Publications

MICo: Learning improved representations via sampling-based state similarity for Markov decision processes
Pablo Samuel Castro
Tyler Kastner
Prakash Panangaden
Mark Rowland
We present a new behavioural distance over the state space of a Markov decision process, and demonstrate the use of this distance as an eff… (see more)ective means of shaping the learnt representations of deep reinforcement learning agents. While existing notions of state similarity are typically difficult to learn at scale due to high computational cost and lack of sample-based algorithms, our newly-proposed distance addresses both of these issues. In addition to providing detailed theoretical analysis
2021-01-01
arXiv.org (preprint)
dblp.uni-trier.de
Multi-Agent Estimation and Filtering for Minimizing Team Mean-Squared Error
Mohammad Afshari
Aditya Mahajan
Motivated by estimation problems arising in autonomous vehicles and decentralized control of unmanned aerial vehicles, we consider multi-age… (see more)nt estimation and filtering problems in which multiple agents generate state estimates based on decentralized information and the objective is to minimize a coupled mean-squared error which we call team mean-square error. We call the resulting estimates as minimum team mean-squared error (MTMSE) estimates. We show that MTMSE estimates are different from minimum mean-squared error (MMSE) estimates. We derive closed-form expressions for MTMSE estimates, which are linear function of the observations where the corresponding gain depends on the weight matrix that couples the estimation error. We then consider a filtering problem where a linear stochastic process is monitored by multiple agents which can share their observations (with delay) over a communication graph. We derive expressions to recursively compute the MTMSE estimates. To illustrate the effectiveness of the proposed scheme we consider an example of estimating the distances between vehicles in a platoon and show that MTMSE estimates significantly outperform MMSE estimates and consensus Kalman filtering estimates.
2021-01-01
IEEE Transactions on Signal Processing (published)
doi.org
arxiv.org
Multilevel Approaches for the Critical Node Problem
Andrea Baggio
Margarida Carvalho
Andrea Lodi
Andrea Tramontani
2021-01-01
Operational Research (published)
doi.org
A Novel Neural Network-Based Malware Severity Classification System
Miles Q. Li
Benjamin Fung
2021-01-01
International Conference on Software and Data Technologies (published)
doi.org
A Novel Neural Network-Based Malware Severity Classification System
Miles Q. Li
Benjamin Fung
2021-01-01
International Conference on Software and Data Technologies (published)
doi.org
On-the-Fly Attention Modularization for Neural Generation
Yue Dong
Chandra Bhagavatula
Ximing Lu
Jena D. Hwang
Antoine Bosselut
Jackie Cheung
Yejin Choi
Despite considerable advancements with deep neural language models (LMs), neural text generation still suffers from de generation: generated… (see more) text is repetitive, generic, self-inconsistent, and lacking commonsense. The empirical analyses on sentence-level attention patterns reveal that neural text degeneration may be associated with insufficient learning of inductive biases by the attention mechanism. Our findings motivate on-the-fly attention modularization, a simple but effective method for injecting inductive biases into attention computation during inference. The resulting text produced by the language model with attention modularization can yield enhanced diversity and commonsense reasoning while maintaining fluency and coherence.
2021-01-01
arXiv.org (preprint)
dblp.uni-trier.de
Overview of the TREC 2021 Fair Ranking Track
Asia J. Biega
Fernando Diaz
Michael D. Ekstrand
Sebastian Kohlmeier
The TREC Fair Ranking Track aims to provide a platform for participants to develop and evaluate novel retrieval algorithms that can provide … (see more)a fair exposure to a mixture of demographics or attributes, such as ethnicity, that are represented by relevant documents in response to a search query. For example, particular demographics or attributes can be represented by the documents' topical content or authors. The 2021 Fair Ranking Track adopted a resource allocation task. The task focused on supporting Wikipedia editors who are looking to improve the encyclopedia's coverage of topics under the purview of a WikiProject. WikiProject coordinators and/or Wikipedia editors search for Wikipedia documents that are in need of editing to improve the quality of the article. The 2021 Fair Ranking track aimed to ensure that documents that are about, or somehow represent, certain protected characteristics receive a fair exposure to the Wikipedia editors, so that the documents have an fair opportunity of being improved and, therefore, be well-represented in Wikipedia. The under-representation of particular protected characteristics in Wikipedia can result in systematic biases that can have a negative human, social, and economic impact, particularly for disadvantaged or protected societal groups.
2021-01-01
TREC (published)
doi.org
arxiv.org
Personalized Medicine for OSA Syndrome in a Nutshell: Conceptual Clarification for Integration.
Christophe Gauld
Marie Darrason
Guillaume Dumas
Jean‐Arthur Micoulaud‐Franchi
2021-01-01
Chest (published)
doi.org
Post-Editing Extractive Summaries by Definiteness Prediction
Jad Kabbara
Jackie Cheung
Extractive summarization has been the main-stay of automatic summarization for decades. Despite all the progress, extractive summarizers sti… (see more)ll 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 postediting 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.
2021-01-01
Conference on Empirical Methods in Natural Language Processing (published)
doi.org
Pretraining Representations for Data-Efficient Reinforcement Learning
Max Schwarzer
Nitarshan Rajkumar
Michael Noukhovitch
Ankesh Anand
Laurent Charlin
(Rex) 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… (see more)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.
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… (see more)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.
2021-01-01
(published)
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… (see more) 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 .
2021-01-01
arXiv.org (preprint)
dblp.uni-trier.de