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… (voir plus)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.
2019-10-14
arXiv.org (prépublication)
dblp.uni-trier.de
Improving Pathological Structure Segmentation via Transfer Learning Across Diseases
Barleen Kaur
Paul Lemaitre
Raghav Mehta
Nazanin Mohammadi Sepahvand
Doina Precup
Douglas Arnold
Tal Arbel
2019-10-13
Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data (publié)
doi.org
Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation
S. Meng
Sharan Vaswani
Issam Hadj Laradji
Mark Schmidt
Simon Lacoste-Julien
We consider stochastic second-order methods for minimizing smooth and strongly-convex functions under an interpolation condition satisfied b… (voir plus)y over-parameterized models. Under this condition, we show that the regularized subsampled Newton method (R-SSN) achieves global linear convergence with an adaptive step-size and a constant batch-size. By growing the batch size for both the subsampled gradient and Hessian, we show that R-SSN can converge at a quadratic rate in a local neighbourhood of the solution. We also show that R-SSN attains local linear convergence for the family of self-concordant functions. Furthermore, we analyze stochastic BFGS algorithms in the interpolation setting and prove their global linear convergence. We empirically evaluate stochastic L-BFGS and a "Hessian-free" implementation of R-SSN for binary classification on synthetic, linearly-separable datasets and real datasets under a kernel mapping. Our experimental results demonstrate the fast convergence of these methods, both in terms of the number of iterations and wall-clock time.
2019-10-11
ArXiv (preprint)
arxiv.org
arxiv.org
Old Dog Learns New Tricks: Randomized UCB for Bandit Problems
Sharan Vaswani
Abbas Mehrabian
Audrey Durand
Branislav Kveton
We propose …
2019-10-11
ArXiv (preprint)
arxiv.org
arxiv.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
2019-10-10
Lecture Notes in Computer Science (publié)
doi.org
arxiv.org
MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis
Kundan Kumar
Rithesh Kumar
Thibault De Boissière
Lucas Gestin
Wei Zhen Teoh
Jose Sotelo
Alexandre De Brébisson
Yoshua Bengio
Aaron Courville
Previous works (Donahue et al., 2018a; Engel et al., 2019a) have found that generating coherent raw audio waveforms with GANs is challenging… (voir plus). In this paper, we show that it is possible to train GANs reliably to generate high quality coherent waveforms by introducing a set of architectural changes and simple training techniques. Subjective evaluation metric (Mean Opinion Score, or MOS) shows the effectiveness of the proposed approach for high quality mel-spectrogram inversion. To establish the generality of the proposed techniques, we show qualitative results of our model in speech synthesis, music domain translation and unconditional music synthesis. We evaluate the various components of the model through ablation studies and suggest a set of guidelines to design general purpose discriminators and generators for conditional sequence synthesis tasks. Our model is non-autoregressive, fully convolutional, with significantly fewer parameters than competing models and generalizes to unseen speakers for mel-spectrogram inversion. Our pytorch implementation runs at more than 100x faster than realtime on GTX 1080Ti GPU and more than 2x faster than real-time on CPU, without any hardware specific optimization tricks.
2019-10-08
ArXiv (prépublication)
arxiv.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 … (voir plus)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.
2019-10-02
NeurIPS.cc/2019/Workshop/Neuro_AI (poster)
openreview.net
openreview.net
Depth with Nonlinearity Creates No Bad Local Minima in ResNets
Kenji Kawaguchi
Yoshua Bengio
2019-10-01
Neural Networks (publié)
doi.org
arxiv.org
Evaluation of a web-based tool for labelling potential hospital outbreaks: a mixed methods study
B. Leclère
David Buckeridge
D. Lepelletier
2019-10-01
Journal of Hospital Infection (publié)
doi.org
Patterns of autism symptoms: hidden structure in the ADOS and ADI-R instruments
Jeremy Lefort-Besnard
Kai Vogeley
Leonhard Schilbach
Gael Varoquaux
Bertrand Thirion
Guillaume Dumas
Danilo Bzdok
2019-09-27
Translational Psychiatry (publié)
doi.org
Avoidance Learning Using Observational Reinforcement Learning
David Venuto
Léonard Boussioux
Junhao Wang
Rola Dali
Jhelum Chakravorty
Yoshua Bengio
Doina Precup
Imitation learning seeks to learn an expert policy from sampled demonstrations. However, in the real world, it is often difficult to find a … (voir plus)perfect expert and avoiding dangerous behaviors becomes relevant for safety reasons. We present the idea of \textit{learning to avoid}, an objective opposite to imitation learning in some sense, where an agent learns to avoid a demonstrator policy given an environment. We define avoidance learning as the process of optimizing the agent's reward while avoiding dangerous behaviors given by a demonstrator. In this work we develop a framework of avoidance learning by defining a suitable objective function for these problems which involves the \emph{distance} of state occupancy distributions of the expert and demonstrator policies. We use density estimates for state occupancy measures and use the aforementioned distance as the reward bonus for avoiding the demonstrator. We validate our theory with experiments using a wide range of partially observable environments. Experimental results show that we are able to improve sample efficiency during training compared to state of the art policy optimization and safety methods.
2019-09-24
ArXiv (prépublication)
arxiv.org
arxiv.org
Learning Sparse Mixture of Experts for Visual Question Answering
Vardaan Pahuja
Jie Fu
Chris Pal
2019-09-19
ArXiv (prépublication)
arxiv.org
arxiv.org