no code implementations • 26 Apr 2018 • Sahil Garg, Irina Rish, Guillermo Cecchi, Palash Goyal, Sarik Ghazarian, Shuyang Gao, Greg Ver Steeg, Aram Galstyan
We also derive a novel lower bound on mutual information, used as a model-selection criterion favoring representations with better alignment between the utterances of participants in a collaborative dialogue setting, as well as higher predictability of the generated responses.
no code implementations • 24 May 2018 • German Abrevaya, Irina Rish, Aleksandr Y. Aravkin, Guillermo Cecchi, James Kozloski, Pablo Polosecki, Peng Zheng, Silvina Ponce Dawson, Juliana Rhee, David Cox
Many real-world data sets, especially in biology, are produced by complex nonlinear dynamical systems.
no code implementations • 1 Dec 2017 • Jumana Dakka, Pouya Bashivan, Mina Gheiratmand, Irina Rish, Shantenu Jha, Russell Greiner
Smart systems that can accurately diagnose patients with mental disorders and identify effective treatments based on brain functional imaging data are of great applicability and are gaining much attention.
no code implementations • 10 May 2017 • Djallel Bouneffouf, Irina Rish, Guillermo A. Cecchi, Raphael Feraud
We consider a novel formulation of the multi-armed bandit model, which we call the contextual bandit with restricted context, where only a limited number of features can be accessed by the learner at every iteration.
no code implementations • 7 Jun 2017 • Djallel Bouneffouf, Irina Rish, Guillermo A. Cecchi
Drawing an inspiration from behavioral studies of human decision making, we propose here a general parametric framework for multi-armed bandit problem, which extends the standard Thompson Sampling approach to incorporate reward processing biases associated with several neurological and psychiatric conditions, including Parkinson's and Alzheimer's diseases, attention-deficit/hyperactivity disorder (ADHD), addiction, and chronic pain.
no code implementations • 6 Jan 2017 • Leila Wehbe, Anwar Nunez-Elizalde, Marcel van Gerven, Irina Rish, Brian Murphy, Moritz Grosse-Wentrup, Georg Langs, Guillermo Cecchi
The goal is to understand the brain by trying to find the function that expresses the activity of brain areas in terms of different properties of the stimulus.
no code implementations • 2 Feb 2016 • Pouya Bashivan, Irina Rish, Steve Heisig
The increasing quality and affordability of consumer electroencephalogram (EEG) headsets make them attractive for situations where medical grade devices are impractical.
no code implementations • 7 Oct 2013 • Dan He, Irina Rish, David Haws, Simon Teyssedre, Zivan Karaman, Laxmi Parida
Whole genome prediction of complex phenotypic traits using high-density genotyping arrays has attracted a great deal of attention, as it is relevant to the fields of plant and animal breeding and genetic epidemiology.
no code implementations • NeurIPS 2009 • Irina Rish, Benjamin Thyreau, Bertrand Thirion, Marion Plaze, Marie-Laure Paillere-Martinot, Catherine Martelli, Jean-Luc Martinot, Jean-Baptiste Poline, Guillermo A. Cecchi
Schizophrenia is a complex psychiatric disorder that has eluded a characterization in terms of local abnormalities of brain activity, and is hypothesized to affect the collective, ``emergent working of the brain.
no code implementations • 19 Apr 2019 • Pouya Bashivan, Martin Schrimpf, Robert Ajemian, Irina Rish, Matthew Riemer, Yuhai Tu
Most previous approaches to this problem rely on memory replay buffers which store samples from previously learned tasks, and use them to regularize the learning on new ones.
no code implementations • 2 Apr 2019 • Djallel Bouneffouf, Irina Rish
In recent years, multi-armed bandit (MAB) framework has attracted a lot of attention in various applications, from recommender systems and information retrieval to healthcare and finance, due to its stellar performance combined with certain attractive properties, such as learning from less feedback.
no code implementations • 18 May 2020 • Hannah Alsdurf, Edmond Belliveau, Yoshua Bengio, Tristan Deleu, Prateek Gupta, Daphne Ippolito, Richard Janda, Max Jarvie, Tyler Kolody, Sekoul Krastev, Tegan Maharaj, Robert Obryk, Dan Pilat, Valerie Pisano, Benjamin Prud'homme, Meng Qu, Nasim Rahaman, Irina Rish, Jean-Francois Rousseau, Abhinav Sharma, Brooke Struck, Jian Tang, Martin Weiss, Yun William Yu
Manual contact tracing of Covid-19 cases has significant challenges that limit the ability of public health authorities to minimize community infections.
no code implementations • 15 Oct 2020 • Djallel Bouneffouf, Raphaël Féraud, Sohini Upadhyay, Yasaman Khazaeni, Irina Rish
In this paper, we analyze and extend an online learning framework known as Context-Attentive Bandit, motivated by various practical applications, from medical diagnosis to dialog systems, where due to observation costs only a small subset of a potentially large number of context variables can be observed at each iteration;however, the agent has a freedom to choose which variables to observe.
no code implementations • 30 Oct 2020 • Prateek Gupta, Tegan Maharaj, Martin Weiss, Nasim Rahaman, Hannah Alsdurf, Abhinav Sharma, Nanor Minoyan, Soren Harnois-Leblanc, Victor Schmidt, Pierre-Luc St. Charles, Tristan Deleu, Andrew Williams, Akshay Patel, Meng Qu, Olexa Bilaniuk, Gaétan Marceau Caron, Pierre Luc Carrier, Satya Ortiz-Gagné, Marc-Andre Rousseau, David Buckeridge, Joumana Ghosn, Yang Zhang, Bernhard Schölkopf, Jian Tang, Irina Rish, Christopher Pal, Joanna Merckx, Eilif B. Muller, Yoshua Bengio
The rapid global spread of COVID-19 has led to an unprecedented demand for effective methods to mitigate the spread of the disease, and various digital contact tracing (DCT) methods have emerged as a component of the solution.
no code implementations • NeurIPS 2020 • Massimo Caccia, Pau Rodriguez, Oleksiy Ostapenko, Fabrice Normandin, Min Lin, Lucas Page-Caccia, Issam Hadj Laradji, Irina Rish, Alexandre Lacoste, David Vázquez, Laurent Charlin
The main challenge is that the agent must not forget previous tasks and also adapt to novel tasks in the stream.
no code implementations • 25 Dec 2020 • Khimya Khetarpal, Matthew Riemer, Irina Rish, Doina Precup
In this article, we aim to provide a literature review of different formulations and approaches to continual reinforcement learning (RL), also known as lifelong or non-stationary RL.
no code implementations • 4 Apr 2021 • Timothée Lesort, Massimo Caccia, Irina Rish
In this paper, we aim to identify and categorize different types of context drifts and potential assumptions about them, to better characterize various continual-learning scenarios.
no code implementations • 14 Apr 2021 • Sreya Francis, Irene Tenison, Irina Rish
In this paper, we propose an approach for learning invariant (causal) features common to all participating clients in a federated learning setup and analyze empirically how it enhances the Out of Distribution (OOD) accuracy as well as the privacy of the final learned model.
no code implementations • 21 Apr 2021 • Irene Tenison, Sreya Francis, Irina Rish
Federated Averaging (FedAVG) has become the most popular federated learning algorithm due to its simplicity and low communication overhead.
no code implementations • 3 Jun 2021 • Timothée Lesort, Thomas George, Irina Rish
Our analysis and results shed light on the dynamics of the output layer in continual learning scenarios and suggest a way of selecting the best type of output layer for a given scenario.
no code implementations • 27 Aug 2021 • Nadhir Hassen, Irina Rish
A body of work has been done to automate machine learning algorithm to highlight the importance of model choice.
no code implementations • 29 Sep 2021 • Shanel Gauthier, Benjamin Thérien, Laurent Alsène-Racicot, Muawiz Sajjad Chaudhary, Irina Rish, Eugene Belilovsky, Michael Eickenberg, Guy Wolf
The wavelet filters used in the scattering transform are typically selected to create a tight frame via a parameterized mother wavelet.
no code implementations • 13 Oct 2021 • Gabriele Prato, Simon Guiroy, Ethan Caballero, Irina Rish, Sarath Chandar
Empirical science of neural scaling laws is a rapidly growing area of significant importance to the future of machine learning, particularly in the light of recent breakthroughs achieved by large-scale pre-trained models such as GPT-3, CLIP and DALL-e.
no code implementations • ICML Workshop LifelongML 2020 • Touraj Laleh, Mojtaba Faramarzi, Irina Rish, Sarath Chandar
Most proposed approaches for this issue try to compensate for the effects of parameter updates in the batch incremental setup in which the training model visits a lot of samples for several epochs.
no code implementations • NeurIPS Workshop Neuro_AI 2019 • Baihan Lin, Guillermo Cecchi, Djallel Bouneffouf, Jenna Reinen, Irina Rish
Drawing an inspiration from behavioral studies of human decision making, we propose here a general parametric framework for a reinforcement learning problem, which extends the standard Q-learning approach to incorporate a two-stream framework of reward processing with biases biologically associated with several neurological and psychiatric conditions, including Parkinson's and Alzheimer's diseases, attention-deficit/hyperactivity disorder (ADHD), addiction, and chronic pain.
no code implementations • 22 Dec 2021 • Mahta Ramezanian Panahi, Germán Abrevaya, Jean-Christophe Gagnon-Audet, Vikram Voleti, Irina Rish, Guillaume Dumas
The principled design and discovery of biologically- and physically-informed models of neuronal dynamics has been advancing since the mid-twentieth century.
no code implementations • 28 Jan 2022 • Irene Tenison, Sai Aravind Sreeramadas, Vaikkunth Mugunthan, Edouard Oyallon, Irina Rish, Eugene Belilovsky
A major challenge in federated learning is the heterogeneity of data across client, which can degrade the performance of standard FL algorithms.
no code implementations • 10 Jul 2022 • Timothée Lesort, Oleksiy Ostapenko, Diganta Misra, Md Rifat Arefin, Pau Rodríguez, Laurent Charlin, Irina Rish
In this paper, we study the progressive knowledge accumulation (KA) in DNNs trained with gradient-based algorithms in long sequences of tasks with data re-occurrence.
no code implementations • 8 Oct 2022 • Ardavan S. Nobandegani, Thomas R. Shultz, Irina Rish
In this work, we substantiate the idea of $\textit{cognitive models as simulators}$, which is to have AI systems interact with, and collect feedback from, cognitive models instead of humans, thereby making their training process both less costly and faster.
no code implementations • 18 Oct 2022 • Jean-Charles Layoun, Alexis Roger, Irina Rish
The goal of vision-language modeling is to allow models to tie language understanding with visual inputs.
no code implementations • 9 Nov 2022 • Alessio Mora, Irene Tenison, Paolo Bellavista, Irina Rish
Federated Learning (FL) enables the training of Deep Learning models without centrally collecting possibly sensitive raw data.
no code implementations • 2 Feb 2023 • Baihan Lin, Djallel Bouneffouf, Irina Rish
The field of compositional generalization is currently experiencing a renaissance in AI, as novel problem settings and algorithms motivated by various practical applications are being introduced, building on top of the classical compositional generalization problem.
no code implementations • 26 Apr 2023 • Alexis Roger, Esma Aïmeur, Irina Rish
Generative AI systems (ChatGPT, DALL-E, etc) are expanding into multiple areas of our lives, from art Rombach et al. [2021] to mental health Rob Morris and Kareem Kouddous [2022]; their rapidly growing societal impact opens new opportunities, but also raises ethical concerns.
no code implementations • 23 Jun 2023 • Pascal Jr. Tikeng Notsawo, Hattie Zhou, Mohammad Pezeshki, Irina Rish, Guillaume Dumas
In essence, by studying the learning curve of the first few epochs, we show that one can predict whether grokking will occur later on.
no code implementations • 11 Jul 2023 • Germán Abrevaya, Mahta Ramezanian-Panahi, Jean-Christophe Gagnon-Audet, Pablo Polosecki, Irina Rish, Silvina Ponce Dawson, Guillermo Cecchi, Guillaume Dumas
Scientific Machine Learning (SciML) is a burgeoning field that synergistically combines domain-aware and interpretable models with agnostic machine learning techniques.
no code implementations • 25 Sep 2023 • Ayush Kaushal, Tejas Vaidhya, Irina Rish
Low Rank Decomposition of matrix - splitting a large matrix into a product of two smaller matrix offers a means for compression that reduces the parameters of a model without sparsification, and hence delivering more speedup on modern hardware.
no code implementations • 20 Dec 2023 • Ardavan S. Nobandegani, Irina Rish, Thomas R. Shultz
Widely considered a cornerstone of human morality, trust shapes many aspects of human social interactions.
no code implementations • 20 Feb 2024 • Md Rifat Arefin, Yan Zhang, Aristide Baratin, Francesco Locatello, Irina Rish, Dianbo Liu, Kenji Kawaguchi
Models prone to spurious correlations in training data often produce brittle predictions and introduce unintended biases.
no code implementations • 10 Apr 2024 • Sahil Garg, Anderson Schneider, Anant Raj, Kashif Rasul, Yuriy Nevmyvaka, Sneihil Gopal, Amit Dhurandhar, Guillermo Cecchi, Irina Rish
In addition to the data efficiency gained from direct sampling, we propose an algorithm that offers a significant reduction in sample complexity for estimating the divergence of the data distribution with respect to the marginal distribution.
1 code implementation • 31 Jan 2022 • Maxence Ernoult, Fabrice Normandin, Abhinav Moudgil, Sean Spinney, Eugene Belilovsky, Irina Rish, Blake Richards, Yoshua Bengio
As such, it is important to explore learning algorithms that come with strong theoretical guarantees and can match the performance of backpropagation (BP) on complex tasks.
1 code implementation • 6 Oct 2022 • Adam Ibrahim, Charles Guille-Escuret, Ioannis Mitliagkas, Irina Rish, David Krueger, Pouya Bashivan
Compared to existing methods, we obtain similar or superior worst-case adversarial robustness on attacks seen during training.
1 code implementation • 13 Dec 2021 • Matthew Riemer, Sharath Chandra Raparthy, Ignacio Cases, Gopeshh Subbaraj, Maximilian Puelma Touzel, Irina Rish
The mixing time of the Markov chain induced by a policy limits performance in real-world continual learning scenarios.
1 code implementation • 10 Nov 2017 • Sahil Garg, Aram Galstyan, Greg Ver Steeg, Irina Rish, Guillermo Cecchi, Shuyang Gao
Here we propose to use random subspaces of KLSH codes for efficiently constructing an explicit representation of NLP structures suitable for general classification methods.
1 code implementation • 22 Jan 2017 • Sahil Garg, Irina Rish, Guillermo Cecchi, Aurelie Lozano
In this paper, we focus on online representation learning in non-stationary environments which may require continuous adaptation of model architecture.
1 code implementation • 3 Feb 2018 • Baihan Lin, Djallel Bouneffouf, Guillermo Cecchi, Irina Rish
Our experiments on a variety of datasets, and both in stationary and non-stationary environments of several kinds demonstrate clear advantages of the proposed adaptive representation learning over the standard contextual bandit based on "raw" input contexts.
1 code implementation • 31 Mar 2020 • Victor Schmidt, Makesh Narsimhan Sreedhar, Mostafa ElAraby, Irina Rish
Unpaired Image-to-Image Translation (I2IT) tasks often suffer from lack of data, a problem which self-supervised learning (SSL) has recently been very popular and successful at tackling.
1 code implementation • NeurIPS 2021 • Pouya Bashivan, Reza Bayat, Adam Ibrahim, Kartik Ahuja, Mojtaba Faramarzi, Touraj Laleh, Blake Aaron Richards, Irina Rish
Our method, called Adversarial Feature Desensitization (AFD), aims at learning features that are invariant towards adversarial perturbations of the inputs.
1 code implementation • ICLR 2021 • Yoshua Bengio, Prateek Gupta, Tegan Maharaj, Nasim Rahaman, Martin Weiss, Tristan Deleu, Eilif Muller, Meng Qu, Victor Schmidt, Pierre-Luc St-Charles, Hannah Alsdurf, Olexa Bilanuik, David Buckeridge, Gáetan Marceau Caron, Pierre-Luc Carrier, Joumana Ghosn, Satya Ortiz-Gagne, Chris Pal, Irina Rish, Bernhard Schölkopf, Abhinav Sharma, Jian Tang, Andrew Williams
Predictions are used to provide personalized recommendations to the individual via an app, as well as to send anonymized messages to the individual's contacts, who use this information to better predict their own infectiousness, an approach we call proactive contact tracing (PCT).
1 code implementation • 4 Apr 2022 • Diganta Misra, Bharat Runwal, Tianlong Chen, Zhangyang Wang, Irina Rish
With the latest advances in deep learning, there has been a lot of focus on the online learning paradigm due to its relevance in practical settings.
1 code implementation • CVPR 2022 • Shanel Gauthier, Benjamin Thérien, Laurent Alsène-Racicot, Muawiz Chaudhary, Irina Rish, Eugene Belilovsky, Michael Eickenberg, Guy Wolf
The wavelet scattering transform creates geometric invariants and deformation stability.
1 code implementation • 24 Jun 2018 • Anna Choromanska, Benjamin Cowen, Sadhana Kumaravel, Ronny Luss, Mattia Rigotti, Irina Rish, Brian Kingsbury, Paolo DiAchille, Viatcheslav Gurev, Ravi Tejwani, Djallel Bouneffouf
Despite significant recent advances in deep neural networks, training them remains a challenge due to the highly non-convex nature of the objective function.
1 code implementation • 21 Jun 2019 • Baihan Lin, Guillermo Cecchi, Djallel Bouneffouf, Jenna Reinen, Irina Rish
Drawing an inspiration from behavioral studies of human decision making, we propose here a more general and flexible parametric framework for reinforcement learning that extends standard Q-learning to a two-stream model for processing positive and negative rewards, and allows to incorporate a wide range of reward-processing biases -- an important component of human decision making which can help us better understand a wide spectrum of multi-agent interactions in complex real-world socioeconomic systems, as well as various neuropsychiatric conditions associated with disruptions in normal reward processing.
1 code implementation • 10 May 2020 • Baihan Lin, Guillermo Cecchi, Djallel Bouneffouf, Jenna Reinen, Irina Rish
Artificial behavioral agents are often evaluated based on their consistent behaviors and performance to take sequential actions in an environment to maximize some notion of cumulative reward.
1 code implementation • 30 Apr 2022 • Oleksiy Ostapenko, Timothee Lesort, Pau Rodríguez, Md Rifat Arefin, Arthur Douillard, Irina Rish, Laurent Charlin
Motivated by this, we study the efficacy of pre-trained vision models as a foundation for downstream continual learning (CL) scenarios.
1 code implementation • 26 Oct 2022 • Ethan Caballero, Kshitij Gupta, Irina Rish, David Krueger
Moreover, this functional form accurately models and extrapolates scaling behavior that other functional forms are incapable of expressing such as the non-monotonic transitions present in the scaling behavior of phenomena such as double descent and the delayed, sharp inflection points present in the scaling behavior of tasks such as arithmetic.
1 code implementation • 18 Mar 2022 • Jean-Christophe Gagnon-Audet, Kartik Ahuja, Mohammad-Javad Darvishi-Bayazi, Pooneh Mousavi, Guillaume Dumas, Irina Rish
We revise the existing OOD generalization algorithms for time series tasks and evaluate them using our systematic framework.
1 code implementation • NeurIPS 2020 • Massimo Caccia, Pau Rodriguez, Oleksiy Ostapenko, Fabrice Normandin, Min Lin, Lucas Caccia, Issam Laradji, Irina Rish, Alexandre Lacoste, David Vazquez, Laurent Charlin
We propose Continual-MAML, an online extension of the popular MAML algorithm as a strong baseline for this scenario.
3 code implementations • 2 Aug 2021 • Fabrice Normandin, Florian Golemo, Oleksiy Ostapenko, Pau Rodriguez, Matthew D Riemer, Julio Hurtado, Khimya Khetarpal, Ryan Lindeborg, Lucas Cecchi, Timothée Lesort, Laurent Charlin, Irina Rish, Massimo Caccia
We propose a taxonomy of settings, where each setting is described as a set of assumptions.
11 code implementations • 19 Nov 2015 • Pouya Bashivan, Irina Rish, Mohammed Yeasin, Noel Codella
One of the challenges in modeling cognitive events from electroencephalogram (EEG) data is finding representations that are invariant to inter- and intra-subject differences, as well as to inherent noise associated with such data.
2 code implementations • 19 Sep 2023 • Mohammad-Javad Darvishi-Bayazi, Mohammad Sajjad Ghaemi, Timothee Lesort, Md Rifat Arefin, Jocelyn Faubert, Irina Rish
We see improvement in the performance of the target model on the target (NMT) datasets by using the knowledge from the source dataset (TUAB) when a low amount of labelled data was available.
1 code implementation • 12 Oct 2023 • Kashif Rasul, Arjun Ashok, Andrew Robert Williams, Hena Ghonia, Rishika Bhagwatkar, Arian Khorasani, Mohammad Javad Darvishi Bayazi, George Adamopoulos, Roland Riachi, Nadhir Hassen, Marin Biloš, Sahil Garg, Anderson Schneider, Nicolas Chapados, Alexandre Drouin, Valentina Zantedeschi, Yuriy Nevmyvaka, Irina Rish
Over the past years, foundation models have caused a paradigm shift in machine learning due to their unprecedented capabilities for zero-shot and few-shot generalization.
2 code implementations • 4 Jun 2021 • Soroosh Shahtalebi, Jean-Christophe Gagnon-Audet, Touraj Laleh, Mojtaba Faramarzi, Kartik Ahuja, Irina Rish
A major bottleneck in the real-world applications of machine learning models is their failure in generalizing to unseen domains whose data distribution is not i. i. d to the training domains.
2 code implementations • NeurIPS 2021 • Kartik Ahuja, Ethan Caballero, Dinghuai Zhang, Jean-Christophe Gagnon-Audet, Yoshua Bengio, Ioannis Mitliagkas, Irina Rish
To answer these questions, we revisit the fundamental assumptions in linear regression tasks, where invariance-based approaches were shown to provably generalize OOD.
2 code implementations • ICLR 2019 • Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, Gerald Tesauro
In this work we propose a new conceptualization of the continual learning problem in terms of a temporally symmetric trade-off between transfer and interference that can be optimized by enforcing gradient alignment across examples.
2 code implementations • 8 Aug 2023 • Kshitij Gupta, Benjamin Thérien, Adam Ibrahim, Mats L. Richter, Quentin Anthony, Eugene Belilovsky, Irina Rish, Timothée Lesort
We study the warmup phase of models pre-trained on the Pile (upstream data, 300B tokens) as we continue to pre-train on SlimPajama (downstream data, 297B tokens), following a linear warmup and cosine decay schedule.
1 code implementation • 13 Mar 2024 • Adam Ibrahim, Benjamin Thérien, Kshitij Gupta, Mats L. Richter, Quentin Anthony, Timothée Lesort, Eugene Belilovsky, Irina Rish
In this work, we show that a simple and scalable combination of learning rate (LR) re-warming, LR re-decaying, and replay of previous data is sufficient to match the performance of fully re-training from scratch on all available data, as measured by the final loss and the average score on several language model (LM) evaluation benchmarks.
3 code implementations • ICLR 2022 • Sarthak Mittal, Sharath Chandra Raparthy, Irina Rish, Yoshua Bengio, Guillaume Lajoie
Through our qualitative analysis, we demonstrate that Compositional Attention leads to dynamic specialization based on the type of retrieval needed.