Publications

Towards Out-of-Distribution Adversarial Robustness

Adam Ibrahim

Charles Guille-Escuret

Adversarial robustness continues to be a major challenge for deep learning. A core issue is that robustness to one type of attack often fail… (voir plus)s to transfer to other attacks. While prior work establishes a theoretical trade-off in robustness against different

2023-06-20

ICML.cc/2023/Workshop/AdvML-Frontiers (publié)

doi.org

openreview.net

BatchGFN: Generative Flow Networks for Batch Active Learning

Shreshth A Malik

Salem Lahlou

Andrew Jesson

Moksh J. Jain

Nikolay Malkin

Tristan Deleu

Yoshua Bengio

Yarin Gal

We introduce BatchGFN—a novel approach for pool-based active learning that uses generative flow networks to sample sets of data points pro… (voir plus)portional to a batch reward. With an appropriate reward function to quantify the utility of acquiring a batch, such as the joint mutual information between the batch and the model parameters, BatchGFN is able to construct highly informative batches for active learning in a principled way. We show our approach enables sampling near-optimal utility batches at inference time with a single forward pass per point in the batch in toy regression problems. This alleviates the computational complexity of batch-aware algorithms and removes the need for greedy approximations to find maximizers for the batch reward. We also present early results for amortizing training across acquisition steps, which will enable scaling to real-world tasks.

2023-06-19

ICML.cc/2023/Workshop/SPIGM (poster)

doi.org

openreview.net

Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation

Chris Emezue

Alexandre Drouin

Tristan Deleu

Stefan Bauer

Yoshua Bengio

2023-06-19

ICML.cc/2023/Workshop/SPIGM (poster)

doi.org

openreview.net

CATS: A Computation-Aware Transaction Processing System with Proactive Unlocking

Bolun Zhu

Yu Hua

Ziyin Long

Xue (Steve) Liu

With the increasing complexity of network applications and high demands for QoS, transaction processing systems have received more attention… (voir plus)s due to salient features of simplicity and atomicity. Computation operations play an important role in transaction processing systems. However, conventional QoS-based mechanisms become inefficient due to the limited concurrent support upon computation operations, thus causing high time consumption in the critical path of concurrency control. In order to efficiently offer concurrent computations, we propose CATS, a Computation Aware Transaction processing System, to mitigate performance impacts caused by computation operations. CATS further leverages program semantics to defer the execution of transaction operations in the commit phase to alleviate unnecessary conflicts caused by computations. Extensive evaluation results demonstrate that CATS significantly outperforms state-of-the-art designs, including 2PL and OCC based transaction processing systems on high-contended and computation-intensive workloads. We have released the open-source codes in GitHub for public use.

2023-06-19

2023 IEEE/ACM 31st International Symposium on Quality of Service (IWQoS) (publié)

doi.org

CATS: A Computation-Aware Transaction Processing System with Proactive Unlocking

Bolun Zhu

Yu Hua

Ziyin Long

Xue (Steve) Liu

With the increasing complexity of network applications and high demands for QoS, transaction processing systems have received more attention… (voir plus)s due to salient features of simplicity and atomicity. Computation operations play an important role in transaction processing systems. However, conventional QoS-based mechanisms become inefficient due to the limited concurrent support upon computation operations, thus causing high time consumption in the critical path of concurrency control. In order to efficiently offer concurrent computations, we propose CATS, a Computation Aware Transaction processing System, to mitigate performance impacts caused by computation operations. CATS further leverages program semantics to defer the execution of transaction operations in the commit phase to alleviate unnecessary conflicts caused by computations. Extensive evaluation results demonstrate that CATS significantly outperforms state-of-the-art designs, including 2PL and OCC based transaction processing systems on high-contended and computation-intensive workloads. We have released the open-source codes in GitHub for public use.

2023-06-19

International Workshop on Quality of Service (published)

doi.org

Causal Discovery with Language Models as Imperfect Experts

Stephanie Long

Alexandre Piché

Valentina Zantedeschi

Tibor Schuster

Alexandre Drouin

Understanding the causal relationships that underlie a system is a fundamental prerequisite to accurate decision-making. In this work, we ex… (voir plus)plore how expert knowledge can be used to improve the data-driven identification of causal graphs, beyond Markov equivalence classes. In doing so, we consider a setting where we can query an expert about the orientation of causal relationships between variables, but where the expert may provide erroneous information. We propose strategies for amending such expert knowledge based on consistency properties, e.g., acyclicity and conditional independencies in the equivalence class. We then report a case study, on real data, where a large language model is used as an imperfect expert.

2023-06-19

ICML.cc/2023/Workshop/SPIGM (poster)

doi.org

openreview.net

Development of a hydrated electron dosimeter for radiotherapy applications: A proof of concept.

Julien Mégrourèche

H. Bekerat

Jingyi Bian

Alaina Bui

Jack C Sankey

Lilian Childress

Shirin A. Enger

BACKGROUND Hydrated electrons, which are short-lived products of radiolysis in water, increase the optical absorption of water, providing a … (voir plus)pathway toward near-tissue-equivalent clinical radiation dosimeters. This has been demonstrated in high-dose-per-pulse radiochemistry research, but, owing to the weak absorption signal, its application in existing low-dose-per-pulse radiotherapy provided by clinical linear accelerators (linacs) has yet to be investigated. PURPOSE The aims of this study were to measure the optical absorption associated with hydrated electrons produced by clinical linacs and to assess the suitability of the technique for radiotherapy (⩽ 1 cGy per pulse) applications. METHODS 40 mW of 660-nm laser light was sent five passes through deionized water contained in a 10 × 4 ×

2023-06-19

Medical Physics (publié)

doi.org

An Empirical Study of the Effectiveness of Using a Replay Buffer on Mode Discovery in GFlowNets

Nikhil Murali Vemgal

Elaine Lau

Doina Precup

Reinforcement Learning (RL) algorithms aim to learn an optimal policy by iteratively sampling actions to learn how to maximize the total exp… (voir plus)ected return,

2023-06-19

ICML.cc/2023/Workshop/SPIGM (poster)

doi.org

openreview.net

Exploring Exchangeable Dataset Amortization for Bayesian Posterior Inference

Sarthak Mittal

Niels Leif Bracher

Guillaume Lajoie

Priyank Jaini

Marcus A Brubaker

Bayesian inference provides a natural way of incorporating uncertainties and different underlying theories when making predictions or analyz… (voir plus)ing complex systems. However, it requires computationally expensive routines for approximation, which have to be re-run when new data is observed and are thus infeasible to efficiently scale and reuse. In this work, we look at the problem from the perspective of amortized inference to obtain posterior parameter distributions for known probabilistic models. We propose a neural network-based approach that can handle exchangeable observations and amortize over datasets to convert the problem of Bayesian posterior inference into a single forward pass of a network. Our empirical analyses explore various design choices for amortized inference by comparing: (a) our proposed variational objective with forward KL minimization, (b) permutation-invariant architectures like Transformers and DeepSets, and (c) parameterizations of posterior families like diagonal Gaussian and Normalizing Flows. Through our experiments, we successfully apply amortization techniques to estimate the posterior distributions for different domains solely through inference.

2023-06-19

ICML.cc/2023/Workshop/SPIGM (poster)

openreview.net