Publications

Invited commentary on Stoehr J et al: The personal impact of involvement in international global health outreach: A national survey of former operation smile student volunteers.

Dan Poenaru

2023-01-01

Journal of Pediatric Surgery (published)

doi.org

Iorl: Inductive-Offline-Reinforcement-Learning for Traffic Signal Control Warmstarting

FranÃ§ois-Xavier Devailly

Denis Larocque

Laurent Charlin

2023-01-01

Social Science Research Network (published)

doi.org

A Kernel Perspective on Behavioural Metrics for Markov Decision Processes

Pablo Samuel Castro

Tyler Kastner

Prakash Panangaden

Mark Rowland

We present a novel perspective on behavioural metrics for Markov decision processes via the use of positive definite kernels. We define a ne… (see more)w metric under this lens that is provably equivalent to the recently introduced MICo distance (Castro et al., 2021). The kernel perspective enables us to provide new theoretical results, including value-function bounds and low-distortion finite-dimensional Euclidean embeddings, which are crucial when using behavioural metrics for reinforcement learning representations. We complement our theory with strong empirical results that demonstrate the effectiveness of these methods in practice.

2023-01-01

Trans. Mach. Learn. Res. (published)

doi.org

openreview.net

Lag-Llama: Towards Foundation Models for Time Series Forecasting

Kashif Rasul

Arjun Ashok

Andrew Robert Williams

Arian Khorasani

George Adamopoulos

Rishika Bhagwatkar

Marin Biloš

Hena Ghonia

N. Hassen

Anderson Schneider

Sahil Garg

Alexandre Drouin

Nicolas Chapados

Yuriy Nevmyvaka

Irina Rish

Aiming to build foundation models for time-series forecasting and study their scaling behavior, we present here our work-in-progress on Lag-… (see more)Llama , a general-purpose univariate probabilistic time-series forecasting model trained on a large collection of time-series data. The model shows good zero-shot prediction capabilities on unseen “out-of-distribution” time-series datasets, outperforming supervised baselines. We use smoothly broken power-laws [7] to fit and predict model scaling behavior. The open source code is made available at https://github

2023-01-01

arXiv.org (preprint)

doi.org

LEAD: Min-Max Optimization from a Physical Perspective

Reyhane Askari Hemmat

Amartya Mitra

Guillaume Lajoie

Ioannis Mitliagkas

Adversarial formulations have rekindled interest in two-player min-max games. A central obstacle in the optimization of such games is the ro… (see more)tational dynamics that hinder their convergence. In this paper, we show that game optimization shares dynamic properties with particle systems subject to multiple forces, and one can leverage tools from physics to improve optimization dynamics. Inspired by the physical framework, we propose LEAD, an optimizer for min-max games. Next, using Lyapunov stability theory from dynamical systems as well as spectral analysis, we study LEAD’s convergence properties in continuous and discrete time settings for a class of quadratic min-max games to demonstrate linear convergence to the Nash equilibrium. Finally, we empirically evaluate our method on synthetic setups and CIFAR-10 image generation to demonstrate improvements in GAN training.

2023-01-01

Trans. Mach. Learn. Res. (published)

openreview.net

Learning GFlowNets from partial episodes for improved convergence and stability

Kanika Madan

Jarrid Rector-Brooks

Maksym Korablyov

Emmanuel Bengio

Moksh J. Jain

Andrei Cristian Nica

Tom Bosc

Yoshua Bengio

Nikolay Malkin

Generative flow networks (GFlowNets) are a family of algorithms for training a sequential sampler of discrete objects under an unnormalized … (see more)target density and have been successfully used for various probabilistic modeling tasks. Existing training objectives for GFlowNets are either local to states or transitions, or propagate a reward signal over an entire sampling trajectory. We argue that these alternatives represent opposite ends of a gradient bias-variance tradeoff and propose a way to exploit this tradeoff to mitigate its harmful effects. Inspired by the TD(

2023-01-01

ICML (published)

doi.org

openreview.net

Learning Syntactic Monoids from Samples by extending known Algorithms for learning State Machines

Simon Dieck

Sicco Verwer

François Coste

Faissal Ouardi

Guillaume Rabusseau

For the inference of regular languages, most current methods learn a version of deterministic finite automata. Syntactic monoids are an alte… (see more)rnative representation of regular languages, which have some advantages over automata. For example, traces can be parsed starting from any index and the star-freeness of the language they represent can be checked in polynomial time. But, to date, there existed no passive learning algorithm for syntactic monoids. In this paper, we prove that known state-merging algorithms for learning deterministic finite automata can be instrumented to learn syntactic monoids instead, by using as the input a special structure proposed in this paper: the interfix-graph. Further, we introduce a method to encode frequencies on the interfix-graph, such that models can also be learned from only positive traces. We implemented this structure and performed experiments with both traditional data and data containing only positive traces. As such this work answers basic theoretical and experimental questions regarding a novel passive learning algorithm for syntactic monoids.

2023-01-01

International Conference on Graphics and Interaction (published)

dblp.uni-trier.de

On learning Whittle index policy for restless bandits with scalable regret

Nima Akbarzadeh

Aditya Mahajan

Reinforcement learning is an attractive approach to learn good resource allocation and scheduling policies based on data when the system mod… (see more)el is unknown. However, the cumulative regret of most RL algorithms scales as ˜ O(S

2023-01-01

IEEE Transactions on Control of Network Systems (published)

doi.org

arxiv.org

List-GRAND: A Practical Way to Achieve Maximum Likelihood Decoding

Syed Mohsin Abbas

Marwan Jalaleddine

Warren Gross

Guessing random additive noise decoding (GRAND) is a recently proposed universal maximum likelihood (ML) decoder for short-length and high-r… (see more)ate linear block codes. Soft-GRAND (SGRAND) is a prominent soft-input GRAND variant, outperforming the other GRAND variants in decoding performance; nevertheless, SGRAND is not suitable for parallel hardware implementation. Ordered Reliability Bits-GRAND (ORBGRAND) is another soft-input GRAND variant that is suitable for parallel hardware implementation; however, it has lower decoding performance than SGRAND. In this article, we propose List-GRAND (LGRAND), a technique for enhancing the decoding performance of ORBGRAND to match the ML decoding performance of SGRAND. Numerical simulation results show that LGRAND enhances ORBGRAND’s decoding performance by 0.5–0.75 dB for channel codes of various classes at a target frame error rate (FER) of 10−7. For linear block codes of length 127/128 and different code rates, LGRAND’s VLSI implementation can achieve an average information throughput of 47.27–51.36 Gb/s. In comparison to ORBGRAND’s VLSI implementation, the proposed LGRAND hardware has a 4.84% area overhead.

2023-01-01

IEEE Transactions on Very Large Scale Integration (VLSI) Systems (published)

doi.org

arxiv.org

A Literature Review on Detecting, Verifying, and Mitigating Online Misinformation

Arezo Bodaghi

Ketra A. Schmitt

Pierre Watine

Benjamin Fung

Social media use has transformed communication and made social interaction more accessible. Public microblogs allow people to share and acce… (see more)ss news through existing and social-media-created social connections and access to public news sources. These benefits also create opportunities for the spread of false information. False information online can mislead people, decrease the benefits derived from social media, and reduce trust in genuine news. We divide false information into two categories: unintentional false information, also known as misinformation; and intentionally false information, also known as disinformation and fake news. Given the increasing prevalence of misinformation, it is imperative to address its dissemination on social media platforms. This survey focuses on six key aspects related to misinformation: 1) clarify the definition of misinformation to differentiate it from intentional forms of false information; 2) categorize proposed approaches to manage misinformation into three types: detection, verification, and mitigation; 3) review the platforms and languages for which these techniques have been proposed and tested; 4) describe the specific features that are considered in each category; 5) compare public datasets created to address misinformation and categorize into prelabeled content-only datasets and those including users and their connections; and 6) survey fact-checking websites that can be used to verify the accuracy of information. This survey offers a comprehensive and unprecedented review of misinformation, integrating various methodological approaches, datasets, and content-, user-, and network-based approaches, which will undoubtedly benefit future research in this field.

2023-01-01

IEEE Transactions on Computational Social Systems (published)

doi.org

Lower Bounds for Active Automata Learning.

Loes Kruger

Bharat Garhewal

François Coste

Frits W. Vaandrager

Faissal Ouardi

Guillaume Rabusseau

2023-01-01

ICGI (published)