Publications

Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization
Kartik Ahuja
Ethan Caballero
Dinghuai Zhang
Jean-Christophe Gagnon-Audet
Yoshua Bengio
Ioannis Mitliagkas
Irina Rish
The invariance principle from causality is at the heart of notable approaches such as invariant risk minimization (IRM) that seek to address… (voir plus) out-of-distribution (OOD) generalization failures. Despite the promising theory, invariance principle-based approaches fail in common classification tasks, where invariant (causal) features capture all the information about the label. Are these failures due to the methods failing to capture the invariance? Or is the invariance principle itself insufficient? To answer these questions, we revisit the fundamental assumptions in linear regression tasks, where invariance-based approaches were shown to provably generalize OOD. In contrast to the linear regression tasks, we show that for linear classification tasks we need much stronger restrictions on the distribution shifts, or otherwise OOD generalization is impossible. Furthermore, even with appropriate restrictions on distribution shifts in place, we show that the invariance principle alone is insufficient. We prove that a form of the information bottleneck constraint along with invariance helps address key failures when invariant features capture all the information about the label and also retains the existing success when they do not. We propose an approach that incorporates both of these principles and demonstrate its effectiveness in several experiments.
openreview.net
Issue Link Label Recovery and Prediction for Open Source Software
Alexander Nicholson
Guo Jin L.C.
Jin Guo
Modern open source software development heavily relies on the issue tracking systems to manage their feature requests, bug reports, tasks, a… (voir plus)nd other similar artifacts. Together, those “issues” form a complex network with links to each other. The heterogeneous character of issues inherently results in varied link types and therefore poses a great challenge for users to create and maintain the label of the link manually. The goal of most existing automated issue link construction techniques ceases with only examining the existence of links between issues. In this work, we focus on the next important question of whether we can assess the type of issue link automatically through a data-driven method. We analyze the links between issues and their labels used the issue tracking system for 66 open source projects. Using three projects, we demonstrate promising results when using supervised machine learning classification for the task of link label recovery with careful model selection and tuning, achieving F1 scores of between 0.56-0.70 for the three studied projects. Further, the performance of our method for future link label prediction is convincing when there is sufficient historical data. Our work signifies the first step in systematically manage and maintain issue links faced in practice.
2021-01-01
2021 IEEE 29th International Requirements Engineering Conference Workshops (REW) (publié)
doi.org
arxiv.org
Learning Robust State Abstractions for Hidden-Parameter Block MDPs
Amy Zhang
Shagun Sodhani
Khimya Khetarpal
Joelle Pineau
2021-01-01
ICLR (publié)
openreview.net
openreview.net
Learning a Universal Template for Few-shot Dataset Generalization
Eleni Triantafillou
Hugo Larochelle
Richard Zemel
Vincent Dumoulin
2021-01-01
ICML (publié)
proceedings.mlr.press
arxiv.org
Learning with Gradient Descent and Weakly Convex Losses
Dominic Richards
Michael Rabbat
We study the learning performance of gradient descent when the empirical risk is weakly convex, namely, the smallest negative eigenvalue of … (voir plus)the empirical risk's Hessian is bounded in magnitude. By showing that this eigenvalue can control the stability of gradient descent, generalisation error bounds are proven that hold under a wider range of step sizes compared to previous work. Out of sample guarantees are then achieved by decomposing the test error into generalisation, optimisation and approximation errors, each of which can be bounded and traded off with respect to algorithmic parameters, sample size and magnitude of this eigenvalue. In the case of a two layer neural network, we demonstrate that the empirical risk can satisfy a notion of local weak convexity, specifically, the Hessian's smallest eigenvalue during training can be controlled by the normalisation of the layers, i.e., network scaling. This allows test error guarantees to then be achieved when the population risk minimiser satisfies a complexity assumption. By trading off the network complexity and scaling, insights are gained into the implicit bias of neural network scaling, which are further supported by experimental findings.
2021-01-01
AISTATS (publié)
proceedings.mlr.press
arxiv.org
MICo: Improved representations via sampling-based state similarity for Markov decision processes
Pablo Samuel Castro
Tyler Kastner
Prakash Panangaden
Mark Rowland
We present a new behavioural distance over the state space of a Markov decision process, and demonstrate the use of this distance as an effe… (voir plus)ctive means of shaping the learnt representations of deep reinforcement learning agents. While existing notions of state similarity are typically difficult to learn at scale due to high computational cost and lack of sample-based algorithms, our newly-proposed distance addresses both of these issues. In addition to providing detailed theoretical analyses, we provide empirical evidence that learning this distance alongside the value function yields structured and informative representations, including strong results on the Arcade Learning Environment benchmark.
openreview.net
MICo: Learning improved representations via sampling-based state similarity for Markov decision processes
Pablo Samuel Castro
Tyler Kastner
Prakash Panangaden
Mark Rowland
We present a new behavioural distance over the state space of a Markov decision process, and demonstrate the use of this distance as an eff… (voir plus)ective means of shaping the learnt representations of deep reinforcement learning agents. While existing notions of state similarity are typically difficult to learn at scale due to high computational cost and lack of sample-based algorithms, our newly-proposed distance addresses both of these issues. In addition to providing detailed theoretical analysis
2021-01-01
arXiv.org (prépublication)
dblp.uni-trier.de
Multi-Agent Estimation and Filtering for Minimizing Team Mean-Squared Error
Mohammad Afshari
Aditya Mahajan
Motivated by estimation problems arising in autonomous vehicles and decentralized control of unmanned aerial vehicles, we consider multi-age… (voir plus)nt estimation and filtering problems in which multiple agents generate state estimates based on decentralized information and the objective is to minimize a coupled mean-squared error which we call team mean-square error. We call the resulting estimates as minimum team mean-squared error (MTMSE) estimates. We show that MTMSE estimates are different from minimum mean-squared error (MMSE) estimates. We derive closed-form expressions for MTMSE estimates, which are linear function of the observations where the corresponding gain depends on the weight matrix that couples the estimation error. We then consider a filtering problem where a linear stochastic process is monitored by multiple agents which can share their observations (with delay) over a communication graph. We derive expressions to recursively compute the MTMSE estimates. To illustrate the effectiveness of the proposed scheme we consider an example of estimating the distances between vehicles in a platoon and show that MTMSE estimates significantly outperform MMSE estimates and consensus Kalman filtering estimates.
2021-01-01
IEEE Transactions on Signal Processing (publié)
doi.org
arxiv.org
Multilevel Approaches for the Critical Node Problem
Andrea Baggio
Margarida Carvalho
Andrea Lodi
Andrea Tramontani
2021-01-01
Operational Research (publié)
doi.org
A Novel Neural Network-Based Malware Severity Classification System
Miles Q. Li
Benjamin Fung
2021-01-01
International Conference on Software and Data Technologies (published)
doi.org
A Novel Neural Network-Based Malware Severity Classification System
Miles Q. Li
Benjamin Fung
2021-01-01
International Conference on Software and Data Technologies (publié)
doi.org
On-the-Fly Attention Modularization for Neural Generation
Yue Dong
Chandra Bhagavatula
Ximing Lu
Jena D. Hwang
Antoine Bosselut
Jackie Cheung
Yejin Choi
Despite considerable advancements with deep neural language models (LMs), neural text generation still suffers from de generation: generated… (voir plus) text is repetitive, generic, self-inconsistent, and lacking commonsense. The empirical analyses on sentence-level attention patterns reveal that neural text degeneration may be associated with insufficient learning of inductive biases by the attention mechanism. Our findings motivate on-the-fly attention modularization, a simple but effective method for injecting inductive biases into attention computation during inference. The resulting text produced by the language model with attention modularization can yield enhanced diversity and commonsense reasoning while maintaining fluency and coherence.
2021-01-01
arXiv.org (prépublication)
dblp.uni-trier.de