Publications

Sociotechnical Harms: Scoping a Taxonomy for Harm Reduction
Renee Shelby
Shalaleh Rismani
Kathryn Henne
AJung Moon
Negar Rostamzadeh
Paul Nicholas
N'mah Fodiatu Yilla
Jess Gallegos
Andrew J Smart
Emilio Garcia
Gurleen Virk
2021-12-31
arXiv.org (prépublication)
doi.org
Source-summary Entity Aggregation in Abstractive Summarization.
José-ángel González
Annie Priyadarshini Louis
Jackie CK Cheung
2021-12-31
COLING (publié)
dblp.uni-trier.de
Straight to the Tree: Constituency Parsing with Neural Syntactic Distance
Yikang Shen
Zhouhan Lin
Athul Paul Jacob
Alessandro Sordoni
Aaron Courville
Yoshua Bengio
In this work, we propose a novel constituency parsing scheme. The model predicts a vector of real-valued scalars, named syntactic distances,… (voir plus) for each split position in the input sentence. The syntactic distances specify the order in which the split points will be selected, recursively partitioning the input, in a top-down fashion. Compared to traditional shift-reduce parsing schemes, our approach is free from the potential problem of compounding errors, while being faster and easier to parallelize. Our model achieves competitive performance amongst single model, discriminative parsers in the PTB dataset and outperforms previous models in the CTB dataset.
2021-12-31
Annual Meeting of the Association for Computational Linguistics (publié)
doi.org
arxiv.org
A Synchro-Set-Aided Breadth-First Sphere Decoder for Polar-Coded MIMO Systems
Huayi Zhou
Xiangyun Deng
Yiqian Cai
Yifei Shen
Minhua Yang
Warren J. Gross
Xiaohu You
Chuan Zhang
The joint optimization of multiple-input-multiple-output (MIMO) detection and polar decoding has become a research hotspot for future commun… (voir plus)ication systems. The error-correction performance of the separate detection and decoding (SDD) is far from the Shannon capacity, which cannot meet the requirements of communication scenarios such as ultra-reliable and low latency communications (URLLC). The existing joint detection and decoding (JDD) using breadth-first sphere decoding (BFSD) improves the reliability over SDD but still has a huge performance loss on low-rate codes. In this paper, JDD using synchro-set-aided BFSD (SA-BFSD) is proposed to greatly improve the error-correction performance for polar-coded MIMO systems. We first propose a method to generate the symbol synchro sets through the concept of frozen symbols, then refine the symbol synchro sets based on the characteristics analysis of the channel matrix. We optimize the enumerating order of the symbols and reduce the enumerating levels. The frame error rate (FER) and the bit error rate of the proposed algorithms are significantly improved especially for the low-rate codes. The proposed SA-BFSD JDD achieves an up to 7.8 dB performance gain over BFSD at FER
2021-12-31
IEEE Transactions on Signal Processing (publié)
doi.org
TaHiD: Tackling Data Hiding in Fake News Detection with News Propagation Networks
Adrien Benamira
Benjamin Devillers
Etienne Lesot
Ayush K. Ray
Manal Saadi
Fragkiskos D 587
Steven Bird
Ewan Klein
Edward Loper
Nat-593
Carlos Castillo
Marcelo Mendoza
Barbara Poblete
Daryna Dementieva
Alexander Panchenko
Jacob Devlin
Ming-Wei Chang
Kenton Lee
Ashish Vaswani
Noam M. Shazeer … (voir 8 de plus)
Niki Parmar
Adriana Romero
Pietro Lio’
Yoshua Bengio
Yaqing Wang
Fenglong Ma
Zhiwei Jin
Ye Yuan
Fake news with detrimental societal effects has 001 attracted extensive attention and research. De-002 spite early success, the state-of-the… (voir plus)-art meth-003 ods fall short of considering the propagation 004 of news. News propagates at different times 005 through different mediums, including users, 006 comments, and sources, which form the news 007 propagation network. Moreover, the serious 008 problem of data hiding arises, which means 009 that fake news publishers disguise fake news 010 as real to confuse users by deleting comments 011 that refute the rumor or deleting the news itself 012 when it has been spread widely. Existing meth-013 ods do not consider the propagation of news 014 and fail to identify what matters in the process, 015 which leads to fake news hiding in the prop-016 agation network and escaping from detection. 017 Inspired by the propagation of news, we pro-018 pose a novel fake news detection framework 019 named TaHiD, which models the propagation 020 as a heterogeneous dynamic graph and contains 021 the propagation attention module to measure 022 the influence of different propagation. Exper-023 iments demonstrate that TaHiD extracts use-024 ful information from the news propagation net-025 work and outperforms state-of-the-art methods 026 on several benchmark datasets for fake news 027 detection. Additional studies also show that 028 TaHiD is capable of identifying fake news in 029 the case of data hiding. 030
2021-12-31
(publié)
www.semanticscholar.org
Task-Agnostic Continual Reinforcement Learning: In Praise of a Simple Baseline
Massimo Caccia
Jonas Mueller
Taesup Kim
Laurent Charlin
Rasool Fakoor
We study task-agnostic continual reinforcement learning (TACRL) in which standard RL challenges are compounded with partial observability st… (voir plus)emming from task agnosticism, as well as additional difficulties of continual learning (CL), i.e., learning on a non-stationary sequence of tasks. Here we compare TACRL methods with their soft upper bounds prescribed by previous literature: multi-task learning (MTL) methods which do not have to deal with non-stationary data distributions, as well as task-aware methods, which are allowed to operate under full observability . We consider a previously unexplored and straightforward baseline for TACRL, replay-based recurrent RL (3RL), in which we augment an RL algorithm with recurrent mechanisms to address partial observability and experience replay mechanisms to address catastrophic forgetting in CL. Studying empirical performance in a sequence of RL tasks, we find surprising occurrences of 3RL matching and overcoming the MTL and task-aware soft upper bounds. We lay out hypotheses that could explain this inflection point of continual and task-agnostic learning research. Our hypotheses are empirically tested in continuous control tasks via a large-scale study of the popular multi-task and continual learning benchmark Meta-World. By analyzing different training statistics including gradient conflict, we find evidence that 3RL’s outperformance stems from its ability to quickly infer how new tasks relate with the previous ones, enabling forward transfer.
2021-12-31
arXiv.org (prépublication)
doi.org
On the benefits of representation regularization in invariance based domain generalization
Changjian Shui
Boyu Wang
Christian Gagné
A crucial aspect of reliable machine learning is to design a deployable system for generalizing new related but unobserved environments. Dom… (voir plus)ain generalization aims to alleviate such a prediction gap between the observed and unseen environments. Previous approaches commonly incorporated learning the invariant representation for achieving good empirical performance. In this paper, we reveal that merely learning the invariant representation is vulnerable to the related unseen environment. To this end, we derive a novel theoretical analysis to control the unseen test environment error in the representation learning, which highlights the importance of controlling the smoothness of representation. In practice, our analysis further inspires an efficient regularization method to improve the robustness in domain generalization. The proposed regularization is orthogonal to and can be straightforwardly adopted in existing domain generalization algorithms that ensure invariant representation learning. Empirical results show that our algorithm outperforms the base versions in various datasets and invariance criteria.
2021-12-31
Machine Learning (publié)
doi.org
arxiv.org
On the Convergence of Stochastic Extragradient for Bilinear Games with Restarted Iteration Averaging
Chris Junchi Li
Yaodong Yu
Nicolas Loizou
Gauthier Gidel
Yitong Ma
Nicolas Roux
Michael I. Jordan
We study the stochastic bilinear minimax optimization problem, presenting an analysis of the same-sample Stochastic ExtraGradient (SEG) meth… (voir plus)od with constant step size, and presenting variations of the method that yield favorable convergence. In sharp contrasts with the basic SEG method whose last iterate only contracts to a fixed neighborhood of the Nash equilibrium, SEG augmented with iteration averaging provably converges to the Nash equilibrium under the same standard settings, and such a rate is further improved by incorporating a scheduled restarting procedure. In the interpolation setting where noise vanishes at the Nash equilibrium, we achieve an optimal convergence rate up to tight constants. We present numerical experiments that validate our theoretical findings and demonstrate the effectiveness of the SEG method when equipped with iteration averaging and restarting.
2021-12-31
AISTATS (publié)
arxiv.org
arxiv.org
On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?
Nouha Dziri
Sivan Milton
Mo Yu
Osmar Zaiane
Siva Reddy
Knowledge-grounded conversational models are known to suffer from producing factually invalid statements, a phenomenon commonly called hallu… (voir plus)cination. In this work, we investigate the underlying causes of this phenomenon: is hallucination due to the training data, or to the models? We conduct a comprehensive human study on both existing knowledge-grounded conversational benchmarks and several state-of-the-art models. Our study reveals that the standard benchmarks consist of >60% hallucinated responses, leading to models that not only hallucinate but even amplify hallucinations. Our findings raise important questions on the quality of existing datasets and models trained using them. We make our annotations publicly available for future research.
2021-12-31
Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (publié)
doi.org
arxiv.org
The Secret to Better AI and Better Software (Is Requirements Engineering)
Nelly Bencomo
Jin L.C. Guo
Rachel Harrison
Hans-Martin Heyn
Tim Menzies
Recently, practitioners and researchers met to discuss the role of requirements, and AI and SE. We offer here notes on that fascinating disc… (voir plus)ussion. Also, have you considered writing for this column? This “SE for AI” column publishes commentaries on the growing field of SE for AI. Submissions are welcomed and encouraged (1,000–2,400 words, each figure and table counts as 250 words, try to use fewer than 12 references, and keep the discussion practitioner focused). Please submit your ideas to me at timm@ieee.org.—Tim Menzies
2021-12-31
IEEE Software (publié)
doi.org
There is no fundamental trade-off between prediction accuracy and feature importance reliability
Jianzhong Chen
L.Q.R. Ooi
Jingwei Li
L. Christopher
Asplund
Simon B. Eickhoff
Danilo Bzdok
Avram J. Holmes
Blake T. Thomas
Yeo
There is significant interest in using neuroimaging data to predict behavior. The predictive models are often interpreted by the computation… (voir plus) of feature importance, which quantifies the predictive relevance of an imaging feature. Tian and Zalesky (2021) suggest that feature importance estimates exhibit low test-retest reliability, pointing to a potential trade-off between prediction accuracy and feature importance reliability. This trade-off is counter-intuitive because both prediction accuracy and test-retest reliability reflect the reliability of brain-behavior relationships across independent samples. Here, we revisit the relationship between prediction accuracy and feature importance reliability in a large well-powered dataset across a wide range of behavioral measures. We demonstrate that, with a sufficient sample size, feature importance (operationalized as Haufe-transformed weights) can achieve fair to excellent test-retest reliability. More specifically, with a sample size of about 2600 participants, Haufe-transformed weights achieve average intra-class correlation coefficients of 0.75, 0.57 and 0.53 for cognitive, personality and mental health measures respectively. Haufe-transformed weights are much more reliable than original regression weights and univariate FC-behavior correlations. Intriguingly, feature importance reliability is strongly positively correlated with prediction accuracy across phenotypes. Within a particular behavioral domain, there was no clear relationship between prediction performance and feature importance reliability across regression algorithms. Finally, we show mathematically that feature importance reliability is necessary, but not sufficient, for low feature importance error. In the case of linear models, lower feature importance error leads to lower prediction error (up to a scaling by the feature covariance matrix). Overall, we find no fundamental trade-off between feature importance reliability and prediction accuracy.
2021-12-31
(publié)
www.semanticscholar.org
Time cell encoding is decoupled from time perception in deep reinforcement learning agents
Ann Zixiang Huang
Dongyan Lin
Blake Richards
2021-12-31
Conference on Cognitive Computational Neuroscience (publié)
doi.org