Publications

Nash Games Among Stackelberg Leaders
Margarida Carvalho
Gabriele Dragotto
Felipe Feijoo
Andrea Lodi
Sriram Sankaranarayanan
We analyze Nash games played among leaders of Stackelberg games (NASP). We show it is Σ p 2 - hard to decide if the game has a mixed-strate… (see more)gy Nash equilibrium (MNE), even when there are only two leaders and each leader has one follower. We provide a finite time algorithm with a running time bounded by O (2 2 n ) which computes MNEs for NASP when it exists and returns infeasibility if no MNE exists. We also provide two ways to improve the algorithm which involves constructing a series of inner approximations (alternatively, outer approximations) to the leaders’ feasible region that will provably obtain the required MNE. Finally, we test our algorithms on a range of NASPs arising out of a game in the energy market, where countries act as Stackelberg leaders who play a Nash game, and the domestic producers act as the followers.
2019-10-13
arXiv.org (preprint)
dblp.uni-trier.de
Early Prediction of Alzheimer's Disease Progression Using Variational Autoencoders
Sumana Basu
Konrad Wagstyl
Azar Zandifar
D. Collins
Adriana Romero
Doina Precup
2019-10-12
International Conference on Medical Image Computing and Computer-Assisted Intervention (published)
doi.org
Improving Pathological Structure Segmentation via Transfer Learning Across Diseases
Barleen Kaur
Paul Lemaitre
Raghav Mehta
Nazanin Mohammadi Sepahvand
Doina Precup
Douglas Arnold
Tal Arbel
2019-10-12
Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data (published)
doi.org
InfoMask: Masked Variational Latent Representation to Localize Chest Disease
Saeid Asgari Taghanaki
Mohammad Havaei
Tess Berthier
Francis Dutil
Lisa Di Jorio
Ghassan Hamarneh
Yoshua Bengio
2019-10-09
Lecture Notes in Computer Science (published)
doi.org
arxiv.org
VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering
Cătălina Cangea
Eugene Belilovsky
Pietro Lio
Aaron Courville
Embodied Question Answering (EQA) is a recently proposed task, where an agent is placed in a rich 3D environment and must act based solely o… (see more)n its egocentric input to answer a given question. The desired outcome is that the agent learns to combine capabilities such as scene understanding, navigation and language understanding in order to perform complex reasoning in the visual world. However, initial advancements combining standard vision and language methods with imitation and reinforcement learning algorithms have shown EQA might be too complex and challenging for these techniques. In order to investigate the feasibility of EQA-type tasks, we build the VideoNavQA dataset that contains pairs of questions and videos generated in the House3D environment. The goal of this dataset is to assess question-answering performance from nearly-ideal navigation paths, while considering a much more complete variety of questions than current instantiations of the EQA task. We investigate several models, adapted from popular VQA methods, on this new benchmark. This establishes an initial understanding of how well VQA-style methods can perform within this novel EQA paradigm.
2019-10-02
Apollo - University of Cambridge Repository (published)
doi.org
arxiv.org
Reinforcement Learning Models of Human Behavior: Reward Processing in Mental Disorders
Baihan Lin
Guillermo Cecchi
Djallel Bouneffouf
Jenna Reinen
Irina Rish
Drawing an inspiration from behavioral studies of human decision making, we propose here a general parametric framework for a reinforcement … (see more)learning problem, which extends the standard Q-learning approach to incorporate a two-stream framework of reward processing with biases biologically associated with several neurological and psychiatric conditions, including Parkinson's and Alzheimer's diseases, attention-deficit/hyperactivity disorder (ADHD), addiction, and chronic pain. For the AI community, the development of agents that react differently to different types of rewards can enable us to understand a wide spectrum of multi-agent interactions in complex real-world socioeconomic systems. Empirically, the proposed model outperforms Q-Learning and Double Q-Learning in artificial scenarios with certain reward distributions and real-world human decision making gambling tasks. Moreover, from the behavioral modeling perspective, our parametric framework can be viewed as a first step towards a unifying computational model capturing reward processing abnormalities across multiple mental conditions and user preferences in long-term recommendation systems.
2019-10-01
NeurIPS.cc/2019/Workshop/Neuro_AI (poster)
openreview.net
openreview.net
Augmenting learning using symmetry in a biologically-inspired domain
Shruti Mishra
Abbas Abdolmaleki
Arthur Guez
Piotr Trochim
Doina Precup
Invariances to translation, rotation and other spatial transformations are a hallmark of the laws of motion, and have widespread use in the … (see more)natural sciences to reduce the dimensionality of systems of equations. In supervised learning, such as in image classification tasks, rotation, translation and scale invariances are used to augment training datasets. In this work, we use data augmentation in a similar way, exploiting symmetry in the quadruped domain of the DeepMind control suite (Tassa et al. 2018) to add to the trajectories experienced by the actor in the actor-critic algorithm of Abdolmaleki et al. (2018). In a data-limited regime, the agent using a set of experiences augmented through symmetry is able to learn faster. Our approach can be used to inject knowledge of invariances in the domain and task to augment learning in robots, and more generally, to speed up learning in realistic robotics applications.
2019-09-30
ArXiv (preprint)
arxiv.org
arxiv.org
Depth with Nonlinearity Creates No Bad Local Minima in ResNets
Kenji Kawaguchi
Yoshua Bengio
2019-09-30
Neural Networks (published)
doi.org
arxiv.org
Evaluation of a web-based tool for labelling potential hospital outbreaks: a mixed methods study
B. Leclère
David L Buckeridge
D. Lepelletier
2019-09-30
Journal of Hospital Infection (published)
doi.org
Learning Fixed Points in Generative Adversarial Networks: From Image-to-Image Translation to Disease Detection and Localization
Md Mahfuzur Rahman Siddiquee
Zongwei Zhou
Nima Tajbakhsh
Ruibin Feng
Michael B. Gotway
Yoshua Bengio
Jianming Liang
Generative adversarial networks (GANs) have ushered in a revolution in image-to-image translation. The development and proliferation of GANs… (see more) raises an interesting question: can we train a GAN to remove an object, if present, from an image while otherwise preserving the image? Specifically, can a GAN "virtually heal" anyone by turning his medical image, with an unknown health status (diseased or healthy), into a healthy one, so that diseased regions could be revealed by subtracting those two images? Such a task requires a GAN to identify a minimal subset of target pixels for domain translation, an ability that we call fixed-point translation, which no GAN is equipped with yet. Therefore, we propose a new GAN, called Fixed-Point GAN, trained by (1) supervising same-domain translation through a conditional identity loss, and (2) regularizing cross-domain translation through revised adversarial, domain classification, and cycle consistency loss. Based on fixed-point translation, we further derive a novel framework for disease detection and localization using only image-level annotation. Qualitative and quantitative evaluations demonstrate that the proposed method outperforms the state of the art in multi-domain image-to-image translation and that it surpasses predominant weakly-supervised localization methods in both disease detection and localization. Implementation is available at https://github.com/jlianglab/Fixed-Point-GAN.
2019-09-30
2019 IEEE/CVF International Conference on Computer Vision (ICCV) (published)
doi.org
arxiv.org
{COMPANYNAME}11K: An Unsupervised Representation Learning Dataset for Arrhythmia Subtype Discovery
Shawn Tan
Guillaume Androz
Ahmad Chamseddine
Pierre Fecteau
Aaron Courville
Yoshua Bengio
Joseph Paul Cohen
2019-09-24
(published)
openreview.net
openreview.net
Equivariant Entity-Relationship Networks
Devon Graham
Siamak Ravanbakhsh
The relational model is a ubiquitous representation of big-data, in part due to its extensive use in databases. In this paper, we propose th… (see more)e Equivariant Entity-Relationship Network (EERN), which is a Multilayer Perceptron equivariant to the symmetry transformations of the Entity-Relationship model. To this end, we identify the most expressive family of linear maps that are exactly equivariant to entity relationship symmetries, and further show that they subsume recently introduced equivariant maps for sets, exchangeable tensors, and graphs. The proposed feed-forward layer has linear complexity in the data and can be used for both inductive and transductive reasoning about relational databases, including database embedding, and the prediction of missing records. This provides a principled theoretical foundation for the application of deep learning to one of the most abundant forms of data. Empirically, EERN outperforms different variants of coupled matrix tensor factorization in both synthetic and real-data experiments.
2019-09-24
(published)
openreview.net
openreview.net