Publications

Global Variations in Event-Based Surveillance for Disease Outbreak Detection: Time Series Analysis
Iris Ganser
Rodolphe Thiébaut
David L Buckeridge
2022-10-30
JMIR Public Health and Surveillance (published)
doi.org
On Learning Fairness and Accuracy on Multiple Subgroups
Changjian Shui
Gezheng Xu
Qi CHEN
Jiaqi Li
Charles Ling
Tal Arbel
Boyu Wang
Christian Gagné
We propose an analysis in fair learning that preserves the utility of the data while reducing prediction disparities under the criteria of g… (see more)roup sufficiency. We focus on the scenario where the data contains multiple or even many subgroups, each with limited number of samples. As a result, we present a principled method for learning a fair predictor for all subgroups via formulating it as a bilevel objective. Specifically, the subgroup specific predictors are learned in the lower-level through a small amount of data and the fair predictor. In the upper-level, the fair predictor is updated to be close to all subgroup specific predictors. We further prove that such a bilevel objective can effectively control the group sufficiency and generalization error. We evaluate the proposed framework on real-world datasets. Empirical evidence suggests the consistently improved fair predictions, as well as the comparable accuracy to the baselines.
2022-10-30
NeurIPS.cc/2022/Conference (accept)
doi.org
openreview.net
Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress
Rishabh Agarwal
Marc Bellemare
Pablo Samuel Castro
Aaron Courville
Marc Bellemare
Max Schwarzer
2022-10-30
Neural Information Processing Systems (accepted)
doi.org
openreview.net
Understanding the Evolution of Linear Regions in Deep Reinforcement Learning
Setareh Cohan
Nam Hee Gordon Kim
David Rolnick
Michiel van de Panne
Policies produced by deep reinforcement learning are typically characterised by their learning curves, but they remain poorly understood in … (see more)many other respects. ReLU-based policies result in a partitioning of the input space into piecewise linear regions. We seek to understand how observed region counts and their densities evolve during deep reinforcement learning using empirical results that span a range of continuous control tasks and policy network dimensions. Intuitively, we may expect that during training, the region density increases in the areas that are frequently visited by the policy, thereby affording fine-grained control. We use recent theoretical and empirical results for the linear regions induced by neural networks in supervised learning settings for grounding and comparison of our results. Empirically, we find that the region density increases only moderately throughout training, as measured along fixed trajectories coming from the final policy. However, the trajectories themselves also increase in length during training, and thus the region densities decrease as seen from the perspective of the current trajectory. Our findings suggest that the complexity of deep reinforcement learning policies does not principally emerge from a significant growth in the complexity of functions observed on-and-around trajectories of the policy.
2022-10-30
NeurIPS.cc/2022/Conference (accept)
doi.org
openreview.net
Washing The Unwashable : On The (Im)possibility of Fairwashing Detection
Ali Shahin Shamsabadi
Mohammad Yaghini
Natalie Dullerud
Sierra Wyllie
Ulrich Matchi Aïvodji
Aisha Alaagib Alryeh Mkean
Sébastien Gambs
Nicolas Papernot
The use of black-box models (e.g., deep neural networks) in high-stakes decision-making systems, whose internal logic is complex, raises the… (see more) need for providing explanations about their decisions. Model explanation techniques mitigate this problem by generating an interpretable and high-fidelity surrogate model (e.g., a logistic regressor or decision tree) to explain the logic of black-box models. In this work, we investigate the issue of fairwashing, in which model explanation techniques are manipulated to rationalize decisions taken by an unfair black-box model using deceptive surrogate models. More precisely, we theoretically characterize and analyze fairwashing, proving that this phenomenon is difficult to avoid due to an irreducible factor---the unfairness of the black-box model. Based on the theory developed, we propose a novel technique, called FRAUD-Detect (FaiRness AUDit Detection), to detect fairwashed models by measuring a divergence over subpopulation-wise fidelity measures of the interpretable model. We empirically demonstrate that this divergence is significantly larger in purposefully fairwashed interpretable models than in honest ones. Furthermore, we show that our detector is robust to an informed adversary trying to bypass our detector. The code implementing FRAUD-Detect is available at https://github.com/cleverhans-lab/FRAUD-Detect.
2022-10-30
NeurIPS.cc/2022/Conference (accepted)
openreview.net
openreview.net
Behind the Machine's Gaze: Neural Networks with Biologically-inspired Constraints Exhibit Human-like Visual Attention
Leo Schwinn
Doina Precup
Bjoern Eskofier
Dario Zanca
By and large, existing computational models of visual attention tacitly assume perfect vision and full access to the stimulus and thereby de… (see more)viate from foveated biological vision. Moreover, modeling top-down attention is generally reduced to the integration of semantic features without incorporating the signal of a high-level visual tasks that have been shown to partially guide human attention. We propose the Neural Visual Attention (NeVA) algorithm to generate visual scanpaths in a top-down manner. With our method, we explore the ability of neural networks on which we impose a biologically-inspired foveated vision constraint to generate human-like scanpaths without directly training for this objective. The loss of a neural network performing a downstream visual task (i.e., classification or reconstruction) flexibly provides top-down guidance to the scanpath. Extensive experiments show that our method outperforms state-of-the-art unsupervised human attention models in terms of similarity to human scanpaths. Additionally, the flexibility of the framework allows to quantitatively investigate the role of different tasks in the generated visual behaviors. Finally, we demonstrate the superiority of the approach in a novel experiment that investigates the utility of scanpaths in real-world applications, where imperfect viewing conditions are given.
2022-10-29
TMLR (accepted)
openreview.net
openreview.net
Notational Programming for Notebook Environments: A Case Study with Quantum Circuits
Ian Arawjo
Anthony DeArmas
Michael Roberts
Shrutarshi Basu
Tapan Parikh
We articulate a vision for computer programming that includes pen-based computing, a paradigm we term notational programming. Notational pro… (see more)gramming blurs contexts: certain typewritten variables can be referenced in handwritten notation and vice-versa. To illustrate this paradigm, we developed an extension, Notate, to computational notebooks which allows users to open drawing canvases within lines of code. As a case study, we explore quantum programming and designed a notation, Qaw, that extends quantum circuit notation with abstraction features, such as variable-sized wire bundles and recursion. Results from a usability study with novices suggest that users find our core interaction of implicit cross-context references intuitive, but suggests further improvements to debugging infrastructure, interface design, and recognition rates. Throughout, we discuss questions raised by the notational paradigm, including a shift from ‘recognition’ of notations to ‘reconfiguration’ of practices and values around programming, and from ‘sketching’ to writing and drawing, or what we call ‘notating.’
2022-10-27
ACM Symposium on User Interface Software and Technology (published)
doi.org
Deep generative molecular design reshapes drug discovery
Xiangxiang Zeng
Yi Wang
Yuan Luo
Seung-gu Kang
Jian Tang
Felice C. Lightstone
Evandro F. Fang
Wendy Cornell
Ruth Nussinov
Feixiong Cheng
2022-10-26
Cell Reports Medicine (published)
doi.org
Segmentation of Multiple Sclerosis Lesions across Hospitals: Learn Continually or Train from Scratch?
Enamundram Naga Karthik
Anne Kerbrat
Pierre Labauge
Tobias Granberg
Jason F. Talbott
Daniel S Reich
Massimo Filippi
Rohit Bakshi
Virginie Callot
A. Chandar
Julien Cohen-Adad
Segmentation of Multiple Sclerosis (MS) lesions is a challenging problem. Several deep-learning-based methods have been proposed in recent y… (see more)ears. However, most methods tend to be static, that is, a single model trained on a large, specialized dataset, which does not generalize well. Instead, the model should learn across datasets arriving sequentially from different hospitals by building upon the characteristics of lesions in a continual manner. In this regard, we explore experience replay, a well-known continual learning method, in the context of MS lesion segmentation across multi-contrast data from 8 different hospitals. Our experiments show that replay is able to achieve positive backward transfer and reduce catastrophic forgetting compared to sequential fine-tuning. Furthermore, replay outperforms the multi-domain training, thereby emerging as a promising solution for the segmentation of MS lesions. The code is available at this link: https://github.com/naga-karthik/continual-learning-ms
2022-10-26
arXiv (preprint)
doi.org
arxiv.org
Arc travel time and path choice model estimation subsumed
Sobhan Mohammadpour
Emma Frejinger
We propose a method for maximum likelihood estimation of path choice model parameters and arc travel time using data of different levels of… (see more) granularity. Hitherto these two tasks have been tackled separately under strong assumptions. Using a small example, we illustrate that this can lead to biased results. Results on both real (New York yellow cab) and simulated data show strong performance of our method compared to existing baselines. models and loss functions. It is designed to estimate arc travel time and path choice model parameters simultaneously. We showed that by marginalizing the unobserved variables and using stochastic gradient estimates, we obtain a maximum likelihood estimation even for observations at different level of granularity. We showed that we can mix different data type when computing the MLE without needing to use a linear combination of losses as
2022-10-24
ArXiv (preprint)
doi.org
arxiv.org
Modeling electronic health record data using an end-to-end knowledge-graph-informed topic model
Yuesong Zou
Ahmad Pesaranghader
Ziyang Song
Aman Verma
David L. Buckeridge
Yue Li
The rapid growth of electronic health record (EHR) datasets opens up promising opportunities to understand human diseases in a systematic wa… (see more)y. However, effective extraction of clinical knowledge from the EHR data has been hindered by its sparsity and noisy information. We present GAT-ETM, an end-to-end knowledge graph-based multimodal embedded topic model. GAT-ETM distills latent disease topics from EHR data by learning the embedding from a constructed medical knowledge graph. We applied GAT-ETM to a large-scale EHR dataset consisting of over 1 million patients. We evaluated its performance based on EHR reconstruction and drug imputation. GAT-ETM demonstrated superior performance over the alternative methods on both tasks. Moreover, our model learned clinically meaningful graph-informed embedding of the EHR codes. In additional, our model is also able to discover interpretable and accurate patient representations for patient stratification and drug recommendations. Our code is available at Anonymous GitHub.
2022-10-24
Scientific Reports (published)
doi.org
Invariant representation driven neural classifier for anti-QCD jet tagging
Taoli Cheng
Aaron Courville
We leverage representation learning and the inductive bias in neural-net-based Standard Model jet classification tasks, to detect non-QCD si… (see more)gnal jets. In establishing the framework for classification-based anomaly detection in jet physics, we demonstrate that, with a \emph{well-calibrated} and \emph{powerful enough feature extractor}, a well-trained \emph{mass-decorrelated} supervised Standard Model neural jet classifier can serve as a strong generic anti-QCD jet tagger for effectively reducing the QCD background. Imposing \emph{data-augmented} mass-invariance (and thus decoupling the dominant factor) not only facilitates background estimation, but also induces more substructure-aware representation learning. We are able to reach excellent tagging efficiencies for all the test signals considered. In the best case, we reach a background rejection rate of 51 and a significance improvement factor of 3.6 at 50 \% signal acceptance, with the jet mass decorrelated. This study indicates that supervised Standard Model jet classifiers have great potential in general new physics searches.
2022-10-23
Journal of High Energy Physics (published)
doi.org
arxiv.org