Publications

A Decomposition-Based Framework for Large-Scale Multi-Period Log-Truck Routing and Scheduling: A Case Study in Canadian Forestry
Abdelhakim Abdellaoui
François Aubé
Loubna Benabbou
I. E. Hallaoui
Mouloud Amazouz
2024-12-31
IFAC-PapersOnLine (publié)
doi.org
Deep Clustering with Self-Supervision using Pairwise Similarities
Mohammadreza Sadeghi
Sareh Soleimani
Narges Armanfard
Deep clustering incorporates embedding into clustering to find a lower-dimensional space appropriate for clustering. In this paper, we propo… (voir plus)se a novel deep clustering framework with self-supervision using pairwise similarities (DCSS). The proposed method consists of two successive phases. In the first phase, we propose to form hypersphere-like groups of similar data points, i.e. one hypersphere per cluster, employing an autoencoder that is trained using cluster-specific losses. The hyper-spheres are formed in the autoencoder's latent space. In the second phase, we propose to employ pairwise similarities to create a
2024-12-31
IEEE Access (publié)
doi.org
arxiv.org
Deflated Dynamics Value Iteration
Jongmin Lee
Amin Rakhsha
Ernest K. Ryu
Amir-massoud Farahmand
The Value Iteration (VI) algorithm is an iterative procedure to compute the value function of a Markov decision process, and is the basis of… (voir plus) many reinforcement learning (RL) algorithms as well. As the error convergence rate of VI as a function of iteration
2024-12-31
Trans. Mach. Learn. Res. (publié)
doi.org
arxiv.org
Dehumanizing Machines: Mitigating Anthropomorphic Behaviors in Text Generation Systems
Myra Cheng
Su Lin Blodgett
Alicia DeVrio
Lisa Egede
A.R. Olteanu
As text generation systems' outputs are increasingly anthropomorphic -- perceived as human-like -- scholars have also raised increasing conc… (voir plus)erns about how such outputs can lead to harmful outcomes, such as users over-relying or developing emotional dependence on these systems. How to intervene on such system outputs to mitigate anthropomorphic behaviors and their attendant harmful outcomes, however, remains understudied. With this work, we aim to provide empirical and theoretical grounding for developing such interventions. To do so, we compile an inventory of interventions grounded both in prior literature and a crowdsourced study where participants edited system outputs to make them less human-like. Drawing on this inventory, we also develop a conceptual framework to help characterize the landscape of possible interventions, articulate distinctions between different types of interventions, and provide a theoretical basis for evaluating the effectiveness of different interventions.
2024-12-31
ACL (1) (publié)
doi.org
arxiv.org
Designing Experimental Setup Emulating Log-Loader Manipulator and Implementing Anti-Sway Trajectory Planner
Iman Jebellat
George Sideris
Rafid Saif
Inna Sharf
Forestry machines are not easily accessible for experimentation or demonstration of research results. These mobile robots are massive, very … (voir plus)expensive, and require a large outdoor space and permits to operate. These factors hinder conducting experiments on real forestry robots. Thus, it is essential to design experimental setups utilizing easily accessible robots in indoor labs that can effectively replicate the behavior of interest of a forestry machine. We design a setup to resemble a log-loader crane and grapple motions using a Kinova Jaco2 arm by manufacturing a specialized end-effector to attach passively to the Jaco2 arm. Passively attached grapple causes undesirable sway, which is problematic and dangerous in forestry. To address the sway problem, we employ dynamic programming to develop an anti-sway motion planner, and validate its performance for different point-to-point maneuvers in our experimental setup. We also repeat each experiment at least 6 times to ensure the repeatability and reliability of the experiments. The experimental results showcase the excellent sway-damping performance of our planner and also the very good repeatability of our experiments.
2024-12-31
IEEE International Conference on Robotics and Automation (publié)
doi.org
Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints
Lingkai Kong
Yuanqi Du
Wenhao Mu
Kirill Neklyudov
Valentin De Bortoli
Haorui Wang
Dongxia Wu
Aaron Ferber
Yi-An Ma
Carla P. Gomes
Chao Zhang
Addressing real-world optimization problems becomes particularly challenging when analytic objective functions or constraints are unavailabl… (voir plus)e. While numerous studies have addressed the issue of unknown objectives, limited research has focused on scenarios where feasibility constraints are not given explicitly. Overlooking these constraints can lead to spurious solutions that are unrealistic in practice. To deal with such unknown constraints, we propose to perform optimization within the data manifold using diffusion models. To constrain the optimization process to the data manifold, we reformulate the original optimization problem as a sampling problem from the product of the Boltzmann distribution defined by the objective function and the data distribution learned by the diffusion model. Depending on the differentiability of the objective function, we propose two different sampling methods. For differentiable objectives, we propose a two-stage framework that begins with a guided diffusion process for warm-up, followed by a Langevin dynamics stage for further correction. For non-differentiable objectives, we propose an iterative importance sampling strategy using the diffusion model as the proposal distribution. Comprehensive experiments on a synthetic dataset, six real-world black-box optimization datasets, and a multi-objective molecule optimization dataset show that our method achieves better or comparable performance with previous state-of-the-art baselines.
2024-12-31
AISTATS (publié)
doi.org
proceedings.mlr.press
Diffusion-Based Adversarial Purification for Intrusion Detection
Mohamed Amine Merzouk
Erwan Beurier
Reda Yaich
N. Cuppens-Boulahia
Frédéric Cuppens
Foutse Khomh
2024-12-31
Database Security (publié)
doi.org
Discrete Audio Tokens: More Than a Survey!
Pooneh Mousavi
Gallil Maimon
Adel Moumen
Darius Petermann
Jiatong Shi
Haibin Wu
Haici Yang
Anastasia Kuznetsova
Artem Ploujnikov
Ricard Marxer
Bhuvana Ramabhadran
Benjamin Elizalde
Loren Lugosch
Jinyu Li
Yusuf Cem Sübakan
Phil Woodland
Minje Kim
Hung-yi Lee
Shinji Watanabe
Yossi Adi … (voir 1 de plus)
Mirco Ravanaelli
Discrete audio tokens are compact representations that aim to preserve perceptual quality, phonetic content, and speaker characteristics whi… (voir plus)le enabling efficient storage and inference, as well as competitive performance across diverse downstream tasks. They provide a practical alternative to continuous features, enabling the integration of speech and audio into modern large language models (LLMs). As interest in token-based audio processing grows, various tokenization methods have emerged, and several surveys have reviewed the latest progress in the field. However, existing studies often focus on specific domains or tasks and lack a unified comparison across various benchmarks. This paper presents a systematic review and benchmark of discrete audio tokenizers, covering three domains: speech, music, and general audio. We propose a taxonomy of tokenization approaches based on encoder-decoder, quantization techniques, training paradigm, streamability, and application domains. We evaluate tokenizers on multiple benchmarks for reconstruction, downstream performance, and acoustic language modeling, and analyze trade-offs through controlled ablation studies. Our findings highlight key limitations, practical considerations, and open challenges, providing insight and guidance for future research in this rapidly evolving area. For more information, including our main results and tokenizer database, please refer to our website: https://poonehmousavi.github.io/dates-website/.
2024-12-31
Trans. Mach. Learn. Res. (publié)
doi.org
openreview.net
A Distributed ADMM-based Deep Learning Approach for Thermal Control in Multi-Zone Buildings
Vincent Taboga
Hanane Dagdougui
The surge in electricity use, coupled with the dependency on intermittent renewable energy sources, poses significant hurdles to effectively… (voir plus) managing power grids, particularly during times of peak demand. Demand Response programs and energy conservation measures are essential to operate energy grids while ensuring a responsible use of our resources This research combines distributed optimization using ADMM with Deep Learning models to plan indoor temperature setpoints effectively. A two-layer hierarchical structure is used, with a central building coordinator at the upper layer and local controllers at the thermal zone layer. The coordinator must limit the building's maximum power by translating the building's total power to local power targets for each zone. Local controllers can modify the temperature setpoints to meet the local power targets. The resulting control algorithm, called Distributed Planning Networks, is designed to be both adaptable and scalable to many types of buildings, tackling two of the main challenges in the development of such systems. The proposed approach is tested on an 18-zone building modeled in EnergyPlus. The algorithm successfully manages Demand Response peak events.
2024-12-31
IEEE Trans Autom. Sci. Eng. (publié)
doi.org
arxiv.org
Does Language Matter for Early Detection of Parkinson's Disease from Speech?
Peter Plantinga
Briac Cordelle
Dominique Louër
Mirco Ravanaelli
Denise Klein
Using speech samples as a biomarker is a promising avenue for detecting and monitoring the progression of Parkinson's disease (PD), but ther… (voir plus)e is considerable disagreement in the literature about how best to collect and analyze such data. Early research in detecting PD from speech used a sustained vowel phonation (SVP) task, while some recent research has explored recordings of more cognitively demanding tasks. To assess the role of language in PD detection, we tested pretrained models with varying data types and pretraining objectives and found that (1) text-only models match the performance of vocal-feature models, (2) multilingual Whisper outperforms self-supervised models whereas monolingual Whisper does worse, and (3) AudioSet pretraining improves performance on SVP but not spontaneous speech. These findings together highlight the critical role of language for the early detection of Parkinson's disease.
2024-12-31
2025 IEEE 35th International Workshop on Machine Learning for Signal Processing (MLSP) (publié)
doi.org
dyAb: Flow Matching for Flexible Antibody Design with AlphaFold-driven Pre-binding Antigen
Cheng Tan
Yijie Zhang
Zhangyang Gao
Yufei Huang
Haitao Lin
Lirong Wu
Fandi Wu
Mathieu Blanchette
Stan Z. Li
The development of therapeutic antibodies heavily relies on accurate predictions of how antigens will interact with antibodies. Existing com… (voir plus)putational methods in antibody design often overlook crucial conformational changes that antigens undergo during the binding process, significantly impacting the reliability of the resulting antibodies. To bridge this gap, we introduce dyAb, a flexible framework that incorporates AlphaFold2-driven predictions to model pre-binding antigen structures and specifically addresses the dynamic nature of antigen conformation changes. Our dyAb model leverages a unique combination of coarse-grained interface alignment and fine-grained flow matching techniques to simulate the interaction dynamics and structural evolution of the antigen-antibody complex, providing a realistic representation of the binding process. Extensive experiments show that dyAb significantly outperforms existing models in antibody design involving changing antigen conformations. These results highlight dyAb's potential to streamline the design process for therapeutic antibodies, promising more efficient development cycles and improved outcomes in clinical applications.
2024-12-31
AAAI Conference on Artificial Intelligence (publié)
doi.org
arxiv.org
An Effective Theory of Bias Amplification
Arjun Subramonian
Samuel J. Bell
Levent Sagun
Elvis Dohmatob
Machine learning models can capture and amplify biases present in data, leading to disparate test performance across social groups. To bette… (voir plus)r understand, evaluate, and mitigate these biases, a deeper theoretical understanding of how model design choices and data distribution properties contribute to bias is needed. In this work, we contribute a precise analytical theory in the context of ridge regression, both with and without random projections, where the former models feedforward neural networks in a simplified regime. Our theory offers a unified and rigorous explanation of machine learning bias, providing insights into phenomena such as bias amplification and minority-group bias in various feature and parameter regimes. For example, we observe that there may be an optimal regularization penalty or training time to avoid bias amplification, and there can be differences in test error between groups that are not alleviated with increased parameterization. Importantly, our theoretical predictions align with empirical observations reported in the literature on machine learning bias. We extensively empirically validate our theory on synthetic and semi-synthetic datasets.
2024-12-31
ICLR (publié)
doi.org
openreview.net