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Accelerated Convergence for Counterfactual Learning to Rank
[article]
2020
pre-print
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Counterfactual Learning to Rank (LTR) algorithms learn a ranking model from logged user interactions, often collected using a production system. ...
One of the major difficulties in applying Stochastic Gradient Descent (SGD) approaches to counterfactual learning problems is the large variance introduced by the propensity weights. ...
ACKNOWLEDGMENTS We thank Chang Li, Harrie Oosterhuis and Ilya Markov for helpful discussions and feedback. We thank the anonymous reviewers for their feedback. ...
doi:10.1145/3397271.3401069
arXiv:2005.10615v1
fatcat:rjgwhs27tjcxxjqcyb2ti3cbei
Counterfactual Online Learning to Rank
[chapter]
2020
Lecture Notes in Computer Science
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Two main methods have arisen for optimizing rankers based on implicit feedback: counterfactual learning to rank (CLTR), which learns a ranker from the historical click-through data collected from a deployed ...
In this paper, we propose a counterfactual online learning to rank algorithm (COLTR) that combines the key components of both CLTR and OLTR. ...
Counterfactual Online Learning to Rank
Counterfactual Evaluation for Online Learning to Rank The proposed COLTR method uses counterfactual evaluation to estimate the effectiveness of candidate rankers ...
doi:10.1007/978-3-030-45439-5_28
fatcat:gpqp6bfqgza6tmd67sfdbqtyky
Algorithms in Multi-Agent Systems: A Holistic Perspective from Reinforcement Learning and Game Theory
[article]
2020
arXiv
pre-print
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Counterfactual regret minimization is an important tool to solve games with incomplete information, and has shown great strength when combined with deep learning. ...
Solution concepts from game theory give inspiration to algorithms which try to evaluate the agents or find better solutions in multi-agent systems. ...
UN-REAL framework [36] is based on A3C and proposes unsupervised auxiliary tasks like reward prediction to accelerate the learning process. ...
arXiv:2001.06487v3
fatcat:o2iovnsbxfgp5omk67jwuoonma
Double Neural Counterfactual Regret Minimization
[article]
2018
arXiv
pre-print
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To make neural learning efficient, we also developed several novel techniques including a robust sampling method, mini-batch Monte Carlo Counterfactual Regret Minimization (MCCFR) and Monte Carlo Counterfactual ...
Experimentally, we demonstrate that the proposed double neural algorithm converges significantly better than the reinforcement learning counterpart. ...
To avoid the algorithm converging to potential local minima or saddle point, we will reset the learning rate to 0.001 and help the optimizer to learn a better performance. θ T best is the best parameters ...
arXiv:1812.10607v1
fatcat:jtnlxl7iobh3pkrgxub2nocsva
Solving Games with Functional Regret Estimation
2015
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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The approach learns a function approximator online to estimate the regret for choosing a particular action. ...
We propose a novel online learning method for minimizing regret in large extensive-form games. ...
Thanks to Compute Canada for computational resources and the Computer Poker Research Group (CPRG) for software infrastructure support. ...
doi:10.1609/aaai.v29i1.9445
fatcat:ajtp6urom5fbxktdi6vvhckb2q
Solving Games with Functional Regret Estimation
[article]
2014
arXiv
pre-print
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The approach learns a function approximator online to estimate the regret for choosing a particular action. ...
We propose a novel online learning method for minimizing regret in large extensive-form games. ...
Thanks to Compute Canada for computational resources and the Computer Poker Research Group (CPRG) for software infrastructure support. ...
arXiv:1411.7974v2
fatcat:57tt42ntbbcvdlqt2jsuqdjway
Trumping Preemption
2000
Journal of Philosophy
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exclusively sensitive to ranking orders (as in trumping preemption). ...
not have occurred.) 20 Now, C is minimally counterfactually sufficient for E if and only if: (i) C is counterfactually sufficient for E, (ii) no proper subset of C is counterfactually sufficient for E ...
doi:10.2307/2678388
fatcat:pnc3zsreljasviyuob7wlbrmfi
Multi-Objective Counterfactual Explanations
[chapter]
2020
Lecture Notes in Computer Science
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Counterfactual explanations are one of the most popular methods to make predictions of black box machine learning models interpretable by providing explanations in the form of 'what-if scenarios'. ...
This enables a more detailed post-hoc analysis to facilitate better understanding and also more options for actionable user responses to change the predicted outcome. ...
Introduction Interpretable machine learning methods have become very important in recent years to explain the behavior of black box machine learning (ML) models. ...
doi:10.1007/978-3-030-58112-1_31
fatcat:qiww7zypcjaffmqonr2iww3bri
SCouT: Synthetic Counterfactuals via Spatiotemporal Transformers for Actionable Healthcare
[article]
2022
arXiv
pre-print
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to support randomized controlled trials, and a medical intervention for patients with Friedreich's ataxia to improve clinical decision-making and promote personalized therapy. ...
We instead propose an approach to use local spatiotemporal information before the onset of the intervention as a promising way to estimate the counterfactual sequence. ...
algorithm to learn a deep representation c i for the units using pre-intervention covariates. ...
arXiv:2207.04208v1
fatcat:b2ya3l3vzbcoho7mlgsjkh3uzu
Generating Plausible Counterfactual Explanations for Deep Transformers in Financial Text Classification
[article]
2020
arXiv
pre-print
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To address these issues, this paper proposes a novel methodology for producing plausible counterfactual explanations, whilst exploring the regularization benefits of adversarial training on language models ...
Exhaustive quantitative experiments demonstrate that not only does this approach improve the model accuracy when compared to the current state-of-the-art and human performance, but it also generates counterfactual ...
Also, we would like to thank the anonymous reviewers for their insightful comments and suggestions to help improve the paper. ...
arXiv:2010.12512v1
fatcat:ynkbj6j6srhj5fy6yymtqs7tfa
Teasing out the overall survival benefit with adjustment for treatment switching to other therapies
[article]
2019
arXiv
pre-print
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Several commonly used statistical methods are available to estimate overall survival benefit while adjusting for treatment switching, ranging from naive exclusion or censoring approaches to more advanced ...
methods including inverse probability of censoring weighting (IPCW), iterative parameter estimation (IPE) algorithm or rank-preserving structural failure time models (RPSFTM). ...
This iterative process continues until the new estimate for exp ( ) converges. ...
arXiv:1908.00654v1
fatcat:6kwvpbcmd5dztmi3phs27hmblq
Leveraging Sparse Linear Layers for Debuggable Deep Networks
[article]
2021
arXiv
pre-print
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We show how fitting sparse linear models over learned deep feature representations can lead to more debuggable neural networks. ...
The code for our toolkit can be found at https://github.com/madrylab/debuggabledeepnetworks. ...
Acknowledgements We thank Dimitris Tsipras for helpful discussions. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein. ...
arXiv:2105.04857v1
fatcat:fqwhkbr7krhnzal5ys74f5euci
Accelerating Economic Growth: the Science beneath the Art
2021
Economic Modelling
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However, a set of counterfactual analyses suggest that such major improvements in growth determinants are a necessary but hardly sufficient condition for success, failing to accelerate growth in 9 out ...
However, a set of counterfactual analyses suggest that such major improvements in growth determinants are a necessary but hardly sufficient condition for success, failing to accelerate growth in 9 out ...
This does not spell well for convergence, as it implies that richer countries make the most of growth accelerations. ...
doi:10.1016/j.econmod.2021.105593
fatcat:h2eibcbvhzbf5nwerev6zezuxa
Benchmark Evaluation of Counterfactual Algorithms for XAI: From a White Box to a Black Box
[article]
2022
arXiv
pre-print
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metrics; (2) the counterfactual generated are not impacted by the different types of machine learning models; (3) DiCE was the only tested algorithm that was able to generate actionable and plausible counterfactuals ...
We evaluated the different counterfactual algorithms using several metrics including proximity, interpretability and functionality for five datasets. ...
This work was also partially supported by Queensland University of Technology (QUT) Centre for Data Science First Byte Funding Program ...
arXiv:2203.02399v1
fatcat:fbbri4u4xvccxjmcg6tbkm2jxa
A Data-Based Perspective on Transfer Learning
[article]
2022
arXiv
pre-print
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In this work, we take a closer look at the role of the source dataset's composition in transfer learning and present a framework for probing its impact on downstream performance. ...
Our framework gives rise to new capabilities such as pinpointing transfer learning brittleness as well as detecting pathologies such as data-leakage and the presence of misleading examples in the source ...
To do so, we use the transfer influences computed for CIFAR-10 in order to perform the counterfactual experiments for other datasets. ...
arXiv:2207.05739v1
fatcat:dn5rtwaa65efpjkgpe5lw7y7ya
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