39 citations · 160 across the 28 of their papers we have counts for
29 papers
Approximability of the Four-Vertex Model
Zhiguo Fu, Tianyu Liu, Xiongxin Yang
We study the approximability of the four-vertex model, a special case of the six-vertex model.We prove that, despite being NP-hard to approximate in the worst case, the four-vertex…
Demystify Optimization and Generalization of Over-parameterized PAC-Bayesian Learning
Wei Huang, Chunrui Liu, Yilan Chen +2
PAC-Bayesian is an analysis framework where the training error can be expressed as the weighted average of the hypotheses in the posterior distribution whilst incorporating the pri…
Explicit Interaction Network for Aspect Sentiment Triplet Extraction
Peiyi Wang, Tianyu Liu, Damai Dai +3
Aspect Sentiment Triplet Extraction (ASTE) aims to recognize targets, their sentiment polarities and opinions explaining the sentiment from a sentence. ASTE could be naturally divi…
Document-level Event Extraction via Heterogeneous Graph-based Interaction Model with a Tracker
Runxin Xu, Tianyu Liu, Lei Li +1
Document-level event extraction aims to recognize event information from a whole piece of article. Existing methods are not effective due to two challenges of this task: a) the tar…
Apply Artificial Neural Network to Solving Manpower Scheduling Problem
Tianyu Liu, Lingyu Zhang
The manpower scheduling problem is a kind of critical combinational optimization problem. Researching solutions to scheduling problems can improve the efficiency of companies, hosp…
An Intelligent Model for Solving Manpower Scheduling Problems
Lingyu Zhang, Tianyu Liu, Yunhai Wang
The manpower scheduling problem is a critical research field in the resource management area. Based on the existing studies on scheduling problem solutions, this paper transforms t…
Automatic Learning to Detect Concept Drift
Hang Yu, Tianyu Liu, Jie Lu +1
Many methods have been proposed to detect concept drift, i.e., the change in the distribution of streaming data, due to concept drift causes a decrease in the prediction accuracy o…
A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation
Tianyu Liu, Yizhe Zhang, Chris Brockett +4
Large pretrained generative models like GPT-3 often suffer from hallucinating non-existent or incorrect content, which undermines their potential merits in real applications. Exist…
Integrating Fast Regional Optimization into Sampling-based Kinodynamic Planning for Multirotor Flight
Hongkai Ye, Tianyu Liu, Chao Xu +1
For real-time multirotor kinodynamic motion planning, the efficiency of sampling-based methods is usually hindered by difficult-to-sample homotopy classes like narrow passages. In…
Towards Faithfulness in Open Domain Table-to-text Generation from an Entity-centric View
Tianyu Liu, Xin Zheng, Baobao Chang +1
In open domain table-to-text generation, we notice that the unfaithful generation usually contains hallucinated content which can not be aligned to any input table record. We thus…
PAC-Bayes Bounds for Meta-learning with Data-Dependent Prior
Tianyu Liu, Jie Lu, Zheng Yan +1
By leveraging experience from previous tasks, meta-learning algorithms can achieve effective fast adaptation ability when encountering new tasks. However it is unclear how the gene…
First Target and Opinion then Polarity: Enhancing Target-opinion Correlation for Aspect Sentiment Triplet Extraction
Lianzhe Huang, Peiyi Wang, Sujian Li +5
Aspect Sentiment Triplet Extraction (ASTE) aims to extract triplets from a sentence, including target entities, associated sentiment polarities, and opinion spans which rationalize…
Visual Companion for Booklovers
Zona Kostic, Jared Jessup, Jeffrey Baglioni +4
An innumerable number of individual choices go into discovering a new book. There are unmistakably two groups of booklovers: those who like to search online, follow other people's…
An Empirical Study on Model-agnostic Debiasing Strategies for Robust Natural Language Inference
Tianyu Liu, Xin Zheng, Xiaoan Ding +2
The prior work on natural language inference (NLI) debiasing mainly targets at one or few known biases while not necessarily making the models more robust. In this paper, we focus…
FPRAS via MCMC where it mixes torpidly (and very little effort)
Jin-Yi Cai, Tianyu Liu
Is Fully Polynomial-time Randomized Approximation Scheme (FPRAS) for a problem via an MCMC algorithm possible when it is known that rapid mixing provably fails? We introduce severa…
Discriminatively-Tuned Generative Classifiers for Robust Natural Language Inference
Xiaoan Ding, Tianyu Liu, Baobao Chang +2
While discriminative neural network classifiers are generally preferred, recent work has shown advantages of generative classifiers in term of data efficiency and robustness. In th…
An Exploration of Arbitrary-Order Sequence Labeling via Energy-Based Inference Networks
Lifu Tu, Tianyu Liu, Kevin Gimpel
Many tasks in natural language processing involve predicting structured outputs, e.g., sequence labeling, semantic role labeling, parsing, and machine translation. Researchers are…
Understanding the hardness of approximate query processing with joins
Tianyu Liu, Chi Wang
We study the hardness of Approximate Query Processing (AQP) of various types of queries involving joins over multiple tables of possibly different sizes. In the case where the quer…
Compare and Select: Video Summarization with Multi-Agent Reinforcement Learning
Tianyu Liu
Video summarization aims at generating concise video summaries from the lengthy videos, to achieve better user watching experience. Due to the subjectivity, purely supervised metho…
HypoNLI: Exploring the Artificial Patterns of Hypothesis-only Bias in Natural Language Inference
Tianyu Liu, Xin Zheng, Baobao Chang +1
Many recent studies have shown that for models trained on datasets for natural language inference (NLI), it is possible to make correct predictions by merely looking at the hypothe…
Table-to-Text Natural Language Generation with Unseen Schemas
Tianyu Liu, Wei Wei, William Yang Wang
Traditional table-to-text natural language generation (NLG) tasks focus on generating text from schemas that are already seen in the training set. This limitation curbs their gener…
Counting perfect matchings and the eight-vertex model
Jin-Yi Cai, Tianyu Liu
We study the approximation complexity of the partition function of the eight-vertex model on general 4-regular graphs. For the first time, we relate the approximability of the eigh…
Approximability of the Eight-vertex Model
Jin-Yi Cai, Tianyu Liu, Pinyan Lu +1
We initiate a study of the classification of approximation complexity of the eight-vertex model defined over 4-regular graphs. The eight-vertex model, together with its special cas…
Torpid Mixing of Markov Chains for the Six-vertex Model on
Tianyu Liu
In this paper, we study the mixing time of two widely used Markov chain algorithms for the six-vertex model, Glauber dynamics and the directed-loop algorithm, on the square lattice…