Publications

Game Theoretical Formulation for Residential Community Microgrid via Mean Field Theory: Proof of Concept

Mohamad Aziz

Issmail ElHallaoui

Incentive-based demand response aggregators are widely recognized as a powerful strategy to increase the flexibility of residential communit… (voir plus)y MG (RCM) while allowing consumers’ assets to participate in the operation of the power system in critical peak times. RCM implementing demand response approaches are of high interest as collectively, they have a high impact on shaping the demand curve during peak time while providing a wide range of economic and technical benefits to consumers and utilities. The penetration of distributed energy resources such as battery energy storage and photovoltaic systems introduces additional flexibility to manage the community loads and increase revenue. This letter proposes a game theoretical formulation for an incentive-based residential community microgrid, where an incentive-based pricing mechanism is developed to encourage peak demand reduction and share the incentive demand curve with the residential community through the aggregator. The aggregator’s objective is to maximize the welfare of the residential community by finding the optimal community equilibrium electricity price. Each household communicates with each other and with the distributed system operator (DSO) through the aggregator and aims to minimize the local electricity cost.

2024-01-01

IEEE Control Systems Letters (publié)

doi.org

An improved column-generation-based matheuristic for learning classification trees

Krunal Kishor Patel

Guy Desaulniers

Andrea Lodi

2024-01-01

Comput. Oper. Res. (publié)

doi.org

arxiv.org

An Improved Neuro-Symbolic Architecture to Fine-Tune Generative AI Systems

Chao Yin

Quentin Cappart

Gilles Pesant

2024-01-01

Integration of AI and OR Techniques in Constraint Programming (publié)

doi.org

Improving the Generalizability and Robustness of Large-Scale Traffic Signal Control

Tianyu Shi

FranÃ§ois-Xavier Devailly

Denis Larocque

Laurent Charlin

A number of deep reinforcement-learning (RL) approaches propose to control traffic signals. Compared to traditional approaches, RL approache… (voir plus)s can learn from higher-dimensionality input road and vehicle sensors and better adapt to varying traffic conditions resulting in reduced travel times (in simulation). However, these RL methods require training from massive traffic sensor data. To offset this relative inefficiency, some recent RL methods have the ability to first learn from small-scale networks and then generalize to unseen city-scale networks without additional retraining (zero-shot transfer). In this work, we study the robustness of such methods along two axes. First, sensor failures and GPS occlusions create missing-data challenges and we show that recent methods remain brittle in the face of these missing data. Second, we provide a more systematic study of the generalization ability of RL methods to new networks with different traffic regimes. Again, we identify the limitations of recent approaches. We then propose using a combination of distributional and vanilla reinforcement learning through a policy ensemble. Building upon the state-of-the-art previous model which uses a decentralized approach for large-scale traffic signal control with graph convolutional networks (GCNs), we first learn models using a distributional reinforcement learning (DisRL) approach. In particular, we use implicit quantile networks (IQN) to model the state-action return distribution with quantile regression. For traffic signal control problems, an ensemble of standard RL and DisRL yields superior performance across different scenarios, including different levels of missing sensor data and traffic flow patterns. Furthermore, the learning scheme of the resulting model can improve zero-shot transferability to different road network structures, including both synthetic networks and real-world networks (e.g., Luxembourg, Manhattan). We conduct extensive experiments to compare our approach to multi-agent reinforcement learning and traditional transportation approaches. Results show that the proposed method improves robustness and generalizability in the face of missing data, varying road networks, and traffic flows.

2024-01-01

IEEE Open Journal of Intelligent Transportation Systems (publié)

doi.org

arxiv.org

Inertia-Based Indices to Determine the Number of Clusters in K-Means: An Experimental Evaluation

Andrei Rykov

Renato Cordeiro De Amorim

Vladimir Makarenkov

Boris Mirkin

This paper gives an experimentally supported review and comparison of several indices based on the conventional K-means inertia criterion fo… (voir plus)r determining the number of clusters,

2024-01-01

IEEE Access (publié)

doi.org

Interacting with a Visuotactile Countertop

M. Jenkin

Francois R. Hogan

Kaleem Siddiqi

Jean-François Tremblay

Bobak H. Baghi

Gregory Dudek

2024-01-01

International Conference on Robotics, Computer Vision and Intelligent Systems (publié)

doi.org

INViTE: INterpret and Control Vision-Language Models with Text Explanations

Haozhe Chen

Junfeng Yang

Carl Vondrick

Chengzhi Mao

Columbia University

M. University

Large-scale pre-trained vision foundation models, such as CLIP, have become de facto backbones for various vision tasks. However, due to the… (voir plus)ir black-box nature, understanding the underlying rules behind these models’ predictions and controlling model behaviors have remained open challenges. We present INViTE: a framework for INterpreting Vision Transformer’s latent tokens with Text Explanations. Given a latent token, INViTE retains its semantic information to the final layer using transformer’s local operations and retrieves the closest text for explanation. INViTE enables understanding of model visual reasoning procedure without needing additional model training or data collection. Based on the obtained interpretations, INViTE allows for model editing that controls model reasoning behaviors and improves model robustness against biases and spurious correlations. Our code is available at https://github.com/tonychenxyz/vit-interpret.

2024-01-01

International Conference on Learning Representations (publié)

openreview.net

ÌròyìnSpeech: A multi-purpose Yorùbá Speech Corpus

Tolúlope' Ògúnremí

Kọ́lá Túbọ̀sún

Aremu Anuoluwapo

Iroro Orife

David Ifeoluwa Adelani

2024-01-01

LREC/COLING (publié)

doi.org

arxiv.org

Learning Conditional Policies for Crystal Design Using Offline Reinforcement Learning

Prashant Govindarajan

Santiago Miret

Jarrid Rector-Brooks

Mariano Phielipp

Janarthanan Rajendran

Sarath Chandar

Navigating through the exponentially large chemical space to search for desirable materials is an extremely challenging task in material dis… (voir plus)covery. Recent developments in generative and geometric deep learning have shown...

2024-01-01

Digital Discovery (publié)

doi.org

openreview.net

Learning Tabu Search Algorithms: A Scheduling Application

Nazgol Niroumandrad

Nadia Lahrichi

Andrea Lodi

. Metaheuristics are widely recognized as efficient approaches for many combinatorial problems. Studies to improve the performance of metahe… (voir plus)uristics have increasingly relied on the use of various methods either combining different metaheuristics or methods originating outside of the metaheuristic field. This paper presents a learning algorithm to improve tabu search by reducing its search space and the evaluation effort. We study the performance of a learning tabu search algorithm using classification methods in an attempt to select moves through the search space more wisely. The experimental results demonstrate the benefit of using a learning mechanism under deterministic and stochastic conditions.

2024-01-01

Comput. Oper. Res. (publié)

doi.org

Learning the Game: Decoding the Differences between Novice and Expert Players in a Citizen Science Game with Millions of Players

Eddie Cai

Roman Sarrazin-Gendron

Renata Mutalova

Parham Ghasemloo Gheidari

Alexander Butyaev

Gabriel Richard

Sébastien Caisse

Rob Knight