Publications

Understanding the impact of IoT security patterns on CPU usage and energy consumption: a dynamic approach for selecting patterns with deep reinforcement learning

Saeid Jamshidi

Amin Nikanjam

Kawser Wazed Nafi

Foutse Khomh

2025-03-09

International Journal of Information Security (published)

What makes a theory of consciousness unscientific?

IIT-Concerned

Derek H. Arnold

Mark G. Baxter

Tristan A. Bekinschtein

Yoshua Bengio

James W. Bisley

Jacob Browning

Dean V. Buonomano

David Carmel

Marisa Carrasco

Peter Carruthers

Olivia Carter

Dorita H. F. Chang

Ian Charest

Mouslim Cherkaoui

Axel Cleeremans

Michael A. Cohen

Philip R. Corlett

Kalina Christoff

Sam Cumming … (see 80 more)

Cody A. Cushing

Beatrice de Gelder

Felipe De Brigard

Daniel C. Dennett

Nadine Dijkstra

Adrien Doerig

Paul E. Dux

Stephen M. Fleming

Keith Frankish

Chris Frith

Sarah Garfinkel

Melvyn A. Goodale

Jacqueline Gottlieb

Jake R. Hanson

Ran R. Hassin

Michael H. Herzog

Cecilia Heyes

Po‐Jang Hsieh

Shao‐Min Hung

Robert W. Kentridge

Tomas Knapen

Nikos Konstantinou

Konrad P. Kording

Timo L. Kvamme

Sze Chai Kwok

Renzo C. Lanfranco

Hakwan Lau

Joseph E. LeDoux

Alan Lee

Camilo Libedinsky

Matthew D. Lieberman

Ying-Tung Lin

Kayuet Liu

Maro G. Machizawa

Julio Martínez-Trujillo

Janet Metcalfe

Matthias Michel

Kenneth D. Miller

Partha P. Mitra

Dean Mobbs

Robert M. Mok

Jorge Morales

Myrto Mylopoulos

Brian Odegaard

Charles C.-F. Or

Adrian M. Owen

David Pereplyotchik

Franco Pestilli

Megan A. K. Peters

Ian Phillips

Rosanne L. Rademaker

Dobromir Rahnev

Geraint Rees

Dario L. Ringach

Adina L. Roskies

Daniela Schiller

Aaron Schurger

D. Samuel Schwarzkopf

R. B. Y. Scott

Aaron R. Seitz

Joshua Shepherd

Juha Silvanto

Heleen A. Slagter

Barry Smith

Guillermo Solovey

David Soto

Hugo J. Spiers

Timo Stein

Vincent Taschereau‐Dumouchel

Frank Tong

Peter U. Tse

Jonas Vibell

Sebastian Watzl

Taylor W. Webb

Josh Weisberg

Thalia Wheatley

Michał Wierzchoń

Martijn E. Wokke

Karen Yan

Michał Klincewicz

2025-03-09

Nature Neuroscience (unknown)

Thibault Le Sellier De Chezelles

The BrowserGym Ecosystem for Web Agent Research

Maxime Gasse

Alexandre Lacoste

Massimo Caccia

Lawrence Keunho Jang

Ori Yoran

Dehan Kong

Frank F. Xu

Siva Reddy

Quentin Cappart

Graham Neubig

Ruslan Salakhutdinov

Nicolas Chapados

The BrowserGym ecosystem addresses the growing need for efficient evaluation and benchmarking of web agents, particularly those leveraging a… (see more)utomation and Large Language Models (LLMs). Many existing benchmarks suffer from fragmentation and inconsistent evaluation methodologies, making it challenging to achieve reliable comparisons and reproducible results. In an earlier work, Drouin et al. (2024) introduced BrowserGym which aims to solve this by providing a unified, gym-like environment with well-defined observation and action spaces, facilitating standardized evaluation across diverse benchmarks. We propose an extended BrowserGym-based ecosystem for web agent research, which unifies existing benchmarks from the literature and includes AgentLab, a complementary framework that aids in agent creation, testing, and analysis. Our proposed ecosystem offers flexibility for integrating new benchmarks while ensuring consistent evaluation and comprehensive experiment management. As a supporting evidence, we conduct the first large-scale, multi-benchmark web agent experiment and compare the performance of 6 state-of-the-art LLMs across 6 popular web agent benchmarks made available in BrowserGym. Among other findings, our results highlight a large discrepancy between OpenAI and Anthropic's latests models, with Claude-3.5-Sonnet leading the way on almost all benchmarks, except on vision-related tasks where GPT-4o is superior. Despite these advancements, our results emphasize that building robust and efficient web agents remains a significant challenge, due to the inherent complexity of real-world web environments and the limitations of current models.

2025-03-07

TMLR (accepted)

NNetscape Navigator: Complex Demonstrations for Web Agents Without a Demonstrator

Shikhar Murty

Dzmitry Bahdanau

Hao Zhu

Christopher D Manning

We introduce NNetscape Navigator (NNetnav), a method for training web agents entirely through synthetic demonstrations. These demonstrations… (see more) are collected by first interacting with a browser to generate trajectory rollouts, which are then retroactively labeled into instructions using a language model. Most work on training browser agents has relied on expensive human supervision, and the limited previous work on such interaction-first synthetic data techniques has failed to provide effective search through the exponential space of exploration. In contrast, NNetnav exploits the hierarchical structure of language instructions to make this search more tractable: complex instructions are typically decomposable into simpler subtasks, allowing NNetnav to automatically prune interaction episodes when an intermediate trajectory cannot be annotated with a meaningful sub-task. We use NNetnav demonstrations from a language model for supervised fine-tuning of a smaller language model policy, and find improvements of 6 points on WebArena and over 20 points on MiniWoB++, two popular environments for web-agents. Notably, on WebArena, we observe that language model policies can be further enhanced when fine-tuned with NNetnav demonstrations derived from the same language model. Finally, we collect and release a dataset of over 6k NNetnav demonstrations on WebArena, spanning a diverse and complex set of instructions.

2025-03-06

ICLR.cc/2025/Workshop/SSI-FM (poster)

Towards Graph Foundation Models: A Study on the Generalization of Positional and Structural Encodings

Billy Joe Franks

Moshe Eliasof

Semih Cantürk

Guy Wolf

Carola-Bibiane Schönlieb

Sophie Fellenz

Marius Kloft

Recent advances in integrating positional and structural encodings (PSEs) into graph neural networks (GNNs) have significantly enhanced thei… (see more)r performance across various graph learning tasks. However, the general applicability of these encodings and their potential to serve as foundational representations for graphs remain uncertain. This paper investigates the fine-tuning efficiency, scalability with sample size, and generalization capability of learnable PSEs across diverse graph datasets. Specifically, we evaluate their potential as universal pre-trained models that can be easily adapted to new tasks with minimal fine-tuning and limited data. Furthermore, we assess the expressivity of the learned representations, particularly, when used to augment downstream GNNs. We demonstrate through extensive benchmarking and empirical analysis that PSEs generally enhance downstream models. However, some datasets may require specific PSE-augmentations to achieve optimal performance. Nevertheless, our findings highlight their significant potential to become integral components of future graph foundation models. We provide new insights into the strengths and limitations of PSEs, contributing to the broader discourse on foundation models in graph learning.

2025-03-06

TMLR (accepted)

Tractable Representations for Convergent Approximation of Distributional HJB Equations

Julie Alhosh

Harley Wiltzer

David Meger

2025-03-06

ArXiv (preprint)

Attention-based Class-Conditioned Alignment for Multi-Source Domain Adaptation of Object Detectors

Atif Belal

Akhil Meethal

Francisco Perdigon Romero

Marco Pedersoli

Eric Granger

Domain adaptation methods for object detection (OD) strive to mitigate the impact of distribution shifts by promoting feature alignment acro… (see more)ss source and target domains. Multi-source domain adaptation (MSDA) allows leveraging multiple annotated source datasets and unlabeled target data to improve the accuracy and robustness of the detection model. Most state-of-the-art MSDA methods for OD perform feature alignment in a class-agnostic manner. This is challenging since the objects have unique modality information due to variations in object appearance across domains. A recent prototype-based approach proposed a class-wise alignment, yet it suffers from error accumulation caused by noisy pseudo-labels that can negatively affect adaptation with imbalanced data. To overcome these limitations, we propose an attention-based class-conditioned alignment method for MSDA, designed to align instances of each object category across domains. In particular, an attention module combined with an adversarial domain classifier allows learning domain-invariant and class-specific instance representations. Experimental results on multiple benchmarking MSDA datasets indicate that our method outperforms state-of-the-art methods and exhibits robustness to class imbalance, achieved through a conceptually simple class-conditioning strategy. Our code is available at: https://github.com/imatif17/ACIA.

2025-03-05

2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (published)

Cross-validation for training and testing co-occurrence network inference algorithms

Daniel Agyapong

Jeffrey Ryan Propster

Jane Marks

Toby Dylan Hocking

2025-03-05

BMC Bioinformatics (published)

EMA-Net: Efficient Multitask Affinity Learning for Dense Scene Predictions

Dimitrios Sinodinos

Narges Armanfard

2025-03-05

2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (published)

From Intuition to Understanding: Using AI Peers to Overcome Physics Misconceptions

Ruben Weijers

Denton Wu

Hannah Betts

Tamara Jacod

Yuxiang Guan

Vidya Sujaya

Kushal Dev

Toshali Goel

William Delooze

Reihaneh Rabbany

Ying Wu

Jean-François Godbout

Kellin Pelrine

Generative AI has the potential to transform personalization and accessibility of education. However, it raises serious concerns about accur… (see more)acy and helping students become independent critical thinkers. In this study, we designed a helpful yet fallible AI "Peer" to help students correct fundamental physics misconceptions related to Newtonian mechanic concepts. In contrast to approaches that seek near-perfect accuracy to create an authoritative AI tutor or teacher, we directly inform students that this AI can answer up to 40\% of questions incorrectly. In a randomized controlled trial with 165 students, those who engaged in targeted dialogue with the AI Peer achieved post-test scores that were, on average, 10.5 percentage points higher—with over 20 percentage points higher normalized gain—than a control group that discussed physics history. Qualitative feedback indicated that 91% of the treatment group's AI interactions were rated as helpful. Furthermore, by comparing student performance on pre- and post-test questions about the same concept, along with experts' annotations of the AI interactions, we find initial evidence suggesting the improvement in performance does not depend on the correctness of the AI. With further research, the AI Peer paradigm described here could open new possibilities for how we learn, adapt to, and grow with AI.

2025-03-05

ICLR.cc/2025/Workshop/FM-Wild (published)

GradTune: Last-layer Fine-tuning for Group Robustness Without Group Annotation

Patrik Joslin Kenfack

Ulrich Matchi Aïvodji

S Ebrahimi Kahou

This work addresses the limitations of deep neural networks (DNNs) in generalizing beyond training data due to spurious correlations. Recent… (see more) research has demonstrated that models trained with empirical risk minimization learn both core and spurious features, often upweighting spurious ones in the final classification, which can frequently lead to poor performance on minority groups. Deep Feature Reweighting alleviates this issue by retraining the model's last classification layer using a group-balanced held-out validation set. However, relying on spurious feature labels during training or validation limits practical application, as spurious features are not always known or costly to annotate. Our preliminary experiments reveal that ERM-trained models exhibit higher gradient norms on minority group samples in the hold-out dataset. Leveraging these insights, we propose an alternative approach called GradTune, which fine-tunes the last classification layer using high-gradient norm samples. Our results on four well-established benchmarks demonstrate that the proposed method can achieve competitive performance compared to existing methods without requiring group labels during training or validation.

2025-03-05

ICLR.cc/2025/Workshop/SCSL (published)