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* 29 pages, 2 figures

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Submodular Maximization under Fading Model: Building Online Quizzes for Better Customer Segmentation

Jan 23, 2019

Shaojie Tang

Jan 23, 2019

Shaojie Tang

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Quantum-inspired classical algorithms for principal component analysis and supervised clustering

Oct 31, 2018

Ewin Tang

We describe classical analogues to quantum algorithms for principal component analysis and nearest-centroid clustering. Given sampling assumptions, our classical algorithms run in time polylogarithmic in input, matching the runtime of the quantum algorithms with only polynomial slowdown. These algorithms are evidence that their corresponding problems do not yield exponential quantum speedups. To build our classical algorithms, we use the same techniques as applied in our previous work dequantizing a quantum recommendation systems algorithm. Thus, we provide further evidence for the strength of classical $\ell^2$-norm sampling assumptions when replacing quantum state preparation assumptions, in the machine learning domain.
Oct 31, 2018

Ewin Tang

* 5 pages

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A recommendation system suggests products to users based on data about user preferences. It is typically modeled by a problem of completing an $m\times n$ matrix of small rank $k$. We give the first classical algorithm to produce a recommendation in $O(\text{poly}(k)\text{polylog}(m,n))$ time, which is an exponential improvement on previous algorithms that run in time linear in $m$ and $n$. Our strategy is inspired by a quantum algorithm by Kerenidis and Prakash: like the quantum algorithm, instead of reconstructing a user's full list of preferences, we only seek a randomized sample from the user's preferences. Our main result is an algorithm that samples high-weight entries from a low-rank approximation of the input matrix in time independent of $m$ and $n$, given natural sampling assumptions on that input matrix. As a consequence, we show that Kerenidis and Prakash's quantum machine learning (QML) algorithm, one of the strongest candidates for provably exponential speedups in QML, does not in fact give an exponential speedup over classical algorithms.

* 35 pages

* 35 pages

**Click to Read Paper and Get Code*** Thesis

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* 13 pages, 2 figures

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This paper reviews the state-of-the-art of semantic change computation, one emerging research field in computational linguistics, proposing a framework that summarizes the literature by identifying and expounding five essential components in the field: diachronic corpus, diachronic word sense characterization, change modelling, evaluation data and data visualization. Despite the potential of the field, the review shows that current studies are mainly focused on testifying hypotheses proposed in theoretical linguistics and that several core issues remain to be solved: the need for diachronic corpora of languages other than English, the need for comprehensive evaluation data for evaluation, the comparison and construction of approaches to diachronic word sense characterization and change modelling, and further exploration of data visualization techniques for hypothesis justification.

* 2018, Natural Language Engineering

* This is review of the state of the art of semantic change computation, submitted to Natural Language Engineering

* 2018, Natural Language Engineering

* This is review of the state of the art of semantic change computation, submitted to Natural Language Engineering

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TF.Learn is a high-level Python module for distributed machine learning inside TensorFlow. It provides an easy-to-use Scikit-learn style interface to simplify the process of creating, configuring, training, evaluating, and experimenting a machine learning model. TF.Learn integrates a wide range of state-of-art machine learning algorithms built on top of TensorFlow's low level APIs for small to large-scale supervised and unsupervised problems. This module focuses on bringing machine learning to non-specialists using a general-purpose high-level language as well as researchers who want to implement, benchmark, and compare their new methods in a structured environment. Emphasis is put on ease of use, performance, documentation, and API consistency.

**Click to Read Paper and Get Code*** 10 pages

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* Contribution to the ICML 2013 Challenges in Representation Learning Workshop

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Iterative proportional scaling revisited: a modern optimization perspective

Jul 02, 2018

Yiyuan She, Shao Tang

Jul 02, 2018

Yiyuan She, Shao Tang

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* 22 pages, 14 figures, submitted to Pattern Recognition Letters

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FSMJ: Feature Selection with Maximum Jensen-Shannon Divergence for Text Categorization

Jun 20, 2016

Bo Tang, Haibo He

Jun 20, 2016

Bo Tang, Haibo He

* 8 pages, 6 figures, World Congress on Intelligent Control and Automation, 2016

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Knowledge graph reasoning, which aims at predicting the missing facts through reasoning with the observed facts, is critical to many applications. Such a problem has been widely explored by traditional logic rule-based approaches and recent knowledge graph embedding methods. A principled logic rule-based approach is the Markov Logic Network (MLN), which is able to leverage domain knowledge with first-order logic and meanwhile handle their uncertainty. However, the inference of MLNs is usually very difficult due to the complicated graph structures. Different from MLNs, knowledge graph embedding methods (e.g. TransE, DistMult) learn effective entity and relation embeddings for reasoning, which are much more effective and efficient. However, they are unable to leverage domain knowledge. In this paper, we propose the probabilistic Logic Neural Network (pLogicNet), which combines the advantages of both methods. A pLogicNet defines the joint distribution of all possible triplets by using a Markov logic network with first-order logic, which can be efficiently optimized with the variational EM algorithm. In the E-step, a knowledge graph embedding model is used for inferring the missing triplets, while in the M-step, the weights of logic rules are updated based on both the observed and predicted triplets. Experiments on multiple knowledge graphs prove the effectiveness of pLogicNet over many competitive baselines.

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A Seq-to-Seq Transformer Premised Temporal Convolutional Network for Chinese Word Segmentation

May 21, 2019

Wei Jiang, Yan Tang

May 21, 2019

Wei Jiang, Yan Tang

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Constrained submodular maximization has been extensively studied in the recent years. In this paper, we study adaptive robust optimization with nearly submodular structure (ARONSS). Our objective is to randomly select a subset of items that maximizes the worst-case value of several reward functions simultaneously. Our work differs from existing studies in two ways: (1) we study the robust optimization problem under the adaptive setting, i.e., one needs to adaptively select items based on the feedback collected from picked items, and (2) our results apply to a broad range of reward functions characterized by $\epsilon$-nearly submodular function. We first analyze the adaptvity gap of ARONSS and show that the gap between the best adaptive solution and the best non-adaptive solution is bounded. Then we propose two algorithms that achieve bounded approximation ratios.

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Real-time Trajectory Generation for Quadrotors using B-spline based Non-uniform Kinodynamic Search

Apr 28, 2019

Lvbang Tang, Hesheng Wang

Apr 28, 2019

Lvbang Tang, Hesheng Wang

* 7 pages,6 figures, conference

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Learning Hierarchical Discourse-level Structure for Fake News Detection

Apr 10, 2019

Hamid Karimi, Jiliang Tang

Apr 10, 2019

Hamid Karimi, Jiliang Tang

* Accepted to 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics June 2-7, 2019 Minneapolis, USA

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