Publications

Overcoming the Technical Challenges of Coordinating Distributed Load Resources at Scale
Johanna Mathieu
Ian Hiskens
Ioannis Marios Granitsas
Oluwagbemileke Oyefeso
Gregory Ledva
Sebastian Nugroho
Salman Nazir
Scott Hinson
Suzanne Russo
Steve Mock
Rachel Jenkins
Jill Harlow
Grant Fisher
Drew Geller
Duncan Callaway
Phillippe Phanivong
Antoine Lesage-Landry
2023-07-07
(publié)
doi.org
Capacity Planning in Stable Matching: An Application to School Choice
Federico Bobbio
Margarida Carvalho
Andrea Lodi
Ignacio Rios
Alfredo Torrico
Centralized mechanisms are becoming the standard approach to solve several assignment problems. Examples include the allocation of students … (voir plus)to schools (school choice), high-school graduates to colleges, residents to hospitals and refugees to cities. In most of these markets, a desirable property of the assignment is stability, which guarantees that no pair of agents has incentive to circumvent the matching. Using school choice as our matching market application, we introduce the problem of jointly allocating a school capacity expansion and finding the best stable matching for the students in the expanded market. We analyze theoretically the problem, focusing on the trade-off behind the multiplicity of student-optimal assignments, and the problem complexity. Since the theoretical intractability of the problem precludes the adaptation of classical approaches to solve it efficiently, we generalize existent mathematical programming formulations of stability constraints to our setting. These generalizations result in integer quadratically-constrained programs, which are computationally hard to solve. In addition, we propose a novel mixed-integer linear programming formulation that is exponentially-large on the problem size. We show that the stability constraints can be separated in linear time, leading to an effective cutting-plane method. We evaluate the performance of our approaches in a detailed computational study, and we find that our cutting-plane method outperforms mixed-integer programming solvers applied to existent formulations extended to our problem setting. We also propose two heuristics that are effective for large instances of the problem. Finally, we use the Chilean school choice system data to demonstrate the impact of capacity planning under stability conditions. Our results show that each additional school seat can benefit multiple students. On the one hand, we can focus on access by prioritizing extra seats that benefit previously unassigned students; on the other hand, we can focus on merit by allocating extra seats that benefit several students via chains of improvement. These insights empower the decision-maker in tuning the matching algorithm to provide a fair application-oriented solution.
2023-07-06
Proceedings of the 24th ACM Conference on Economics and Computation (publié)
doi.org
Deep Multirepresentation Learning for Data Clustering
Mohammadreza Sadeghi
Narges Armanfard
Deep clustering incorporates embedding into clustering in order to find a lower-dimensional space suitable for clustering task. Conventional… (voir plus) deep clustering methods aim to obtain a single global embedding subspace (aka latent space) for all the data clusters. In contrast, in this paper, we propose a deep multi-representation learning (DML) framework for data clustering whereby each difficult to cluster data group is associated with its own distinct optimized latent space, and all the easy to cluster data groups are associated to a general common latent space. Autoencoders are employed for generating the cluster-specific and general latent spaces. To specialize each autoencoder in its associated data cluster(s), we propose a novel and effective loss function which consists of weighted reconstruction and clustering losses of the data points, where higher weights are assigned to the samples more probable to belong to the corresponding cluster(s). Experimental results on benchmark datasets demonstrate that the proposed DML framework and loss function outperform state-of-the-art clustering approaches. In addition, the results show that the DML method significantly outperforms the SOTA on imbalanced datasets as a result of assigning an individual latent space to the difficult clusters. <br>
2023-07-06
IEEE Transactions on Neural Networks and Learning Systems (inconnu)
doi.org
Preface
François Coste
Faissal Ouardi
Guillaume Rabusseau
2023-07-04
Proceedings of 16th edition of the International Conference on Grammatical Inference (publié)
proceedings.mlr.press
proceedings.mlr.press
Generative Flow Networks: a Markov Chain Perspective
Tristan Deleu
Yoshua Bengio
2023-07-03
ArXiv (prépublication)
doi.org
arxiv.org
Better Training of GFlowNets with Local Credit and Incomplete Trajectories
Ling Pan
Nikolay Malkin
Dinghuai Zhang
Yoshua Bengio
Generative Flow Networks or GFlowNets are related to Monte-Carlo Markov chain methods (as they sample from a distribution specified by an en… (voir plus)ergy function), reinforcement learning (as they learn a policy to sample composed objects through a sequence of steps), generative models (as they learn to represent and sample from a distribution) and amortized variational methods (as they can be used to learn to approximate and sample from an otherwise intractable posterior, given a prior and a likelihood). They are trained to generate an object
2023-07-02
Proceedings of the 40th International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
Bidirectional Learning for Offline Model-based Biological Sequence Design
Can (Sam) Chen
Yingxue Zhang
Xue Liu
Mark Coates
Offline model-based optimization aims to maximize a black-box objective function with a static dataset of designs and their scores. In this … (voir plus)paper, we focus on biological sequence design to maximize some sequence score. A recent approach employs bidirectional learning, combining a forward mapping for exploitation and a backward mapping for constraint, and it relies on the neural tangent kernel (NTK) of an infinitely wide network to build a proxy model. Though effective, the NTK cannot learn features because of its parametrization, and its use prevents the incorporation of powerful pre-trained Language Models (LMs) that can capture the rich biophysical information in millions of biological sequences. We adopt an alternative proxy model, adding a linear head to a pre-trained LM, and propose a linearization scheme. This yields a closed-form loss and also takes into account the biophysical information in the pre-trained LM. In addition, the forward mapping and the backward mapping play different roles and thus deserve different weights during sequence optimization. To achieve this, we train an auxiliary model and leverage its weak supervision signal via a bi-level optimization framework to effectively learn how to balance the two mappings. Further, by extending the framework, we develop the first learning rate adaptation module \textit{Adaptive}-
2023-07-02
Proceedings of the 40th International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
Bigger, Better, Faster: Human-level Atari with human-level efficiency
Max Schwarzer
Johan Obando-Ceron
Aaron Courville
Bellemare Marc-Emmanuel
Rishabh Agarwal
Pablo Samuel Castro
We introduce a value-based RL agent, which we call BBF, that achieves super-human performance in the Atari 100K benchmark. BBF relies on sca… (voir plus)ling the neural networks used for value estimation, as well as a number of other design choices that enable this scaling in a sample-efficient manner. We conduct extensive analyses of these design choices and provide insights for future work. We end with a discussion about updating the goalposts for sample-efficient RL research on the ALE. We make our code and data publicly available at https://github.com/google-research/google-research/tree/master/bigger_better_faster.
2023-07-02
Proceedings of the 40th International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
Bootstrapped Representations in Reinforcement Learning
Charline Le Lan
Stephen Tu
Mark Rowland
Anna Harutyunyan
Rishabh Agarwal
Bellemare Marc-Emmanuel
Will Dabney
In reinforcement learning (RL), state representations are key to dealing with large or continuous state spaces. While one of the promises of… (voir plus) deep learning algorithms is to automatically construct features well-tuned for the task they try to solve, such a representation might not emerge from end-to-end training of deep RL agents. To mitigate this issue, auxiliary objectives are often incorporated into the learning process and help shape the learnt state representation. Bootstrapping methods are today's method of choice to make these additional predictions. Yet, it is unclear which features these algorithms capture and how they relate to those from other auxiliary-task-based approaches. In this paper, we address this gap and provide a theoretical characterization of the state representation learnt by temporal difference learning (Sutton, 1988). Surprisingly, we find that this representation differs from the features learned by Monte Carlo and residual gradient algorithms for most transition structures of the environment in the policy evaluation setting. We describe the efficacy of these representations for policy evaluation, and use our theoretical analysis to design new auxiliary learning rules. We complement our theoretical results with an empirical comparison of these learning rules for different cumulant functions on classic domains such as the four-room domain (Sutton et al, 1999) and Mountain Car (Moore, 1990).
2023-07-02
Proceedings of the 40th International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
Can We Scale Transformers to Predict Parameters of Diverse ImageNet Models?
Boris Knyazev
Doha Hwang
Simon Lacoste-Julien
Pretraining a neural network on a large dataset is becoming a cornerstone in machine learning that is within the reach of only a few communi… (voir plus)ties with large-resources. We aim at an ambitious goal of democratizing pretraining. Towards that goal, we train and release a single neural network that can predict high quality ImageNet parameters of other neural networks. By using predicted parameters for initialization we are able to boost training of diverse ImageNet models available in PyTorch. When transferred to other datasets, models initialized with predicted parameters also converge faster and reach competitive final performance.
2023-07-02
Proceedings of the 40th International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
Convergence of Proximal Point and Extragradient-Based Methods Beyond Monotonicity: the Case of Negative Comonotonicity
Eduard Gorbunov
Adrien Taylor
Samuel Horváth
Gauthier Gidel
2023-07-02
Proceedings of the 40th International Conference on Machine Learning (publié)
proceedings.mlr.press
proceedings.mlr.press
CrossSplit: Mitigating Label Noise Memorization through Data Splitting
Jihye Kim
Aristide Baratin
Yan Zhang
Simon Lacoste-Julien
We approach the problem of improving robustness of deep learning algorithms in the presence of label noise. Building upon existing label cor… (voir plus)rection and co-teaching methods, we propose a novel training procedure to mitigate the memorization of noisy labels, called CrossSplit, which uses a pair of neural networks trained on two disjoint parts of the labelled dataset. CrossSplit combines two main ingredients: (i) Cross-split label correction. The idea is that, since the model trained on one part of the data cannot memorize example-label pairs from the other part, the training labels presented to each network can be smoothly adjusted by using the predictions of its peer network; (ii) Cross-split semi-supervised training. A network trained on one part of the data also uses the unlabeled inputs of the other part. Extensive experiments on CIFAR-10, CIFAR-100, Tiny-ImageNet and mini-WebVision datasets demonstrate that our method can outperform the current state-of-the-art in a wide range of noise ratios.
2023-07-02
Proceedings of the 40th International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press