Publications

Post-Editing Extractive Summaries by Definiteness Prediction
Jad Kabbara
Jackie Chi Kit Cheung
Extractive summarization has been the mainstay of automatic summarization for decades. Despite all the progress, extractive summarizers stil… (voir plus)l suffer from shortcomings including coreference issues arising from extracting sentences away from their original context in the source document. This affects the coherence and readability of extractive summaries. In this work, we propose a lightweight post-editing step for extractive summaries that centers around a single linguistic decision: the definiteness of noun phrases. We conduct human evaluation studies that show that human expert judges substantially prefer the output of our proposed system over the original summaries. Moreover, based on an automatic evaluation study, we provide evidence for our system’s ability to generate linguistic decisions that lead to improved extractive summaries. We also draw insights about how the automatic system is exploiting some local cues related to the writing style of the main article texts or summary texts to make the decisions, rather than reasoning about the contexts pragmatically.
2020-12-31
Conference on Empirical Methods in Natural Language Processing (publié)
doi.org
Preferential Temporal Difference Learning
Nishanth Anand
Doina Precup
Temporal-Difference (TD) learning is a general and very useful tool for estimating the value function of a given policy, which in turn is re… (voir plus)quired to find good policies. Generally speaking, TD learning updates states whenever they are visited. When the agent lands in a state, its value can be used to compute the TD-error, which is then propagated to other states. However, it may be interesting, when computing updates, to take into account other information than whether a state is visited or not. For example, some states might be more important than others (such as states which are frequently seen in a successful trajectory). Or, some states might have unreliable value estimates (for example, due to partial observability or lack of data), making their values less desirable as targets. We propose an approach to re-weighting states used in TD updates, both when they are the input and when they provide the target for the update. We prove that our approach converges with linear function approximation and illustrate its desirable empirical behaviour compared to other TD-style methods.
2020-12-31
ICML (publié)
doi.org
proceedings.mlr.press
Pretraining Representations for Data-Efficient Reinforcement Learning
Max Schwarzer
Nitarshan Rajkumar
Michael Noukhovitch
Ankesh Anand
Laurent Charlin
Devon Hjelm
Philip Bachman
Aaron Courville
Data efficiency is a key challenge for deep reinforcement learning. We address this problem by using unlabeled data to pretrain an encoder w… (voir plus)hich is then finetuned on a small amount of task-specific data. To encourage learning representations which capture diverse aspects of the underlying MDP, we employ a combination of latent dynamics modelling and unsupervised goal-conditioned RL. When limited to 100k steps of interaction on Atari games (equivalent to two hours of human experience), our approach significantly surpasses prior work combining offline representation pretraining with task-specific finetuning, and compares favourably with other pretraining methods that require orders of magnitude more data. Our approach shows particular promise when combined with larger models as well as more diverse, task-aligned observational data -- approaching human-level performance and data-efficiency on Atari in our best setting. We provide code associated with this work at https://github.com/mila-iqia/SGI.
2020-12-31
Advances in Neural Information Processing Systems 34 (NeurIPS 2021) (publié)
doi.org
openreview.net
RAFFIC V IS : Fighting Human Trafficking through Visualization
Catalina Vajiac
Andreas Olligschlaeger
Yifei Li
Pratheeksha Nair
Meng-Chieh Lee
Namyong Park
Reihaneh Rabbany
Duen Horng Chau
Christos Faloutsos
Law enforcement can detect human trafficking (HT) in online escort websites by analyzing suspicious clusters of connected ads. Given such cl… (voir plus)usters, how can we interactively visualize potential evidence for law enforcement and domain experts? We present TRAFFICVIS, which, to our knowledge, is the first interface for cluster-level HT detection and labeling. It builds on state-of-the-art HT clustering algorithms by incorporating metadata as a signal of organized and potentially suspicious activity. Also, domain experts can label clusters as HT, spam, and more, efficiently creating labeled datasets to enable further HT research. TRAFFICVIS has been built in close collaboration with domain experts, who estimate that TRAFFICVIS provides a median 36x speedup over manual labeling.
2020-12-31
(publié)
www.semanticscholar.org
Randomized Least Squares Policy Optimization
Haque Ishfaq
Zhuoran Yang
Andrei Lupu
Viet Bang Nguyen
Lewis Liu
Riashat Islam
Zhaoran Wang
Doina Precup
Policy Optimization (PO) methods with function approximation are one of the most popular classes of Reinforcement Learning (RL) algorithms. … (voir plus)However, designing provably efficient policy optimization algorithms remains a challenge. Recent work in this area has focused on incorporating upper confidence bound (UCB)-style bonuses to drive exploration in policy optimization. In this paper, we present Randomized Least Squares Policy Optimization (RLSPO) which is inspired by Thompson Sampling. We prove that, in an episodic linear kernel MDP setting, RLSPO achieves (cid:101) O ( d 3 / 2 H 3 / 2 √ T ) worst-case (frequentist) regret, where H is the number of episodes, T is the total number of steps and d is the feature dimension. Finally, we evaluate RLSPO empirically and show that it is competitive with existing provably efficient PO algorithms.
2020-12-31
(publié)
www.semanticscholar.org
A relaxed technical assumption for posterior sampling-based reinforcement learning for control of unknown linear systems
Mukul Gagrani
Sagar Sudhakara
Aditya Mahajan
Ashutosh Nayyar
Yi Ouyang
—We revisit the Thompson sampling algorithm to control an unknown linear quadratic (LQ) system recently proposed by Ouyang et al. [1]. The… (voir plus) regret bound of the algorithm was derived under a technical assumption on the induced norm of the closed loop system. In this technical note, we show that by making a minor modification in the algorithm (in particular, ensuring that an episode does not end too soon), this technical assumption on the induced norm can be replaced by a milder assumption in terms of the spectral radius of the closed loop system. The modified algorithm has the same Bayesian regret of ˜ O ( √ T ) , where T is the time-horizon and the ˜ O ( · ) notation hides logarithmic terms in T .
2020-12-31
arXiv.org (prépublication)
dblp.uni-trier.de
Rethinking Graph Transformers with Spectral Attention
Devin Kreuzer
Dominique Beaini
William L. Hamilton
Vincent Létourneau
Prudencio Tossou
In recent years, the Transformer architecture has proven to be very successful in sequence processing, but its application to other data str… (voir plus)uctures, such as graphs, has remained limited due to the difficulty of properly defining positions. Here, we present the
2020-12-31
Advances in Neural Information Processing Systems 34 (NeurIPS 2021) (publié)
doi.org
openreview.net
Routine Bandits: Minimizing Regret on Recurring Problems
Hassan Saber
L'eo Saci
Odalric-Ambrym Maillard
Audrey Durand
2020-12-31
ECML/PKDD (publié)
doi.org
Scalable Change Point Detection for Dynamic Graphs
Shenyang Huang
Guillaume Rabusseau
Reihaneh Rabbany
Real world networks often evolve in complex ways over time. Understanding anomalies in dynamic networks is crucial for applications such as … (voir plus)traffic accident detection, intrusion identification and detection of ecosystem disturbances. In this work, we focus on the problem of change point detection in dynamic graphs. The goal is to identify time steps where the graph structure deviates significantly from the norm. Despite empirical success of recent methods, building a change point detection method for real world dynamic graphs, which often scale to millions of nodes, remains an open question. To fill this gap, we propose LADdos, a scalable method for change point detection in dynamic graphs. LADdos brings together ideas from two recent works: an accurate change point detection method for graphs called LAD [10] which detects the changes in the full Laplacian spectrum of the graph in each timestamp, and the general framework of network density of states (DOS) [5] which models the distribution of the singular values through efficient approximation methods. In experiments with two common graph models –the Stochastic Block Model (SBM) and the Barabási-Albert (BA) model – we show that LADdos has equal performance to LAD, which is the current state-of-the-art, while being orders of magnitude faster. For instance, on a dynamic graph with total 21 million edges over 150 timestamps, LADdos achieves 100x speedup when compared to LAD.
2020-12-31
(publié)
www.semanticscholar.org
Seeing things or seeing scenes: Investigating the capabilities of V&L models to align scene descriptions to images
M. D. Anderson
Erich W Graf
James H Elder
Peter Anderson
Xiaodong He
Chris Buehler
Mark Teney
Stephen Johnson
Gould Lei
Emily M. Bender
Timnit Gebru
Angelina McMillan-575
Alexander Koller. 2020
Climb-582
Yonatan Bisk
Ari Holtzman
Jesse Thomason
Yoshua Bengio
Joyce Chai
Angeliki Lazaridou … (voir 32 de plus)
Jonathan May
Aleksandr
Thomas Unterthiner
Mostafa Dehghani
Georg Minderer
Sylvain Heigold
Jakob Gelly
Uszkoreit Neil
Houlsby. 2020
An
Lisa Anne Hendricks
Gabriel Ilharco
Rowan Zellers
Ali Farhadi
John M. Henderson
Contextual
Thomas L. Griffiths. 2021
Are Convolutional
Neu-827
Melissa L.-H. Võ
Jeremy M. Wolfe
Differen-830
Jianfeng Wang
Xiaowei Hu
Xiu-834 Pengchuan Zhang
Roy Schwartz
Bolei Zhou
Àgata Lapedriza
Jianxiong Xiao
Hang Zhao
Xavier Puig
Sanja Fidler
Images can be described in terms of the objects 001 they contain, or in terms of the types of scene 002 or place that they instantiate. In t… (voir plus)his paper we 003 address to what extent pretrained Vision and 004 Language models can learn to align descrip-005 tions of both types with images. We com-006 pare 3 state-of-the-art models, VisualBERT, 007 LXMERT and CLIP. We find that (i) V&L 008 models are susceptible to stylistic biases ac-009 quired during pretraining; (ii) only CLIP per-010 forms consistently well on both object-and 011 scene-level descriptions. A follow-up ablation 012 study shows that CLIP uses object-level infor-013 mation in the visual modality to align with 014 scene-level textual descriptions
2020-12-31
(publié)
www.semanticscholar.org
Signal diffusion along connectome gradients and inter-hub routing differentially contribute to dynamic human brain function
Bo-yong Park
Reinder Vos de Wael
Casey Paquola
Sara Larivière
Oualid Benkarim
Jessica Royer
Shahin Tavakol
Raul R. Cruces
Qiongling Li
Sofie L. Valk
Daniel S. Margulies
Bratislav Misic
Danilo Bzdok
Jonathan Smallwood
Boris C. Bernhardt
2020-12-31
NeuroImage (publié)
doi.org
A Simple and Effective Model for Multi-Hop Question Generation
Jimmy Lei Ba
Jamie Ryan Kiros
Geoffrey E Hin-602
Peter W. Battaglia
Jessica Blake
Chandler Hamrick
Vic-613 tor Bapst
Alvaro Sanchez
Vinicius Zambaldi
M. Malinowski
Andrea Tacchetti
David Raposo
Tom B. Brown
Benjamin Mann
Nick Ryder
Melanie Subbiah
Jared Kaplan
Prafulla Dhariwal
Arvind Neelakantan
Pranav Shyam … (voir 72 de plus)
Girish Sastry
Koustuv Sinha
Shagun Sodhani
Jin Dong
Joelle Pineau
William L. Hamilton
Clutrr
Nitish Srivastava
Geoffrey Hinton
Alex Krizhevsky
Ilya Sutskever
Ruslan Salakhutdinov. 2014
Gabriel Stanovsky
Julian Michael
Luke Zettlemoyer
Dan Su
Yan Xu
Wenliang Dai
Ziwei Ji
Tiezheng Yu
Minghao Tu
Kevin Huang
Guangtao Wang
Jing Huang
Ashish Vaswani
Noam M. Shazeer
Niki Parmar
Jakob Uszkoreit
Llion Jones
Aidan N. Gomez
Łukasz Kaiser
Illia Polosukhin. 2017
Attention
Petar Veliˇckovi´c
Guillem Cucurull
Arantxa Casanova
Adriana Romero
Pietro Lio’
Yoshua Bengio
Johannes Welbl
Pontus Stenetorp
Yonghui Wu
Mike Schuster
Quoc Zhifeng Chen
Mohammad Le
Wolfgang Norouzi
Macherey
M. Krikun
Yuan Cao
Qin Gao
William W. Cohen
Jianxing Yu
Xiaojun Quan
Qinliang Su
Jian Yin
Yuyu Zhang
Hanjun Dai
Zornitsa Kozareva
Cheng Zhao
Chenyan Xiong
Corby Rosset
Xia
Paul Song
Bennett Saurabh
Tiwary
Yao Zhao
Xiaochuan Ni
Yuanyuan Ding
Qingyu Zhou
Nan Yang
Furu Wei
Chuanqi Tan
Previous research on automated question gen-001 eration has almost exclusively focused on gen-002 erating factoid questions whose answers ca… (voir plus)n 003 be extracted from a single document. How-004 ever, there is an increasing interest in develop-005 ing systems that are capable of more complex 006 multi-hop question generation (QG), where an-007 swering the question requires reasoning over 008 multiple documents. In this work, we pro-009 pose a simple and effective approach based on 010 the transformer model for multi-hop QG. Our 011 approach consists of specialized input repre-012 sentations, a supporting sentence classification 013 objective, and training data weighting. Prior 014 work on multi-hop QG considers the simpli-015 fied setting of shorter documents and also ad-016 vocates the use of entity-based graph struc-017 tures as essential ingredients in model design. 018 On the contrary, we showcase that our model 019 can scale to the challenging setting of longer 020 documents as input, does not rely on graph 021 structures, and substantially outperforms the 022 state-of-the-art approaches as measured by au-023 tomated metrics and human evaluation. 024
2020-12-31
(publié)
www.semanticscholar.org