Publications

A framework for fair decision-making over time with time-invariant utilities
Andrea Lodi
Sriram Sankaranarayanan
Guanyi Wang
2024-01-01
Eur. J. Oper. Res. (published)
doi.org
arxiv.org
Game Theoretical Formulation for Residential Community Microgrid via Mean Field Theory: Proof of Concept
Mohamad Aziz
Hanane Dagdougui
Issmail ElHallaoui
Incentive-based demand response aggregators are widely recognized as a powerful strategy to increase the flexibility of residential communit… (see more)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 (published)
doi.org
Hessian Aware Low-Rank Weight Perturbation for Continual Learning
Jiaqi Li
Yuanhao Lai
Rui Wang
Changjian Shui
Sabyasachi Sahoo
Charles Ling
Shichun Yang
Boyu Wang
Christian Gagné
Fan Zhou
Continual learning aims to learn a series of tasks sequentially without forgetting the knowledge acquired from the previous ones. In this wo… (see more)rk, we propose the Hessian Aware Low-Rank Perturbation algorithm for continual learning. By modeling the parameter transitions along the sequential tasks with the weight matrix transformation, we propose to apply the low-rank approximation on the task-adaptive parameters in each layer of the neural networks. Specifically, we theoretically demonstrate the quantitative relationship between the Hessian and the proposed low-rank approximation. The approximation ranks are then globally determined according to the marginal increment of the empirical loss estimated by the layer-specific gradient and low-rank approximation error. Furthermore, we control the model capacity by pruning less important parameters to diminish the parameter growth. We conduct extensive experiments on various benchmarks, including a dataset with large-scale tasks, and compare our method against some recent state-of-the-art methods to demonstrate the effectiveness and scalability of our proposed method. Empirical results show that our method performs better on different benchmarks, especially in achieving task order robustness and handling the forgetting issue. The source code is at https://github.com/lijiaqi/HALRP.
2024-01-01
IEEE Trans. Knowl. Data Eng. (published)
doi.org
arxiv.org
High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise.
Eduard Gorbunov
Abdurakhmon Sadiev
Marina Danilova
Samuel Horváth
Gauthier Gidel
Pavel Dvurechensky
Alexander Gasnikov
Peter Richtárik
2024-01-01
International Conference on Machine Learning (published)
doi.org
openreview.net
How well do models of visual cortex generalize to out of distribution samples?
Yifei Ren
Pouya Bashivan
2024-01-01
PLoS Comput. Biol. (published)
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. (published)
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 (published)
doi.org
Improving Adversarial Robustness in Vision-Language Models with Architecture and Prompt Design.
Rishika Bhagwatkar
Shravan Nayak
Pouya Bashivan
Irina Rish
2024-01-01
EMNLP (Findings) (published)
doi.org
Improving the Generalizability and Robustness of Large-Scale Traffic Signal Control
Tianyu Shi
François-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… (see more)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 (published)
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… (see more)r determining the number of clusters,
2024-01-01
IEEE Access (published)
doi.org
Information Complexity of Stochastic Convex Optimization: Applications to Generalization, Memorization, and Tracing
Idan Attias
Gintare Karolina Dziugaite
MAHDI HAGHIFAM
Roi Livni
Daniel M. Roy
In this work, we investigate the interplay between memorization and learning in the context of stochastic convex optimization (SCO)… (see more). We define memorization via the information a learning algorithm reveals about its training data points. We then quantify this information using the framework of conditional mutual information (CMI) proposed by Steinke and Zakynthinou (2020). Our main result is a precise characterization of the tradeoff between the accuracy of a learning algorithm and its CMI, answering an open question posed by Livni (2023). We show that, in the
2024-01-01
International Conference on Machine Learning (published)
proceedings.mlr.press
Interacting with a Visuotactile Countertop
M. Jenkin
Francois Hogan
Kaleem Siddiqi
Jean-François Tremblay
Bobak H. Baghi
Gregory Dudek
2024-01-01
International Conference on Robotics, Computer Vision and Intelligent Systems (published)
doi.org