Publications

Deep Discourse Analysis for Generating Personalized Feedback in Intelligent Tutor Systems
Matt Grenander
Robert Belfer
Ekaterina Kochmar
Iulian V. Serban
Franccois St-Hilaire
Jackie CK Cheung
We explore creating automated, personalized feedback in an intelligent tutoring system (ITS). Our goal is to pinpoint correct and incorrect … (see more)concepts in student answers in order to achieve better student learning gains. Although automatic methods for providing personalized feedback exist, they do not explicitly inform students about which concepts in their answers are correct or incorrect. Our approach involves decomposing students answers using neural discourse segmentation and classification techniques. This decomposition yields a relational graph over all discourse units covered by the reference solutions and student answers. We use this inferred relational graph structure and a neural classifier to match student answers with reference solutions and generate personalized feedback. Although the process is completely automated and data-driven, the personalized feedback generated is highly contextual, domain-aware and effectively targets each student's misconceptions and knowledge gaps. We test our method in a dialogue-based ITS and demonstrate that our approach results in high-quality feedback and significantly improved student learning gains.
2021-05-17
Proceedings of the AAAI Conference on Artificial Intelligence (published)
doi.org
arxiv.org
Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models
Tong Che
Xiaofeng Liu
Site Li
Yubin Ge
Ruixiang Zhang
Caiming Xiong
Yoshua Bengio
AI Safety is a major concern in many deep learning applications such as autonomous driving. Given a trained deep learning model, an importan… (see more)t natural problem is how to reliably verify the model's prediction. In this paper, we propose a novel framework --- deep verifier networks (DVN) to detect unreliable inputs or predictions of deep discriminative models, using separately trained deep generative models. Our proposed model is based on conditional variational auto-encoders with disentanglement constraints to separate the label information from the latent representation. We give both intuitive and theoretical justifications for the model. Our verifier network is trained independently with the prediction model, which eliminates the need of retraining the verifier network for a new model. We test the verifier network on both out-of-distribution detection and adversarial example detection problems, as well as anomaly detection problems in structured prediction tasks such as image caption generation. We achieve state-of-the-art results in all of these problems.
2021-05-17
Proceedings of the AAAI Conference on Artificial Intelligence (published)
doi.org
arxiv.org
DIBS: Diversity inducing Information Bottleneck in Model Ensembles
Samarth Sinha
Homanga Bharadhwaj
Anirudh Goyal
Hugo Larochelle
Animesh Garg
Florian Shkurti
2021-05-17
Proceedings of the AAAI Conference on Artificial Intelligence (published)
doi.org
arxiv.org
Individual Fairness in Kidney Exchange Programs
Golnoosh Farnadi
William St-Arnaud
Behrouz Babaki
Margarida Carvalho
Kidney transplant is the preferred method of treatment for patients suffering from kidney failure. However, not all patients can find a dono… (see more)r which matches their physiological characteristics. Kidney exchange programs (KEPs) seek to match such incompatible patient-donor pairs together, usually with the main objective of maximizing the total number of transplants. Since selecting one optimal solution translates to a decision on who receives a transplant, it has a major effect on the lives of patients. The current practice in selecting an optimal solution does not necessarily ensure fairness in the selection process. In this paper, the existence of multiple optimal plans for a KEP is explored as a mean to achieve individual fairness. We propose the use of randomized policies for selecting an optimal solution in which patients' equal opportunity to receive a transplant is promoted. Our approach gives rise to the problem of enumerating all optimal solutions, which we tackle using a hybrid of constraint programming and linear programming. The advantages of our proposed method over the common practice of using the optimal solution obtained by a solver are stressed through computational experiments. Our methodology enables decision makers to fully control KEP outcomes, overcoming any potential bias or vulnerability intrinsic to a deterministic solver.
2021-05-17
AAAI Conference on Artificial Intelligence (published)
doi.org
Learning Intuitive Physics with Multimodal Generative Models
Sahand Rezaei-Shoshtari
Francois Hogan
M. Jenkin
David Meger
Gregory Dudek
Predicting the future interaction of objects when they come into contact with their environment is key for autonomous agents to take intelli… (see more)gent and anticipatory actions. This paper presents a perception framework that fuses visual and tactile feedback to make predictions about the expected motion of objects in dynamic scenes. Visual information captures object properties such as 3D shape and location, while tactile information provides critical cues about interaction forces and resulting object motion when it makes contact with the environment. Utilizing a novel See-Through-your-Skin (STS) sensor that provides high resolution multimodal sensing of contact surfaces, our system captures both the visual appearance and the tactile properties of objects. We interpret the dual stream signals from the sensor using a Multimodal Variational Autoencoder (MVAE), allowing us to capture both modalities of contacting objects and to develop a mapping from visual to tactile interaction and vice-versa. Additionally, the perceptual system can be used to infer the outcome of future physical interactions, which we validate through simulated and real-world experiments in which the resting state of an object is predicted from given initial conditions.
2021-05-17
Proceedings of the AAAI Conference on Artificial Intelligence (published)
doi.org
arxiv.org
Meta-learning framework with applications to zero-shot time-series forecasting
Boris Oreshkin
Dmitri Carpov
Nicolas Chapados
Yoshua Bengio
Can meta-learning discover generic ways of processing time series (TS) from a diverse dataset so as to greatly improve generalization on new… (see more) TS coming from different datasets? This work provides positive evidence to this using a broad meta-learning framework which we show subsumes many existing meta-learning algorithms. Our theoretical analysis suggests that residual connections act as a meta-learning adaptation mechanism, generating a subset of task-specific parameters based on a given TS input, thus gradually expanding the expressive power of the architecture on-the-fly. The same mechanism is shown via linearization analysis to have the interpretation of a sequential update of the final linear layer. Our empirical results on a wide range of data emphasize the importance of the identified meta-learning mechanisms for successful zero-shot univariate forecasting, suggesting that it is viable to train a neural network on a source TS dataset and deploy it on a different target TS dataset without retraining, resulting in performance that is at least as good as that of state-of-practice univariate forecasting models.
2021-05-17
Proceedings of the AAAI Conference on Artificial Intelligence (published)
doi.org
arxiv.org
Metrics and continuity in reinforcement learning
Charline Le Lan
Bellemare Marc-Emmanuel
Pablo Samuel Castro
2021-05-17
Proceedings of the AAAI Conference on Artificial Intelligence (published)
doi.org
arxiv.org
Object-Centric Image Generation from Layouts
Tristan Sylvain
Pengchuan Zhang
Yoshua Bengio
R Devon Hjelm
Shikhar Sharma
2021-05-17
Proceedings of the AAAI Conference on Artificial Intelligence (published)
doi.org
arxiv.org
Parameterizing Branch-and-Bound Search Trees to Learn Branching Policies
Giulia Zarpellon
Jason Jo
Andrea Lodi
Yoshua Bengio
Branch and Bound (B&B) is the exact tree search method typically used to solve Mixed-Integer Linear Programming problems (MILPs). Learni… (see more)ng branching policies for MILP has become an active research area, with most works proposing to imitate the strong branching rule and specialize it to distinct classes of problems. We aim instead at learning a policy that generalizes across heterogeneous MILPs: our main hypothesis is that parameterizing the state of the B&B search tree can aid this type of generalization. We propose a novel imitation learning framework, and introduce new input features and architectures to represent branching. Experiments on MILP benchmark instances clearly show the advantages of incorporating an explicit parameterization of the state of the search tree to modulate the branching decisions, in terms of both higher accuracy and smaller B&B trees. The resulting policies significantly outperform the current state-of-the-art method for "learning to branch" by effectively allowing generalization to generic unseen instances.
2021-05-17
Proceedings of the AAAI Conference on Artificial Intelligence (published)
doi.org
arxiv.org
Self-Supervised Attention-Aware Reinforcement Learning
Haiping Wu
Khimya Khetarpal
Doina Precup
Visual saliency has emerged as a major visualization tool for interpreting deep reinforcement learning (RL) agents. However, much of the exi… (see more)sting research uses it as an analyzing tool rather than an inductive bias for policy learning. In this work, we use visual attention as an inductive bias for RL agents. We propose a novel self-supervised attention learning approach which can 1. learn to select regions of interest without explicit annotations, and 2. act as a plug for existing deep RL methods to improve the learning performance. We empirically show that the self-supervised attention-aware deep RL methods outperform the baselines in the context of both the rate of convergence and performance. Furthermore, the proposed self-supervised attention is not tied with specific policies, nor restricted to a specific scene. We posit that the proposed approach is a general self-supervised attention module for multi-task learning and transfer learning, and empirically validate the generalization ability of the proposed method. Finally, we show that our method learns meaningful object keypoints highlighting improvements both qualitatively and quantitatively.
2021-05-17
AAAI Conference on Artificial Intelligence (published)
doi.org
Towered Actor Critic For Handling Multiple Action Types In Reinforcement Learning For Drug Discovery
Sai Krishna Gottipati
Yashaswi Pathak
Boris Sattarov
Sahir
Rohan Nuttall
Mohammad Amini
Matthew E. Taylor
Sarath Chandar
Reinforcement learning (RL) has made significant progress in both abstract and real-world domains, but the majority of state-of-the-art algo… (see more)rithms deal only with monotonic actions. However, some applications require agents to reason over different types of actions. Our application simulates reaction-based molecule generation, used as part of the drug discovery pipeline, and includes both uni-molecular and bi-molecular reactions. This paper introduces a novel framework, towered actor critic (TAC), to handle multiple action types. The TAC framework is general in that it is designed to be combined with any existing RL algorithms for continuous action space. We combine it with TD3 to empirically obtain significantly better results than existing methods in the drug discovery setting. TAC is also applied to RL benchmarks in OpenAI Gym and results show that our framework can improve, or at least does not hurt, performance relative to standard TD3.
2021-05-17
AAAI Conference on Artificial Intelligence (published)
doi.org
Variance Penalized On-Policy and Off-Policy Actor-Critic
Arushi Jain
Gandharv Patil
Ayush Jain
Khimya Khetarpal
Doina Precup
Reinforcement learning algorithms are typically geared towards optimizing the expected return of an agent. However, in many practical applic… (see more)ations, low variance in the return is desired to ensure the reliability of an algorithm. In this paper, we propose on-policy and off-policy actor-critic algorithms that optimize a performance criterion involving both mean and variance in the return. Previous work uses the second moment of return to estimate the variance indirectly. Instead, we use a much simpler recently proposed direct variance estimator which updates the estimates incrementally using temporal difference methods. Using the variance-penalized criterion, we guarantee the convergence of our algorithm to locally optimal policies for finite state action Markov decision processes. We demonstrate the utility of our algorithm in tabular and continuous MuJoCo domains. Our approach not only performs on par with actor-critic and prior variance-penalization baselines in terms of expected return, but also generates trajectories which have lower variance in the return.
2021-05-17
Proceedings of the AAAI Conference on Artificial Intelligence (published)
doi.org
arxiv.org