under Relaxed Identifiability Conditions

no code implementations30 Oct 2017 Yining Wang

In this paper we study the frequentist convergence rate for the Latent Dirichlet Allocation (Blei et al., 2003) topic models.

Topic Models

Stochastic Zeroth-order Optimization in High Dimensions

no code implementations29 Oct 2017 Yining Wang, Simon Du, Sivaraman Balakrishnan, Aarti Singh

We consider the problem of optimizing a high-dimensional convex function using stochastic zeroth-order queries.

Feature Selection

A Note on a Tight Lower Bound for MNL-Bandit Assortment Selection Models

no code implementations18 Sep 2017 Xi Chen, Yining Wang

In this short note we consider a dynamic assortment planning problem under the capacitated multinomial logit (MNL) bandit model.

Non-stationary Stochastic Optimization under $L_{p,q}$-Variation Measures

no code implementations9 Aug 2017 Xi Chen, Yining Wang, Yu-Xiang Wang

We consider a non-stationary sequential stochastic optimization problem, in which the underlying cost functions change over time under a variation budget constraint.

Stochastic Optimization

Near-Optimal Design of Experiments via Regret Minimization

no code implementations ICML 2017 Zeyuan Allen-Zhu, Yuanzhi Li, Aarti Singh, Yining Wang

We consider computationally tractable methods for the experimental design problem, where k out of n design points of dimension p are selected so that certain optimality criteria are approximately satisfied.

Experimental Design

Sequence Modeling via Segmentations

2 code implementations ICML 2017 Chong Wang, Yining Wang, Po-Sen Huang, Abdel-rahman Mohamed, Dengyong Zhou, Li Deng

The probability of a segmented sequence is calculated as the product of the probabilities of all its segments, where each segment is modeled using existing tools such as recurrent neural networks.

Speech Recognition Text Segmentation

On the Power of Truncated SVD for General High-rank Matrix Estimation Problems

no code implementations NeurIPS 2017 Simon S. Du, Yining Wang, Aarti Singh

This observation leads to many interesting results on general high-rank matrix estimation problems, which we briefly summarize below ($A$ is an $n\times n$ high-rank PSD matrix and $A_k$ is the best rank-$k$ approximation of $A$): (1) High-rank matrix completion: By observing $\Omega(\frac{n\max\{\epsilon^{-4}, k^2\}\mu_0^2\|A\|_F^2\log n}{\sigma_{k+1}(A)^2})$ elements of $A$ where $\sigma_{k+1}\left(A\right)$ is the $\left(k+1\right)$-th singular value of $A$ and $\mu_0$ is the incoherence, the truncated SVD on a zero-filled matrix satisfies $\|\widehat{A}_k-A\|_F \leq (1+O(\epsilon))\|A-A_k\|_F$ with high probability.

Matrix Completion

Rate Optimal Estimation and Confidence Intervals for High-dimensional Regression with Missing Covariates

no code implementations9 Feb 2017 Yining Wang, Jialei Wang, Sivaraman Balakrishnan, Aarti Singh

We consider the problems of estimation and of constructing component-wise confidence intervals in a sparse high-dimensional linear regression model when some covariates of the design matrix are missing completely at random.

A Theoretical Analysis of Noisy Sparse Subspace Clustering on Dimensionality-Reduced Data

no code implementations24 Oct 2016 Yining Wang, Yu-Xiang Wang, Aarti Singh

Subspace clustering is the problem of partitioning unlabeled data points into a number of clusters so that data points within one cluster lie approximately on a low-dimensional linear subspace.

Dimensionality Reduction

Online and Differentially-Private Tensor Decomposition

no code implementations NeurIPS 2016 Yining Wang, Animashree Anandkumar

In this paper, we resolve many of the key algorithmic questions regarding robustness, memory efficiency, and differential privacy of tensor decomposition.

Tensor Decomposition

An Improved Gap-Dependency Analysis of the Noisy Power Method

no code implementations23 Feb 2016 Maria Florina Balcan, Simon S. Du, Yining Wang, Adams Wei Yu

We consider the noisy power method algorithm, which has wide applications in machine learning and statistics, especially those related to principal component analysis (PCA) under resource (communication, memory or privacy) constraints.

Spectral Learning for Supervised Topic Models

no code implementations19 Feb 2016 Yong Ren, Yining Wang, Jun Zhu

Spectral methods have been applied to learn unsupervised topic models, such as latent Dirichlet allocation (LDA), with provable guarantees.

Topic Models

On Computationally Tractable Selection of Experiments in Measurement-Constrained Regression Models

no code implementations9 Jan 2016 Yining Wang, Adams Wei Yu, Aarti Singh

We derive computationally tractable methods to select a small subset of experiment settings from a large pool of given design points.

Combinatorial Optimization

Differentially private subspace clustering

no code implementations NeurIPS 2015 Yining Wang, Yu-Xiang Wang, Aarti Singh

Subspace clustering is an unsupervised learning problem that aims at grouping data points into multiple ``clusters'' so that data points in a single cluster lie approximately on a low-dimensional linear subspace.

Motion Segmentation

Fast and Guaranteed Tensor Decomposition via Sketching

no code implementations NeurIPS 2015 Yining Wang, Hsiao-Yu Tung, Alexander Smola, Animashree Anandkumar

Such tensor contractions are encountered in decomposition methods such as tensor power iterations and alternating least squares.

Latent Variable Models Tensor Decomposition

Provably Correct Algorithms for Matrix Column Subset Selection with Selectively Sampled Data

no code implementations17 May 2015 Yining Wang, Aarti Singh

We consider the problem of matrix column subset selection, which selects a subset of columns from an input matrix such that the input can be well approximated by the span of the selected columns.

Recommendation Systems

Graph Connectivity in Noisy Sparse Subspace Clustering

no code implementations4 Apr 2015 Yining Wang, Yu-Xiang Wang, Aarti Singh

A line of recent work (4, 19, 24, 20) provided strong theoretical guarantee for sparse subspace clustering (4), the state-of-the-art algorithm for subspace clustering, on both noiseless and noisy data sets.

Spectral Methods for Supervised Topic Models

no code implementations NeurIPS 2014 Yining Wang, Jun Zhu

Supervised topic models simultaneously model the latent topic structure of large collections of documents and a response variable associated with each document.

Topic Models

Noise-adaptive Margin-based Active Learning and Lower Bounds under Tsybakov Noise Condition

no code implementations20 Jun 2014 Yining Wang, Aarti Singh

We present a simple noise-robust margin-based active learning algorithm to find homogeneous (passing the origin) linear separators and analyze its error convergence when labels are corrupted by noise.

Active Learning

A Theoretical Analysis of NDCG Type Ranking Measures

no code implementations24 Apr 2013 Yining Wang, Li-Wei Wang, Yuanzhi Li, Di He, Tie-Yan Liu, Wei Chen

We show that NDCG with logarithmic discount has consistent distinguishability although it converges to the same limit for all ranking functions.

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