Publications

Consistency and Rate of Convergence of Switched Least Squares System Identification for Autonomous Switched Linear Systems
Borna Sayedana
Mohammad Afshari
Peter E. Caines
Aditya Mahajan
In this paper, we investigate the problem of system identification for autonomous switched linear systems with complete state observations.… (see more) We propose switched least squares method for the identification for switched linear systems, show that this method is strongly consistent, and derive data-dependent and data-independent rates of convergence. In particular, our data-dependent rate of convergence shows that, almost surely, the system identification error is O (cid:0)(cid:112) log( T ) /T (cid:1) where T is the time horizon. These results show that our method for switched linear systems has the same rate of convergence as least squares method for non-switched linear systems. We compare our results with those in the literature. We present numerical examples to illustrate the performance of the proposed system identification method.
2021-01-01
arXiv.org (preprint)
dblp.uni-trier.de
Continual Learning via Local Module Composition
Oleksiy Ostapenko
Pau Rodriguez
Massimo Caccia
Laurent Charlin
Modularity is a compelling solution to continual learning (CL), the problem of modeling sequences of related tasks. Learning and then compos… (see more)ing modules to solve different tasks provides an abstraction to address the principal challenges of CL including catastrophic forgetting, backward and forward transfer across tasks, and sub-linear model growth. We introduce local module composition (LMC), an approach to modular CL where each module is provided a local structural component that estimates a module's relevance to the input. Dynamic module composition is performed layer-wise based on local relevance scores. We demonstrate that agnosticity to task identities (IDs) arises from (local) structural learning that is module-specific as opposed to the task- and/or model-specific as in previous works, making LMC applicable to more CL settings compared to previous works. In addition, LMC also tracks statistics about the input distribution and adds new modules when outlier samples are detected. In the first set of experiments, LMC performs favorably compared to existing methods on the recent Continual Transfer-learning Benchmark without requiring task identities. In another study, we show that the locality of structural learning allows LMC to interpolate to related but unseen tasks (OOD), as well as to compose modular networks trained independently on different task sequences into a third modular network without any fine-tuning. Finally, in search for limitations of LMC we study it on more challenging sequences of 30 and 100 tasks, demonstrating that local module selection becomes much more challenging in presence of a large number of candidate modules. In this setting best performing LMC spawns much fewer modules compared to an oracle based baseline, however, it reaches a lower overall accuracy. The codebase is available under https://github.com/oleksost/LMC.
openreview.net
Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning
Rishabh Agarwal
Marlos C. Machado
Pablo Samuel Castro
Marc Gendron-Bellemare
Reinforcement learning methods trained on few environments rarely learn policies that generalize to unseen environments. To improve generali… (see more)zation, we incorporate the inherent sequential structure in reinforcement learning into the representation learning process. This approach is orthogonal to recent approaches, which rarely exploit this structure explicitly. Specifically, we introduce a theoretically motivated policy similarity metric (PSM) for measuring behavioral similarity between states. PSM assigns high similarity to states for which the optimal policies in those states as well as in future states are similar. We also present a contrastive representation learning procedure to embed any state similarity metric, which we instantiate with PSM to obtain policy similarity embeddings (PSEs). We demonstrate that PSEs improve generalization on diverse benchmarks, including LQR with spurious correlations, a jumping task from pixels, and Distracting DM Control Suite.
2021-01-01
ICLR (published)
openreview.net
openreview.net
Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization
Chin-Wei Huang
Ricky T. Q. Chen
Christos Tsirigotis
Aaron Courville
Flow-based models are powerful tools for designing probabilistic models with tractable density. This paper introduces Convex Potential Flows… (see more) (CP-Flow), a natural and efficient parameterization of invertible models inspired by the optimal transport (OT) theory. CP-Flows are the gradient map of a strongly convex neural potential function. The convexity implies invertibility and allows us to resort to convex optimization to solve the convex conjugate for efficient inversion. To enable maximum likelihood training, we derive a new gradient estimator of the log-determinant of the Jacobian, which involves solving an inverse-Hessian vector product using the conjugate gradient method. The gradient estimator has constant-memory cost, and can be made effectively unbiased by reducing the error tolerance level of the convex optimization routine. Theoretically, we prove that CP-Flows are universal density approximators and are optimal in the OT sense. Our empirical results show that CP-Flow performs competitively on standard benchmarks of density estimation and variational inference.
2021-01-01
ICLR (published)
openreview.net
openreview.net
Cooperative Semi-Supervised Transfer Learning of Machine Reading Comprehension
Oliver Bender
Franz Josef Och
Yoshua Bengio
R´ejean Ducharme
Pascal Vincent
Kevin Clark
Quoc Minh-Thang Luong
V. Le
Jacob Devlin
Ming-Wei Chang
Kenton Lee
Adam Fisch
Alon Talmor
Robin Jia
Minjoon Seo
Michael R. Glass
A. Gliozzo
Rishav Chakravarti
Ian J Goodfellow
Jean Pouget-Abadie … (see 39 more)
Mehdi Mirza
Serhii Havrylov
Ivan Titov. 2017
Emergence
Jun-Tao He
Jiatao Gu
Jiajun Shen
Marc’Aurelio
Matthew Henderson
I. Casanueva
Nikola Mrkˇsi´c
Pei-hao Su
Tsung-Hsien Wen
Ivan Vuli´c
Yikang Shen
Yi Tay
Che Zheng
Dara Bahri
Donald
Metzler Aaron
Courville
Structformer
Ashish Vaswani
Noam M. Shazeer
Niki Parmar
Thomas Wolf
Lysandre Debut
Julien Victor Sanh
Clement Chaumond
Anthony Delangue
Pier-339 Moi
Tim ric Cistac
R´emi Rault
Morgan Louf
Qizhe Xie
Eduard H. Hovy
Silei Xu
Sina Jandaghi Semnani
Giovanni Campagna
Pretrained language models have significantly 001 improved the performance of down-stream 002 language understanding tasks, including ex-00… (see more)3 tractive question answering, by providing 004 high-quality contextualized word embeddings. 005 However, training question answering models 006 still requires large amounts of annotated data 007 for specific domains. In this work, we pro-008 pose a cooperative, self-play learning frame-009 work, REGEX, for automatically generating 010 more non-trivial question-answer pairs to im-011 prove model performance. REGEX is built 012 upon a masked answer extraction task with an 013 interactive learning environment containing an 014 answer entity REcognizer, a question Gener-015 ator, and an answer EXtractor. Given a pas-016 sage with a masked entity, the generator gen-017 erates a question around the entity, and the 018 extractor is trained to extract the masked en-019 tity with the generated question and raw texts. 020 The framework allows the training of question 021 generation and answering models on any text 022 corpora without annotation. We further lever-023 age a reinforcement learning technique to re-024 ward generating high-quality questions and to 025 improve the answer extraction model’s perfor-026 mance. Experiment results show that REGEX 027 outperforms the state-of-the-art (SOTA) pre-028 trained language models and transfer learning 029 approaches on standard question-answering 030 benchmarks, and yields the new SOTA per-031 formance under given model size and transfer 032 learning settings. 033
Data-Efficient Reinforcement Learning with Self-Predictive Representations
Max Schwarzer
Ankesh Anand
Rishab Goel
(Rex) Devon Hjelm
Aaron Courville
Philip Bachman
While deep reinforcement learning excels at solving tasks where large amounts of data can be collected through virtually unlimited interacti… (see more)on with the environment, learning from limited interaction remains a key challenge. We posit that an agent can learn more efficiently if we augment reward maximization with self-supervised objectives based on structure in its visual input and sequential interaction with the environment. Our method, Self-Predictive Representations (SPR), trains an agent to predict its own latent state representations multiple steps into the future. We compute target representations for future states using an encoder which is an exponential moving average of the agent’s parameters and we make predictions using a learned transition model. On its own, this future prediction objective outperforms prior methods for sample-efficient deep RL from pixels. We further improve performance by adding data augmentation to the future prediction loss, which forces the agent’s representations to be consistent across multiple views of an observation. Our full self-supervised objective, which combines future prediction and data augmentation, achieves a median human-normalized score of 0.415 on Atari in a setting limited to 100k steps of environment interaction, which represents a 55% relative improvement over the previous state-of-the-art. Notably, even in this limited data regime, SPR exceeds expert human scores on 7 out of 26 games. We’ve made the code associated with this work available at https://github.com/mila-iqia/spr.
2021-01-01
ICLR (published)
openreview.net
openreview.net
DATA-EFFICIENT REINFORCEMENT LEARNING
Nitarshan Rajkumar
Michael Noukhovitch
Ankesh Anand
Laurent Charlin
(Rex) Devon Hjelm
Philip Bachman
Aaron Courville
Data efficiency poses a major challenge for deep reinforcement learning. We approach this issue from the perspective of self-supervised repr… (see more)esentation learning, leveraging reward-free exploratory data to pretrain encoder networks. We employ a novel combination of latent dynamics modelling and goal-reaching objectives, which exploit the inherent structure of data in reinforcement learning. We demonstrate that our method scales well with network capacity and pretraining data. When evaluated on the Atari 100k data-efficiency benchmark, our approach significantly outperforms previous methods combining unsupervised pretraining with task-specific finetuning, and approaches human-level performance.
Deep LDA-Pruned Nets for Efficient Facial Gender Classification
Qing Tian
Tal Arbel
James J. Clark
Many real-time tasks, such as human-computer interac-tion, require fast and efficient facial gender classification. Although deep CNN nets… (see more) have been very effective for a mul-titude of classification tasks, their high space and time de-mands make them impractical for personal computers and mobile devices without a powerful GPU. In this paper, we develop a 16-layer, yet lightweight, neural network which boosts efficiency while maintaining high accuracy. Our net is pruned from the VGG-16 model [35] starting from the last convolutional (conv) layer where we find neuron activations are highly uncorrelated given the gender. Through Fisher’s Linear Discriminant Analysis (LDA) [8], we show that this high decorrelation makes it safe to discard directly last conv layer neurons with high within-class variance and low between-class variance. Combined with either Support Vector Machines (SVM) or Bayesian classification, the reduced CNNs are capable of achieving comparable (or even higher) accuracies on the LFW and CelebA datasets than the original net with fully connected layers. On LFW, only four Conv5 3 neurons are able to maintain a comparably high recognition accuracy, which results in a reduction of total network size by a factor of 70X with a 11 fold speedup. Comparisons with a state-of-the-art pruning method [12] (as well as two smaller nets [20, 24]) in terms of accuracy loss and convolutional layers pruning rate are also provided.
2021-01-01
(published)
www.semanticscholar.org
Deep Reinforcement Learning at the Edge of the Statistical Precipice
Rishabh Agarwal
Max Schwarzer
Pablo Samuel Castro
Aaron Courville
Marc Gendron-Bellemare
Deep reinforcement learning (RL) algorithms are predominantly evaluated by comparing their relative performance on a large suite of tasks. M… (see more)ost published results on deep RL benchmarks compare point estimates of aggregate performance such as mean and median scores across tasks, ignoring the statistical uncertainty implied by the use of a finite number of training runs. Beginning with the Arcade Learning Environment (ALE), the shift towards computationally-demanding benchmarks has led to the practice of evaluating only a small number of runs per task, exacerbating the statistical uncertainty in point estimates. In this paper, we argue that reliable evaluation in the few run deep RL regime cannot ignore the uncertainty in results without running the risk of slowing down progress in the field. We illustrate this point using a case study on the Atari 100k benchmark, where we find substantial discrepancies between conclusions drawn from point estimates alone versus a more thorough statistical analysis. With the aim of increasing the field's confidence in reported results with a handful of runs, we advocate for reporting interval estimates of aggregate performance and propose performance profiles to account for the variability in results, as well as present more robust and efficient aggregate metrics, such as interquartile mean scores, to achieve small uncertainty in results. Using such statistical tools, we scrutinize performance evaluations of existing algorithms on other widely used RL benchmarks including the ALE, Procgen, and the DeepMind Control Suite, again revealing discrepancies in prior comparisons. Our findings call for a change in how we evaluate performance in deep RL, for which we present a more rigorous evaluation methodology, accompanied with an open-source library rliable, to prevent unreliable results from stagnating the field. This work received an outstanding paper award at NeurIPS 2021.
openreview.net
Dynamic Inference with Neural Interpreters
Nasim Rahaman
Muhammad Waleed Gondal
Shruti Joshi
Peter Vincent Gehler
Yoshua Bengio
Francesco Locatello
Bernhard Schölkopf
Modern neural network architectures can leverage large amounts of data to generalize well within the training distribution. However, they ar… (see more)e less capable of systematic generalization to data drawn from unseen but related distributions, a feat that is hypothesized to require compositional reasoning and reuse of knowledge. In this work, we present Neural Interpreters, an architecture that factorizes inference in a self-attention network as a system of modules, which we call _functions_. Inputs to the model are routed through a sequence of functions in a way that is end-to-end learned. The proposed architecture can flexibly compose computation along width and depth, and lends itself well to capacity extension after training. To demonstrate the versatility of Neural Interpreters, we evaluate it in two distinct settings: image classification and visual abstract reasoning on Raven Progressive Matrices. In the former, we show that Neural Interpreters perform on par with the vision transformer using fewer parameters, while being transferrable to a new task in a sample efficient manner. In the latter, we find that Neural Interpreters are competitive with respect to the state-of-the-art in terms of systematic generalization.
openreview.net
Embedding Signals on Knowledge Graphs with Unbalanced Diffusion Earth Mover's Distance
Alexander Tong
Guillaume Huguet
Dennis L. Shung
Amine Natik
Manik Kuchroo
Guillaume Lajoie
Guy Wolf
Smita Krishnaswamy
In modern relational machine learning it is common to encounter large graphs that arise via interactions or similarities between observation… (see more)s in many domains. Further
2021-01-01
arXiv.org (preprint)
dblp.uni-trier.de
Embedding Signals on Knowledge Graphs with Unbalanced Diffusion Earth Mover's Distance
Alexander Tong
Guillaume Huguet
Dennis Shung
Amine Natik
Manik Kuchroo
Guillaume Lajoie
Guy Wolf
Smita Krishnaswamy
In modern relational machine learning it is common to encounter large graphs that arise via interactions or similarities between observation… (see more)s in many domains. Further
2021-01-01
arXiv.org (preprint)
dblp.uni-trier.de