Publications

Towards More Realistic Extraction Attacks: An Adversarial Perspective

Yash More

Prakhar Ganesh

Golnoosh Farnadi

2024-07-02

ArXiv (prépublication)

doi.org

arxiv.org

169Yb-based high dose rate intensity modulated brachytherapy for focal treatment of prostate cancer

Maude Robitaille

Cynthia Ménard

Gabriel Famulari

Dominic Béliveau-Nadeau

Shirin A. Enger

2024-07-01

Brachytherapy (publié)

doi.org

Accelerated Benders Decomposition and Local Branching for Dynamic Maximum Covering Location Problems

Steven Lamontagne

Margarida Carvalho

Ribal Atallah

The maximum covering location problem (MCLP) is a key problem in facility location, with many applications and variants. One such variant is… (voir plus) the dynamic (or multi-period) MCLP, which considers the installation of facilities across multiple time periods. To the best of our knowledge, no exact solution method has been proposed to tackle large-scale instances of this problem. To that end, in this work, we expand upon the current state-of-the-art branch-and-Benders-cut solution method in the static case, by exploring several acceleration techniques. Additionally, we propose a specialised local branching scheme, that uses a novel distance metric in its definition of subproblems and features a new method for efficient and exact solving of the subproblems. These methods are then compared through extensive computational experiments, highlighting the strengths of the proposed methodologies.

2024-07-01

Computers & Operations Research (publié)

doi.org

arxiv.org

Expressivity of Neural Networks with Random Weights and Learned Biases

Ezekiel Williams

Avery Hee-Woon Ryoo

Thomas Jiralerspong

Alexandre Payeur

Matt Perich

Luca Mazzucato

Guillaume Lajoie

2024-07-01

ArXiv (prépublication)

doi.org

arxiv.org

Imagining a Future of Designing with AI: Dynamic Grounding, Constructive Negotiation, and Sustainable Motivation

Priyan Vaithilingam

Ian Arawjo

Elena L. Glassman

2024-07-01

Designing Interactive Systems Conference (publié)

doi.org

arxiv.org

Do LLMs Meet the Needs of Software Tutorial Writers? Opportunities and Design Implications

Avinash Bhat

Disha Shrivastava

Jin Guo

Creating software tutorials involves developing accurate code examples and explanatory text that engages and informs the reader. Large Langu… (voir plus)age Models (LLMs) demonstrate a strong capacity to generate both text and code, but their potential to assist tutorial writing is unknown. By interviewing and observing seven experienced writers using OpenAI playground as an exploration environment, we uncover design opportunities for leveraging LLMs in software tutorial writing. Our findings reveal background research, resource creation, and maintaining quality standards as critical areas where LLMs could significantly assist writers. We observe how tutorial writers generated tutorial content while exploring LLMs’ capabilities, formulating prompts, verifying LLM outputs, and reflecting on interaction goals and strategies. Our observation highlights that the unpredictability of LLM outputs and unintuitive interface design contributed to skepticism about LLM’s utility. Informed by these results, we contribute recommendations for designing LLM-based tutorial writing tools to mitigate usability challenges and harness LLMs’ full potential.

2024-07-01

Conference on Designing Interactive Systems (publié)

doi.org

A logistics provider’s profit maximization facility location problem with random utility maximizing followers

David Pinzon Ulloa

Emma Frejinger

Bernard Gendron

2024-07-01

Computers & Operations Research (publié)

doi.org

arxiv.org

Motivating Users to Attend to Privacy: A Theory-Driven Design Study

Varun Shiri

Maggie Xiong

Jinghui Cheng

Jin Guo

In modern technology environments, raising users’ privacy awareness is crucial. Existing efforts largely focused on privacy policy present… (voir plus)ation and failed to systematically address a radical challenge of user motivation for initiating privacy awareness. Leveraging the Protection Motivation Theory (PMT), we proposed design ideas and categories dedicated to motivating users to engage with privacy-related information. Using these design ideas, we created a conceptual prototype, enhancing the current App Store product page. Results from an online experiment and follow-up interviews showed that our design effectively motivated participants to attend to privacy issues, raising both the threat appraisal and coping appraisal, two main factors in PMT. Our work indicated that effective design should consider combining PMT components, calibrating information content, and integrating other design elements, such as visual cues and user familiarity. Overall, our study contributes valuable design considerations driven by the PMT to amplify the motivational aspect of privacy communication.

2024-07-01

Designing Interactive Systems Conference (publié)

doi.org

arxiv.org

Normalization and effective learning rates in reinforcement learning

Clare Lyle

Zeyu Zheng

Khimya Khetarpal

James Martens

Hado van Hasselt

Razvan Pascanu

Will Dabney

Normalization layers have recently experienced a renaissance in the deep reinforcement learning and continual learning literature, with seve… (voir plus)ral works highlighting diverse benefits such as improving loss landscape conditioning and combatting overestimation bias. However, normalization brings with it a subtle but important side effect: an equivalence between growth in the norm of the network parameters and decay in the effective learning rate. This becomes problematic in continual learning settings, where the resulting effective learning rate schedule may decay to near zero too quickly relative to the timescale of the learning problem. We propose to make the learning rate schedule explicit with a simple re-parameterization which we call Normalize-and-Project (NaP), which couples the insertion of normalization layers with weight projection, ensuring that the effective learning rate remains constant throughout training. This technique reveals itself as a powerful analytical tool to better understand learning rate schedules in deep reinforcement learning, and as a means of improving robustness to nonstationarity in synthetic plasticity loss benchmarks along with both the single-task and sequential variants of the Arcade Learning Environment. We also show that our approach can be easily applied to popular architectures such as ResNets and transformers while recovering and in some cases even slightly improving the performance of the base model in common stationary benchmarks.

2024-07-01

ArXiv (prépublication)

doi.org

arxiv.org

One-shot Learning for MIPs with SOS1 Constraints

Charly Robinson La Rocca

Jean-François Cordeau

Emma Frejinger

2024-07-01

Operations Research Forum (publié)

doi.org

arxiv.org