OpenRaiser/Intern-Atlas huggingface dataset:
Cotype: Joint extraction of typed entities and relations with knowledge bases
Authors: Xiang Ren, Zeqiu Wu, Wenqi He, Meng Qu, Clare R. Voss, Heng Ji, Tarek F. Abdelzaher, Jiawei Han
Venue: Conference WWW 2017
Year: 2017

Extracting entities and relations for types of interest from text is important for understanding massive text corpora. Traditionally, systems of entity relation extraction have relied on human-annotated corpora for training and adopted an incremental pipeline. Such systems require additional human expertise to be ported to a new domain, and are vulnerable to errors cascading down the pipeline. In this paper, we investigate joint extraction of typed entities and relations with labeled data heuristically obtained from knowledge bases (i.e., distant supervision). As our algorithm for type labeling via distant supervision is context-agnostic, noisy training data poses unique challenges for the task. We propose a novel domain-independent framework, called CoType, that runs a data-driven text segmentation algorithm to extract entity mentions, and jointly embeds entity mentions, relation mentions, text features and type labels into two low-dimensional spaces (for entity and relation mentions respectively), where, in each space, objects whose types are close will also have similar representations. CoType, then using these learned embeddings, estimates the types of test (unlinkable) mentions. We formulate a joint optimization problem to learn embeddings from text corpora and knowledge bases, adopting a novel partial-label loss function for noisy labeled data and introducing an object "translation" function to capture the cross-constraints of entities and relations on each other. Experiments on three public datasets demonstrate the effectiveness of CoType across different domains (e.g., news, biomedical), with an average of 25% improvement in F1 score compared to the next best method.
ProMP: Proximal Meta-Policy Search
Authors: Jonas Rothfuss, Dennis Lee 0003, Ignasi Clavera, Tamim Asfour, Pieter Abbeel
Venue: Conference ICLR 2019
Year: 2019

Credit assignment in Meta-reinforcement learning (Meta-RL) is still poorly understood. Existing methods either neglect credit assignment to pre-adaptation behavior or implement it naively. This leads to poor sample-efficiency during meta-training as well as ineffective task identification strategies. This paper provides a theoretical analysis of credit assignment in gradient-based Meta-RL. Building on the gained insights we develop a novel meta-learning algorithm that overcomes both the issue of poor credit assignment and previous difficulties in estimating meta-policy gradients. By controlling the statistical distance of both pre-adaptation and adapted policies during meta-policy search, the proposed algorithm endows efficient and stable meta-learning. Our approach leads to superior pre-adaptation policy behavior and consistently outperforms previous Meta-RL algorithms in sample-efficiency, wall-clock time, and asymptotic performance.
Detecting biased statements in wikipedia
Authors: Christoph Hube, Besnik Fetahu
Venue: Conference WWW 2018
Year: 2018

Quality in Wikipedia is enforced through a set of editing policies and guidelines recommended for Wikipedia editors. Neutral point of view (NPOV) is one of the main principles in Wikipedia, which ensures that for controversial information all possible points of view are represented proportionally. Furthermore, language used in Wikipedia should be neutral and not opinionated. However, due to the large number of Wikipedia articles and its operating principle based on a voluntary basis of Wikipedia editors; quality assurances and Wikipedia guidelines cannot always be enforced. Currently, there are more than 40,000 articles, which are flagged with NPOV or similar quality tags. Furthermore, these represent only the portion of articles for which such quality issues are explicitly flagged by the Wikipedia editors, however, the real number may be higher considering that only a small percentage of articles are of good quality or featured as categorized by Wikipedia. In this work, we focus on the case of language bias at the sentence level in Wikipedia. Language bias is a hard problem, as it represents a subjective task and usually the linguistic cues are subtle and can be determined only through its context. We propose a supervised classification approach, which relies on an automatically created lexicon of bias words, and other syntactical and semantic characteristics of biased statements. We experimentally evaluate our approach on a dataset consisting of biased and unbiased statements, and show that we are able to detect biased statements with an accuracy of 74%. Furthermore, we show that competitors that determine bias words are not suitable for detecting biased statements, which we outperform with a relative improvement of over 20%.
Bayesian poisson regression for crowd counting
Authors: Antoni B. Chan, Nuno Vasconcelos
Venue: Conference ICCV 2009
Year: 2009

Poisson regression models the noisy output of a counting function as a Poisson random variable, with a log-mean parameter that is a linear function of the input vector. In this work, we analyze Poisson regression in a Bayesian setting, by introducing a prior distribution on the weights of the linear function. Since exact inference is analytically unobtainable, we derive a closed-form approximation to the predictive distribution of the model. We show that the predictive distribution can be kernelized, enabling the representation of non-linear log-mean functions. We also derive an approximate marginal likelihood that can be optimized to learn the hyperparameters of the kernel. We then relate the proposed approximate Bayesian Poisson regression to Gaussian processes. Finally, we present experimental results using Bayesian Poisson regression for crowd counting from low-level features.
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
Authors: Jonathan Frankle, Michael Carbin
Venue: Conference ICLR 2019
Year: 2019

Neural network pruning techniques can reduce the parameter counts of trained networks by over 90%, decreasing storage requirements and improving computational performance of inference without compromising accuracy. However, contemporary experience is that the sparse architectures produced by pruning are difficult to train from the start, which would similarly improve training performance. We find that a standard pruning technique naturally uncovers subnetworks whose initializations made them capable of training effectively. Based on these results, we articulate the "lottery ticket hypothesis:" dense, randomly-initialized, feed-forward networks contain subnetworks ("winning tickets") that - when trained in isolation - reach test accuracy comparable to the original network in a similar number of iterations. The winning tickets we find have won the initialization lottery: their connections have initial weights that make training particularly effective. We present an algorithm to identify winning tickets and a series of experiments that support the lottery ticket hypothesis and the importance of these fortuitous initializations. We consistently find winning tickets that are less than 10-20% of the size of several fully-connected and convolutional feed-forward architectures for MNIST and CIFAR10. Above this size, the winning tickets that we find learn faster than the original network and reach higher test accuracy.
Crowdsourcing cybersecurity: Cyber attack detection using social media
Authors: Rupinder Paul Khandpur, Taoran Ji, Steve T. K. Jan, Gang Wang, Chang-Tien Lu, Naren Ramakrishnan
Venue: Conference CIKM 2017
Year: 2017

Social media is often viewed as a sensor into various societal events such as disease outbreaks, protests, and elections. We describe the use of social media as a crowdsourced sensor to gain insight into ongoing cyber-attacks. Our approach detects a broad range of cyber-attacks (e.g., distributed denial of service (DDoS) attacks, data breaches, and account hijacking) in a weakly supervised manner using just a small set of seed event triggers and requires no training or labeled samples. A new query expansion strategy based on convolution kernels and dependency parses helps model semantic structure and aids in identifying key event characteristics. Through a large-scale analysis over Twitter, we demonstrate that our approach consistently identifies and encodes events, outperforming existing methods.
Ensemble selection from libraries of models
Authors: Rich Caruana, Alexandru Niculescu-Mizil, Geoff Crew, Alex Ksikes
Venue: Conference ICML 2004
Year: 2004

We present a method for constructing ensembles from libraries of thousands of models. Model libraries are generated using different learning algorithms and parameter settings. Forward stepwise selection is used to add to the ensemble the models that maximize its performance. Ensemble selection allows ensembles to be optimized to performance metric such as accuracy, cross entropy, mean precision, or ROC Area. Experiments with seven test problems and ten metrics demonstrate the benefit of ensemble selection.
Multiple queries as bandit arms
Authors: Cheng Li, Paul Resnick, Qiaozhu Mei
Venue: Conference CIKM 2016
Year: 2016

Existing retrieval systems rely on a single active query to pull documents from the index. Relevance feedback may be used to iteratively refine the query, but only one query is active at a time. If the user's information need has multiple aspects, the query must represent the union of these aspects. We consider a new paradigm of retrieval where multiple queries are kept ``active'' simultaneously. In the presence of rate limits, the active queries take turns accessing the index to retrieve another ``page'' of results. Turns are assigned by a multi-armed bandit based on user feedback. This allows the system to explore which queries return more relevant results and to exploit the best ones. In empirical tests, query pools outperform solo, combined queries. Significant improvement is observed both when the subtopic queries are known in advance and when the queries are generated in a user-interactive process.
Win-win search: dual-agent stochastic game in session search
Authors: Jiyun Luo, Sicong Zhang, Hui Yang
Venue: Conference SIGIR 2014
Year: 2014

Session search is a complex search task that involves multiple search iterations triggered by query reformulations. We observe a Markov chain in session search: user's judgment of retrieved documents in the previous search iteration affects user's actions in the next iteration. We thus propose to model session search as a dual-agent stochastic game: the user agent and the search engine agent work together to jointly maximize their long term rewards. The framework, which we term "win-win search", is based on Partially Observable Markov Decision Process. We mathematically model dynamics in session search, including decision states, query changes, clicks, and rewards, as a cooperative game between the user and the search engine. The experiments on TREC 2012 and 2013 Session datasets show a statistically significant improvement over the state-of-the-art interactive search and session search algorithms.
Lda-based document models for ad-hoc retrieval
Authors: Xing Wei, W. Bruce Croft
Venue: Conference SIGIR 2006
Year: 2006

Search algorithms incorporating some form of topic model have a long history in information retrieval. For example, cluster-based retrieval has been studied since the 60s and has recently produced good results in the language model framework. An approach to building topic models based on a formal generative model of documents, Latent Dirichlet Allocation (LDA), is heavily cited in the machine learning literature, but its feasibility and effectiveness in information retrieval is mostly unknown. In this paper, we study how to efficiently use LDA to improve ad-hoc retrieval. We propose an LDA-based document model within the language modeling framework, and evaluate it on several TREC collections. Gibbs sampling is employed to conduct approximate inference in LDA and the computational complexity is analyzed. We show that improvements over retrieval using cluster-based models can be obtained with reasonable efficiency.
Dynamic information retrieval modeling
Authors: Hui Yang, Marc Sloan, Jun Wang
Venue: Conference WSDM 2015
Year: 2015

In Dynamic Information Retrieval modeling we model dynamic systems which change or adapt over time or a sequence of events using a range of techniques from artificial intelligence and reinforcement learning. Many of the open problems in current IR research can be described as dynamic systems, for instance, session search or computational advertising. State of the art research provides solutions to these problems that are responsive to a changing environment, learn from past interactions and predict future utility. Advances in IR interface, personalization and ad display demand models that can react to users in real time and in an intelligent, contextual way. The objective of this half-day tutorial is to provide a comprehensive and up-to-date introduction to Dynamic Information Retrieval Modeling. We motivate a conceptual model linking static, interactive and dynamic retrieval and use this to define dynamics within the context of IR. We then cover a number of algorithms and techniques from the artificial intelligence (AI) and online learning literature such as Markov Decision Processes (MDP), their partially observable variation (POMDP) and multi-armed bandits. Following this we describe how to identify dynamics in an IR problem and demonstrate how to model them using the described techniques. The remainder of the tutorial will then cover an array of state-of-the-art research on dynamic systems in IR and how they can be modeled using using dynamic IR. We use research on session search, multi-page search and online advertising as in-depth examples of such work. This tutorial is of relevance to IR practitioners and researchers, where we will present the merits of dynamic information retrieval modeling and introduce the relevant techniques. The content will be of particular interest to researchers working in the areas of statistical modeling, personalization and recommendation, and is also relevant to practitioners in Web search, online advertising and anyone who works with big data. After this tutorial, attendees will: Be able to identify the dynamics in an IR system; Be able to model these dynamics using techniques from AI and reinforcement learning; Have knowledge of the state-of-the-art research in dynamic information retrieval modeling.
Weisfeiler-lehman neural machine for link prediction
Authors: Muhan Zhang, Yixin Chen
Venue: Conference KDD 2017
Year: 2017

In this paper, we propose a next-generation link prediction method, Weisfeiler-Lehman Neural Machine (WLNM), which learns topological features in the form of graph patterns that promote the formation of links. WLNM has unmatched advantages including higher performance than state-of-the-art methods and universal applicability over various kinds of networks. WLNM extracts an enclosing subgraph of each target link and encodes the subgraph as an adjacency matrix. The key novelty of the encoding comes from a fast hashing-based Weisfeiler-Lehman (WL) algorithm that labels the vertices according to their structural roles in the subgraph while preserving the subgraph's intrinsic directionality. After that, a neural network is trained on these adjacency matrices to learn a predictive model. Compared with traditional link prediction methods, WLNM does not assume a particular link formation mechanism (such as common neighbors), but learns this mechanism from the graph itself. We conduct comprehensive experiments to show that WLNM not only outperforms a great number of state-of-the-art link prediction methods, but also consistently performs well across networks with different characteristics.
Beyond the point cloud: from transductive to semi-supervised learning
Authors: Vikas Sindhwani, Partha Niyogi, Mikhail Belkin
Venue: Conference ICML 2005
Year: 2005

Due to its occurrence in engineering domains and implications for natural learning, the problem of utilizing unlabeled data is attracting increasing attention in machine learning. A large body of recent literature has focussed on the transductive setting where labels of unlabeled examples are estimated by learning a function defined only over the point cloud data. In a truly semi-supervised setting however, a learning machine has access to labeled and unlabeled examples and must make predictions on data points never encountered before. In this paper, we show how to turn transductive and standard supervised learning algorithms into semi-supervised learners. We construct a family of data-dependent norms on Reproducing Kernel Hilbert Spaces (RKHS). These norms allow us to warp the structure of the RKHS to reflect the underlying geometry of the data. We derive explicit formulas for the corresponding new kernels. Our approach demonstrates state of the art performance on a variety of classification tasks.
Reward estimation for variance reduction in deep reinforcement learning
Authors: Joshua Romoff, Alexandre Piché, Peter Henderson 0002, Vincent François-Lavet, Joelle Pineau
Venue: Conference ICLR 2018
Year: 2018

Reinforcement Learning (RL) agents require the specification of a reward signal for learning behaviours. However, introduction of corrupt or stochastic rewards can yield high variance in learning. Such corruption may be a direct result of goal misspecification, randomness in the reward signal, or correlation of the reward with external factors that are not known to the agent. Corruption or stochasticity of the reward signal can be especially problematic in robotics, where goal specification can be particularly difficult for complex tasks. While many variance reduction techniques have been studied to improve the robustness of the RL process, handling such stochastic or corrupted reward structures remains difficult. As an alternative for handling this scenario in model-free RL methods, we suggest using an estimator for both rewards and value functions. We demonstrate that this improves performance under corrupted stochastic rewards in both the tabular and non-linear function approximation settings for a variety of noise types and environments. The use of reward estimation is a robust and easy-to-implement improvement for handling corrupted reward signals in model-free RL.
Recasting Gradient-Based Meta-Learning as Hierarchical Bayes
Authors: Erin Grant, Chelsea Finn, Sergey Levine, Trevor Darrell, Thomas L. Griffiths 0001
Venue: Conference ICLR 2018
Year: 2018

Meta-learning allows an intelligent agent to leverage prior learning episodes as a basis for quickly improving performance on a novel task. Bayesian hierarchical modeling provides a theoretical framework for formalizing meta-learning as inference for a set of parameters that are shared across tasks. Here, we reformulate the model-agnostic meta-learning algorithm (MAML) of Finn et al. (2017) as a method for probabilistic inference in a hierarchical Bayesian model. In contrast to prior methods for meta-learning via hierarchical Bayes, MAML is naturally applicable to complex function approximators through its use of a scalable gradient descent procedure for posterior inference. Furthermore, the identification of MAML as hierarchical Bayes provides a way to understand the algorithm's operation as a meta-learning procedure, as well as an opportunity to make use of computational strategies for efficient inference. We use this opportunity to propose an improvement to the MAML algorithm that makes use of techniques from approximate inference and curvature estimation.
Texttruth: An unsupervised approach to discover trustworthy information from multi-sourced text data
Authors: Hengtong Zhang, Yaliang Li, Fenglong Ma, Jing Gao, Lu Su
Venue: Conference KDD 2018
Year: 2018

Truth discovery has attracted increasingly more attention due to its ability to distill trustworthy information from noisy multi-sourced data without any supervision. However, most existing truth discovery methods are designed for structured data, and cannot meet the strong need to extract trustworthy information from raw text data as text data has its unique characteristics. The major challenges of inferring true information on text data stem from the multifactorial property of text answers (i.e., an answer may contain multiple key factors) and the diversity of word usages (i.e., different words may have the same semantic meaning). To tackle these challenges, in this paper, we propose a novel truth discovery method, named "TextTruth", which jointly groups the keywords extracted from the answers of a specific question into multiple interpretable factors, and infers the trustworthiness of both answer factors and answer providers. After that, the answers to each question can be ranked based on the estimated trustworthiness of factors. The proposed method works in an unsupervised manner, and thus can be applied to various application scenarios that involve text data. Experiments on three real-world datasets show that the proposed TextTruth model can accurately select trustworthy answers, even when these answers are formed by multiple factors.
Face recognition in unconstrained videos with matched background similarity
Authors: Lior Wolf, Tal Hassner, Itay Maoz
Venue: Conference CVPR 2011
Year: 2011

Recognizing faces in unconstrained videos is a task of mounting importance. While obviously related to face recognition in still images, it has its own unique characteristics and algorithmic requirements. Over the years several methods have been suggested for this problem, and a few benchmark data sets have been assembled to facilitate its study. However, there is a sizable gap between the actual application needs and the current state of the art. In this paper we make the following contributions. (a) We present a comprehensive database of labeled videos of faces in challenging, uncontrolled conditions (i.e., `in the wild'), the `YouTube Faces' database, along with benchmark, pair-matching tests <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . (b) We employ our benchmark to survey and compare the performance of a large variety of existing video face recognition techniques. Finally, (c) we describe a novel set-to-set similarity measure, the Matched Background Similarity (MBGS). This similarity is shown to considerably improve performance on the benchmark tests.