Publications

Digital Ageism: Challenges and Opportunities in Artificial Intelligence for Older Adults
Charlene H. Chu
Rune Nyrup
Kathleen Leslie
Jiamin Shi
Andria Bianchi
Alexandra Lyn
Molly McNicholl
Shehroz Khan
Samira Rahimi
Amanda Grenier
Artificial intelligence (AI) and machine learning are changing our world through their impact on sectors including health care, education, e… (voir plus)mployment, finance, and law. AI systems are developed using data that reflect the implicit and explicit biases of society, and there are significant concerns about how the predictive models in AI systems amplify inequity, privilege, and power in society. The widespread applications of AI have led to mainstream discourse about how AI systems are perpetuating racism, sexism, and classism; yet, concerns about ageism have been largely absent in the AI bias literature. Given the globally aging population and proliferation of AI, there is a need to critically examine the presence of age-related bias in AI systems. This forum article discusses ageism in AI systems and introduces a conceptual model that outlines intersecting pathways of technology development that can produce and reinforce digital ageism in AI systems. We also describe the broader ethical and legal implications and considerations for future directions in digital ageism research to advance knowledge in the field and deepen our understanding of how ageism in AI is fostered by broader cycles of injustice.
2021-11-14
The Gerontologist (publié)
doi.org
Splitting, Renaming, Removing: A Study of Common Cleaning Activities in Jupyter Notebooks
Helen Dong
Shurui Zhou
Jin L.C. Guo
Christian Kästner
Data scientists commonly use computational notebooks because they provide a good environment for testing multiple models. However, once the … (voir plus)scientist completes the code and finds the ideal model, he or she will have to dedicate time to clean up the code in order for others to easily understand it. In this paper, we perform a qualitative study on how scientists clean their code in hopes of being able to suggest a tool to automate this process. Our end goal is for tool builders to address possible gaps and provide additional aid to data scientists, who then can focus more on their actual work rather than the routine and tedious cleaning work. By sampling notebooks from GitHub and analyzing changes between subsequent commits, we identified common cleaning activities, such as changes to markdown (e.g., adding headers sections or descriptions) or comments (both deleting dead code and adding descriptions) as well as reordering cells. We also find that common cleaning activities differ depending on the intended purpose of the notebook. Our results provide a valuable foundation for tool builders and notebook users, as many identified cleaning activities could benefit from codification of best practices and dedicated tool support, possibly tailored depending on intended use.
2021-11-14
2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW) (publié)
doi.org
Subtle Bugs Everywhere: Generating Documentation for Data Wrangling Code
Chenyang Yang
Shurui Zhou
Jin L.C. Guo
Christian Kästner
Data scientists reportedly spend a significant amount of their time in their daily routines on data wrangling, i.e. cleaning data and extrac… (voir plus)ting features. However, data wrangling code is often repetitive and error-prone to write. Moreover, it is easy to introduce subtle bugs when reusing and adopting existing code, which results in reduced model quality. To support data scientists with data wrangling, we present a technique to generate documentation for data wrangling code. We use (1) program synthesis techniques to automatically summarize data transformations and (2) test case selection techniques to purposefully select representative examples from the data based on execution information collected with tailored dynamic program analysis. We demonstrate that a JupyterLab extension with our technique can provide on-demand documentation for many cells in popular notebooks and find in a user study that users with our plugin are faster and more effective at finding realistic bugs in data wrangling code.
2021-11-14
2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE) (publié)
doi.org
ZERO: Playing Mathematical Programming Games
Gabriele Dragotto
S. Sankaranarayanan
Margarida Carvalho
Andrea Lodi
2021-11-14
ArXiv (prépublication)
arxiv.org
arxiv.org
Hidden Hypergraphs, Error-Correcting Codes, and Critical Learning in Hopfield Networks
Christopher Hillar
Tenzin Chan
Rachel Taubman
David Rolnick
In 1943, McCulloch and Pitts introduced a discrete recurrent neural network as a model for computation in brains. The work inspired breakthr… (voir plus)oughs such as the first computer design and the theory of finite automata. We focus on learning in Hopfield networks, a special case with symmetric weights and fixed-point attractor dynamics. Specifically, we explore minimum energy flow (MEF) as a scalable convex objective for determining network parameters. We catalog various properties of MEF, such as biological plausibility, and then compare to classical approaches in the theory of learning. Trained Hopfield networks can perform unsupervised clustering and define novel error-correcting coding schemes. They also efficiently find hidden structures (cliques) in graph theory. We extend this known connection from graphs to hypergraphs and discover n-node networks with robust storage of 2Ω(n1−ϵ) memories for any ϵ>0. In the case of graphs, we also determine a critical ratio of training samples at which networks generalize completely.
2021-11-10
Entropy (publié)
doi.org
The Cut and Play Algorithm: Computing Nash Equilibria via Outer Approximations
Margarida Carvalho
Gabriele Dragotto
Andrea Lodi
Sriram Sankaranarayanan
We introduce the Cut-and-Play, an efficient algorithm for computing equilibria in simultaneous non-cooperative games where players solve non… (voir plus)convex and possibly unbounded optimization problems. Our algorithm exploits an intrinsic relationship between the equilibria of the original nonconvex game and the ones of a convexified counterpart. In practice, Cut-and-Play formulates a series of convex approximations of the original game and refines them with techniques from integer programming, for instance, cutting planes and branching operations. We test our algorithm on two families of challenging nonconvex games involving discrete decisions and bilevel programs, and we empirically demonstrate that it efficiently computes equilibria and outperforms existing game-specific algorithms.
2021-11-09
ArXiv (prépublication)
arxiv.org
arxiv.org
S$^3$: Sign-Sparse-Shift Reparametrization for Effective Training of Low-bit Shift Networks
Xinlin Li
Bang Liu
Yaoliang Yu
Wulong Liu
Chunjing Xu
Vahid Partovi Nia
2021-11-08
NeurIPS.cc/2021/Conference (poster)
openreview.net
openreview.net
Active 3D Shape Reconstruction from Vision and Touch
Edward J. Smith
David Meger
Luis Pineda
Roberto Calandra
Jitendra Malik
Adriana Romero
Michal Drozdzal
Humans build 3D understandings of the world through active object exploration, using jointly their senses of vision and touch. However, in 3… (voir plus)D shape reconstruction, most recent progress has relied on static datasets of limited sensory data such as RGB images, depth maps or haptic readings, leaving the active exploration of the shape largely unexplored. In active touch sensing for 3D reconstruction, the goal is to actively select the tactile readings that maximize the improvement in shape reconstruction accuracy. However, the development of deep learning-based active touch models is largely limited by the lack of frameworks for shape exploration. In this paper, we focus on this problem and introduce a system composed of: 1) a haptic simulator leveraging high spatial resolution vision-based tactile sensors for active touching of 3D objects; 2) a mesh-based 3D shape reconstruction model that relies on tactile or visuotactile signals; and 3) a set of data-driven solutions with either tactile or visuotactile priors to guide the shape exploration. Our framework enables the development of the first fully data-driven solutions to active touch on top of learned models for object understanding. Our experiments show the benefits of such solutions in the task of 3D shape understanding where our models consistently outperform natural baselines. We provide our framework as a tool to foster future research in this direction.
2021-11-08
NeurIPS.cc/2021/Conference (poster)
openreview.net
openreview.net
MICo: 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 effe… (voir plus)ctive 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 analyses, we provide empirical evidence that learning this distance alongside the value function yields structured and informative representations, including strong results on the Arcade Learning Environment benchmark.
2021-11-08
NeurIPS.cc/2021/Conference (poster)
openreview.net
openreview.net
Multi-Objective SPIBB: Seldonian Offline Policy Improvement with Safety Constraints in Finite MDPs
Harsh Satija
Philip S. Thomas
Joelle Pineau
Romain Laroche
We study the problem of Safe Policy Improvement (SPI) under constraints in the offline Reinforcement Learning (RL) setting. We consider the … (voir plus)scenario where: (i) we have a dataset collected under a known baseline policy, (ii) multiple reward signals are received from the environment inducing as many objectives to optimize. We present an SPI formulation for this RL setting that takes into account the preferences of the algorithm’s user for handling the trade-offs for different reward signals while ensuring that the new policy performs at least as well as the baseline policy along each individual objective. We build on traditional SPI algorithms and propose a novel method based on Safe Policy Iteration with Baseline Bootstrapping (SPIBB, Laroche et al., 2019) that provides high probability guarantees on the performance of the agent in the true environment. We show the effectiveness of our method on a synthetic grid-world safety task as well as in a real-world critical care context to learn a policy for the administration of IV fluids and vasopressors to treat sepsis.
2021-11-08
NeurIPS.cc/2021/Conference (poster)
openreview.net
openreview.net
Temporally Abstract Partial Models
Khimya Khetarpal
Zafarali Ahmed
Gheorghe Comanici
Doina Precup
Humans and animals have the ability to reason and make predictions about different courses of action at many time scales. In reinforcement l… (voir plus)earning, option models (Sutton, Precup \& Singh, 1999; Precup, 2000) provide the framework for this kind of temporally abstract prediction and reasoning. Natural intelligent agents are also able to focus their attention on courses of action that are relevant or feasible in a given situation, sometimes termed affordable actions. In this paper, we define a notion of affordances for options, and develop temporally abstract partial option models, that take into account the fact that an option might be affordable only in certain situations. We analyze the trade-offs between estimation and approximation error in planning and learning when using such models, and identify some interesting special cases. Additionally, we demonstrate empirically the potential impact of partial option models on the efficiency of planning.
2021-11-08
NeurIPS.cc/2021/Conference (poster)
doi.org
openreview.net
On the Expressivity of Markov Reward
David Abel
Will Dabney
Anna Harutyunyan
Mark K. Ho
Michael L. Littman
Doina Precup
Satinder Singh
Reward is the driving force for reinforcement-learning agents. This paper is dedicated to understanding the expressivity of reward as a way … (voir plus)to capture tasks that we would want an agent to perform. We frame this study around three new abstract notions of"task"that might be desirable: (1) a set of acceptable behaviors, (2) a partial ordering over behaviors, or (3) a partial ordering over trajectories. Our main results prove that while reward can express many of these tasks, there exist instances of each task type that no Markov reward function can capture. We then provide a set of polynomial-time algorithms that construct a Markov reward function that allows an agent to optimize tasks of each of these three types, and correctly determine when no such reward function exists. We conclude with an empirical study that corroborates and illustrates our theoretical findings.
2021-11-08
NeurIPS.cc/2021/Conference (présentation orale)
openreview.net
openreview.net