Paper Group ANR 1663
Features in Extractive Supervised Single-document Summarization: Case of Persian News. Optimized Score Transformation for Fair Classification. Group-based Fair Learning Leads to Counter-intuitive Predictions. Non-Rigid Structure from Motion: Prior-Free Factorization Method Revisited. On Performance Estimation in Automatic Algorithm Configuration. U …
Features in Extractive Supervised Single-document Summarization: Case of Persian News
| Title | Features in Extractive Supervised Single-document Summarization: Case of Persian News |
| Authors | Hosein Rezaei, Seyed Amid Moeinzadeh, Azar Shahgholian, Mohamad Saraee |
| Abstract | Text summarization has been one of the most challenging areas of research in NLP. Much effort has been made to overcome this challenge by using either the abstractive or extractive methods. Extractive methods are more popular, due to their simplicity compared with the more elaborate abstractive methods. In extractive approaches, the system will not generate sentences. Instead, it learns how to score sentences within the text by using some textual features and subsequently selecting those with the highest-rank. Therefore, the core objective is ranking and it highly depends on the document. This dependency has been unnoticed by many state-of-the-art solutions. In this work, the features of the document are integrated into vectors of every sentence. In this way, the system becomes informed about the context, increases the precision of the learned model and consequently produces comprehensive and brief summaries. |
| Tasks | Document Summarization, Text Summarization |
| Published | 2019-09-06 |
| URL | https://arxiv.org/abs/1909.02776v2 |
| https://arxiv.org/pdf/1909.02776v2.pdf | |
| PWC | https://paperswithcode.com/paper/features-in-extractive-supervised-single |
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Optimized Score Transformation for Fair Classification
| Title | Optimized Score Transformation for Fair Classification |
| Authors | Dennis Wei, Karthikeyan Natesan Ramamurthy, Flavio du Pin Calmon |
| Abstract | This paper considers fair probabilistic classification where the outputs of primary interest are predicted probabilities, commonly referred to as scores. We formulate the problem of transforming scores to satisfy fairness constraints that are linear in conditional means of scores while minimizing the loss in utility. The formulation can be applied either to post-process classifier outputs or to pre-process training data, thus allowing maximum freedom in selecting a classification algorithm. We derive a closed-form expression for the optimal transformed scores and a convex optimization problem for the transformation parameters. In the population limit, the transformed score function is the fairness-constrained minimizer of cross-entropy with respect to the optimal unconstrained scores. In the finite sample setting, we propose to approach this solution using a combination of standard probabilistic classifiers and ADMM. The transformation parameters obtained from the finite-sample procedure are shown to be asymptotically optimal. Comprehensive experiments comparing to 10 existing methods show that the proposed FairScoreTransformer has advantages for score-based metrics such as Brier score and AUC while remaining competitive for binary label-based metrics such as accuracy. |
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| Published | 2019-05-31 |
| URL | https://arxiv.org/abs/1906.00066v2 |
| https://arxiv.org/pdf/1906.00066v2.pdf | |
| PWC | https://paperswithcode.com/paper/190600066 |
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Group-based Fair Learning Leads to Counter-intuitive Predictions
| Title | Group-based Fair Learning Leads to Counter-intuitive Predictions |
| Authors | Ofir Nachum, Heinrich Jiang |
| Abstract | A number of machine learning (ML) methods have been proposed recently to maximize model predictive accuracy while enforcing notions of group parity or fairness across sub-populations. We propose a desirable property for these procedures, slack-consistency: For any individual, the predictions of the model should be monotonic with respect to allowed slack (i.e., maximum allowed group-parity violation). Such monotonicity can be useful for individuals to understand the impact of enforcing fairness on their predictions. Surprisingly, we find that standard ML methods for enforcing fairness violate this basic property. Moreover, this undesirable behavior arises in situations agnostic to the complexity of the underlying model or approximate optimizations, suggesting that the simple act of incorporating a constraint can lead to drastically unintended behavior in ML. We present a simple theoretical method for enforcing slack-consistency, while encouraging further discussions on the unintended behaviors potentially induced when enforcing group-based parity. |
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| Published | 2019-10-04 |
| URL | https://arxiv.org/abs/1910.02097v1 |
| https://arxiv.org/pdf/1910.02097v1.pdf | |
| PWC | https://paperswithcode.com/paper/group-based-fair-learning-leads-to-counter |
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Non-Rigid Structure from Motion: Prior-Free Factorization Method Revisited
| Title | Non-Rigid Structure from Motion: Prior-Free Factorization Method Revisited |
| Authors | Suryansh Kumar |
| Abstract | A simple prior free factorization algorithm \cite{dai2014simple} is quite often cited work in the field of Non-Rigid Structure from Motion (NRSfM). The benefit of this work lies in its simplicity of implementation, strong theoretical justification to the motion and structure estimation, and its invincible originality. Despite this, the prevailing view is, that it performs exceedingly inferior to other methods on several benchmark datasets \cite{jensen2018benchmark,akhter2009nonrigid}. However, our subtle investigation provides some empirical statistics which made us think against such views. The statistical results we obtained supersedes Dai {\it{et al.}}\cite{dai2014simple} originally reported results on the benchmark datasets by a significant margin under some elementary changes in their core algorithmic idea \cite{dai2014simple}. Now, these results not only exposes some unrevealed areas for research in NRSfM but also give rise to new mathematical challenges for NRSfM researchers. We argue that by \textbf{properly} utilizing the well-established assumptions about a non-rigidly deforming shape i.e, it deforms smoothly over frames \cite{rabaud2008re} and it spans a low-rank space, the simple prior-free idea can provide results which is comparable to the best available algorithms. In this paper, we explore some of the hidden intricacies missed by Dai {\it{et. al.}} work \cite{dai2014simple} and how some elementary measures and modifications can enhance its performance, as high as approx. 18% on the benchmark dataset. The improved performance is justified and empirically verified by extensive experiments on several datasets. We believe our work has both practical and theoretical importance for the development of better NRSfM algorithms. |
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| Published | 2019-02-27 |
| URL | https://arxiv.org/abs/1902.10274v5 |
| https://arxiv.org/pdf/1902.10274v5.pdf | |
| PWC | https://paperswithcode.com/paper/a-simple-prior-free-method-for-non-rigid |
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On Performance Estimation in Automatic Algorithm Configuration
| Title | On Performance Estimation in Automatic Algorithm Configuration |
| Authors | Shengcai Liu, Ke Tang, Yunwen Lei, Xin Yao |
| Abstract | Over the last decade, research on automated parameter tuning, often referred to as automatic algorithm configuration (AAC), has made significant progress. Although the usefulness of such tools has been widely recognized in real world applications, the theoretical foundations of AAC are still very weak. This paper addresses this gap by studying the performance estimation problem in AAC. More specifically, this paper first proves the universal best performance estimator in a practical setting, and then establishes theoretical bounds on the estimation error, i.e., the difference between the training performance and the true performance for a parameter configuration, considering finite and infinite configuration spaces respectively. These findings were verified in extensive experiments conducted on four algorithm configuration scenarios involving different problem domains. Moreover, insights for enhancing existing AAC methods are also identified. |
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| Published | 2019-11-19 |
| URL | https://arxiv.org/abs/1911.08200v1 |
| https://arxiv.org/pdf/1911.08200v1.pdf | |
| PWC | https://paperswithcode.com/paper/on-performance-estimation-in-automatic |
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User independent Emotion Recognition with Residual Signal-Image Network
| Title | User independent Emotion Recognition with Residual Signal-Image Network |
| Authors | Guanghao Yin, Shouqian Sun, Hui Zhang, Dian Yu, Chao Li, Kejun Zhang, Ning Zou |
| Abstract | User independent emotion recognition with large scale physiological signals is a tough problem. There exist many advanced methods but they are conducted under relatively small datasets with dozens of subjects. Here, we propose Res-SIN, a novel end-to-end framework using Electrodermal Activity(EDA) signal images to classify human emotion. We first apply convex optimization-based EDA (cvxEDA) to decompose signals and mine the static and dynamic emotion changes. Then, we transform decomposed signals to images so that they can be effectively processed by CNN frameworks. The Res-SIN combines individual emotion features and external emotion benchmarks to accelerate convergence. We evaluate our approach on the PMEmo dataset, the currently largest emotional dataset containing music and EDA signals. To the best of author’s knowledge, our method is the first attempt to classify large scale subject-independent emotion with 7962 pieces of EDA signals from 457 subjects. Experimental results demonstrate the reliability of our model and the binary classification accuracy of 73.65% and 73.43% on arousal and valence dimension can be used as a baseline. |
| Tasks | Emotion Recognition |
| Published | 2019-08-10 |
| URL | https://arxiv.org/abs/1908.03692v1 |
| https://arxiv.org/pdf/1908.03692v1.pdf | |
| PWC | https://paperswithcode.com/paper/user-independent-emotion-recognition-with |
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Online-Learning for min-max discrete problems
| Title | Online-Learning for min-max discrete problems |
| Authors | Evripidis Bampis, Dimitris Christou, Bruno Escoffier, Nguyen Kim Thang |
| Abstract | We study various discrete nonlinear combinatorial optimization problems in an online learning framework. In the first part, we address the question of whether there are negative results showing that getting a vanishing (or even vanishing approximate) regret is computational hard. We provide a general reduction showing that many (min-max) polynomial time solvable problems not only do not have a vanishing regret, but also no vanishing approximation $\alpha$-regret, for some $\alpha$ (unless $NP=BPP$). Then, we focus on a particular min-max problem, the min-max version of the vertex cover problem which is solvable in polynomial time in the offline case. The previous reduction proves that there is no $(2-\epsilon)$-regret online algorithm, unless Unique Game is in $BPP$; we prove a matching upper bound providing an online algorithm based on the online gradient descent method. Then, we turn our attention to online learning algorithms that are based on an offline optimization oracle that, given a set of instances of the problem, is able to compute the optimum static solution. We show that for different nonlinear discrete optimization problems, it is strongly $NP$-hard to solve the offline optimization oracle, even for problems that can be solved in polynomial time in the static case (e.g. min-max vertex cover, min-max perfect matching, etc.). On the positive side, we present an online algorithm with vanishing regret that is based on the follow the perturbed leader algorithm for a generalized knapsack problem. |
| Tasks | Combinatorial Optimization |
| Published | 2019-07-12 |
| URL | https://arxiv.org/abs/1907.05944v1 |
| https://arxiv.org/pdf/1907.05944v1.pdf | |
| PWC | https://paperswithcode.com/paper/online-learning-for-min-max-discrete-problems |
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Fast Search with Poor OCR
| Title | Fast Search with Poor OCR |
| Authors | Taivanbat Badamdorj, Adiel Ben-Shalom, Nachum Dershowitz, Lior Wolf |
| Abstract | The indexing and searching of historical documents have garnered attention in recent years due to massive digitization efforts of important collections worldwide. Pure textual search in these corpora is a problem since optical character recognition (OCR) is infamous for performing poorly on such historical material, which often suffer from poor preservation. We propose a novel text-based method for searching through noisy text. Our system represents words as vectors, projects queries and candidates obtained from the OCR into a common space, and ranks the candidates using a metric suited to nearest-neighbor search. We demonstrate the practicality of our method on typewritten German documents from the WWII era. |
| Tasks | Optical Character Recognition |
| Published | 2019-09-17 |
| URL | https://arxiv.org/abs/1909.07899v2 |
| https://arxiv.org/pdf/1909.07899v2.pdf | |
| PWC | https://paperswithcode.com/paper/fast-search-with-poor-ocr |
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Learning in Confusion: Batch Active Learning with Noisy Oracle
| Title | Learning in Confusion: Batch Active Learning with Noisy Oracle |
| Authors | Gaurav Gupta, Anit Kumar Sahu, Wan-Yi Lin |
| Abstract | We study the problem of training machine learning models incrementally using active learning with access to imperfect or noisy oracles. We specifically consider the setting of batch active learning, in which multiple samples are selected as opposed to a single sample as in classical settings so as to reduce the training overhead. Our approach bridges between uniform randomness and score based importance sampling of clusters when selecting a batch of new samples. Experiments on benchmark image classification datasets (MNIST, SVHN, and CIFAR10) shows improvement over existing active learning strategies. We introduce an extra denoising layer to deep networks to make active learning robust to label noises and show significant improvements. |
| Tasks | Active Learning, Denoising, Image Classification |
| Published | 2019-09-27 |
| URL | https://arxiv.org/abs/1909.12473v1 |
| https://arxiv.org/pdf/1909.12473v1.pdf | |
| PWC | https://paperswithcode.com/paper/learning-in-confusion-batch-active-learning |
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Unsupervised Deformable Registration for Multi-Modal Images via Disentangled Representations
| Title | Unsupervised Deformable Registration for Multi-Modal Images via Disentangled Representations |
| Authors | Chen Qin, Bibo Shi, Rui Liao, Tommaso Mansi, Daniel Rueckert, Ali Kamen |
| Abstract | We propose a fully unsupervised multi-modal deformable image registration method (UMDIR), which does not require any ground truth deformation fields or any aligned multi-modal image pairs during training. Multi-modal registration is a key problem in many medical image analysis applications. It is very challenging due to complicated and unknown relationships between different modalities. In this paper, we propose an unsupervised learning approach to reduce the multi-modal registration problem to a mono-modal one through image disentangling. In particular, we decompose images of both modalities into a common latent shape space and separate latent appearance spaces via an unsupervised multi-modal image-to-image translation approach. The proposed registration approach is then built on the factorized latent shape code, with the assumption that the intrinsic shape deformation existing in original image domain is preserved in this latent space. Specifically, two metrics have been proposed for training the proposed network: a latent similarity metric defined in the common shape space and a learningbased image similarity metric based on an adversarial loss. We examined different variations of our proposed approach and compared them with conventional state-of-the-art multi-modal registration methods. Results show that our proposed methods achieve competitive performance against other methods at substantially reduced computation time. |
| Tasks | Image Registration, Image-to-Image Translation |
| Published | 2019-03-22 |
| URL | http://arxiv.org/abs/1903.09331v1 |
| http://arxiv.org/pdf/1903.09331v1.pdf | |
| PWC | https://paperswithcode.com/paper/unsupervised-deformable-registration-for |
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Exploring the Role of Prior Beliefs for Argument Persuasion
| Title | Exploring the Role of Prior Beliefs for Argument Persuasion |
| Authors | Esin Durmus, Claire Cardie |
| Abstract | Public debate forums provide a common platform for exchanging opinions on a topic of interest. While recent studies in natural language processing (NLP) have provided empirical evidence that the language of the debaters and their patterns of interaction play a key role in changing the mind of a reader, research in psychology has shown that prior beliefs can affect our interpretation of an argument and could therefore constitute a competing alternative explanation for resistance to changing one’s stance. To study the actual effect of language use vs. prior beliefs on persuasion, we provide a new dataset and propose a controlled setting that takes into consideration two reader level factors: political and religious ideology. We find that prior beliefs affected by these reader level factors play a more important role than language use effects and argue that it is important to account for them in NLP studies of persuasion. |
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| Published | 2019-06-26 |
| URL | https://arxiv.org/abs/1906.11301v1 |
| https://arxiv.org/pdf/1906.11301v1.pdf | |
| PWC | https://paperswithcode.com/paper/exploring-the-role-of-prior-beliefs-for-1 |
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Minimax-optimal decoding of movement goals from local field potentials using complex spectral features
| Title | Minimax-optimal decoding of movement goals from local field potentials using complex spectral features |
| Authors | Marko Angjelichinoski, Taposh Banerjee, John Choi, Bijan Pesaran, Vahid Tarokh |
| Abstract | We consider the problem of predicting eye movement goals from local field potentials (LFP) recorded through a multielectrode array in the macaque prefrontal cortex. The monkey is tasked with performing memory-guided saccades to one of eight targets during which LFP activity is recorded and used to train a decoder. Previous reports have mainly relied on the spectral amplitude of the LFPs as a feature in the decoding step to limited success, while neglecting the phase without proper theoretical justification. This paper formulates the problem of decoding eye movement intentions in a statistically optimal framework and uses Gaussian sequence modeling and Pinsker’s theorem to generate minimax-optimal estimates of the LFP signals which are later used as features in the decoding step. The approach is shown to act as a low-pass filter and each LFP in the feature space is represented via its complex Fourier coefficients after appropriate shrinking such that higher frequency components are attenuated; this way, the phase information inherently present in the LFP signal is naturally embedded into the feature space. The proposed complex spectrum-based decoder achieves prediction accuracy of up to $94%$ at superficial electrode depths near the surface of the prefrontal cortex, which marks a significant performance improvement over conventional power spectrum-based decoders. |
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| Published | 2019-01-29 |
| URL | http://arxiv.org/abs/1901.10397v1 |
| http://arxiv.org/pdf/1901.10397v1.pdf | |
| PWC | https://paperswithcode.com/paper/minimax-optimal-decoding-of-movement-goals |
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Distributed High-dimensional Regression Under a Quantile Loss Function
| Title | Distributed High-dimensional Regression Under a Quantile Loss Function |
| Authors | Xi Chen, Weidong Liu, Xiaojun Mao, Zhuoyi Yang |
| Abstract | This paper studies distributed estimation and support recovery for high-dimensional linear regression model with heavy-tailed noise. To deal with heavy-tailed noise whose variance can be infinite, we adopt the quantile regression loss function instead of the commonly used squared loss. However, the non-smooth quantile loss poses new challenges to high-dimensional distributed estimation in both computation and theoretical development. To address the challenge, we transform the response variable and establish a new connection between quantile regression and ordinary linear regression. Then, we provide a distributed estimator that is both computationally and communicationally efficient, where only the gradient information is communicated at each iteration. Theoretically, we show that, after a constant number of iterations, the proposed estimator achieves a near-oracle convergence rate without any restriction on the number of machines. Moreover, we establish the theoretical guarantee for the support recovery. The simulation analysis is provided to demonstrate the effectiveness of our method. |
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| Published | 2019-06-13 |
| URL | https://arxiv.org/abs/1906.05741v1 |
| https://arxiv.org/pdf/1906.05741v1.pdf | |
| PWC | https://paperswithcode.com/paper/distributed-high-dimensional-regression-under |
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Modeling User Selection in Quality Diversity
| Title | Modeling User Selection in Quality Diversity |
| Authors | Alexander Hagg, Alexander Asteroth, Thomas Bäck |
| Abstract | The initial phase in real world engineering optimization and design is a process of discovery in which not all requirements can be made in advance, or are hard to formalize. Quality diversity algorithms, which produce a variety of high performing solutions, provide a unique chance to support engineers and designers in the search for what is possible and high performing. In this work we begin to answer the question how a user can interact with quality diversity and turn it into an interactive innovation aid. By modeling a user’s selection it can be determined whether the optimization is drifting away from the user’s preferences. The optimization is then constrained by adding a penalty to the objective function. We present an interactive quality diversity algorithm that can take into account the user’s selection. The approach is evaluated in a new multimodal optimization benchmark that allows various optimization tasks to be performed. The user selection drift of the approach is compared to a state of the art alternative on both a planning and a neuroevolution control task, thereby showing its limits and possibilities. |
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| Published | 2019-07-16 |
| URL | https://arxiv.org/abs/1907.06912v1 |
| https://arxiv.org/pdf/1907.06912v1.pdf | |
| PWC | https://paperswithcode.com/paper/modeling-user-selection-in-quality-diversity |
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Visual Hide and Seek
| Title | Visual Hide and Seek |
| Authors | Boyuan Chen, Shuran Song, Hod Lipson, Carl Vondrick |
| Abstract | We train embodied agents to play Visual Hide and Seek where a prey must navigate in a simulated environment in order to avoid capture from a predator. We place a variety of obstacles in the environment for the prey to hide behind, and we only give the agents partial observations of their environment using an egocentric perspective. Although we train the model to play this game from scratch, experiments and visualizations suggest that the agent learns to predict its own visibility in the environment. Furthermore, we quantitatively analyze how agent weaknesses, such as slower speed, effect the learned policy. Our results suggest that, although agent weaknesses make the learning problem more challenging, they also cause more useful features to be learned. Our project website is available at: http://www.cs.columbia.edu/ ~bchen/visualhideseek/. |
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| Published | 2019-10-15 |
| URL | https://arxiv.org/abs/1910.07882v1 |
| https://arxiv.org/pdf/1910.07882v1.pdf | |
| PWC | https://paperswithcode.com/paper/visual-hide-and-seek |
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