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@InProceedings{schut2021generating,
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  title         = {Generating {{Interpretable Counterfactual Explanations By Implicit Minimisation}} of {{Epistemic}} and {{Aleatoric Uncertainties}}},
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@InProceedings{slack2020fooling,
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@Unpublished{spooner2021counterfactual,
  author        = {Spooner, Thomas and Dervovic, Danial and Long, Jason and Shepard, Jon and Chen, Jiahao and Magazzeni, Daniele},
  title         = {Counterfactual {{Explanations}} for {{Arbitrary Regression Models}}},
  archiveprefix = {arXiv},
  date-added    = {2022-12-13 12:58:01 +0100},
  date-modified = {2022-12-13 12:58:01 +0100},
  eprint        = {2106.15212},
  eprinttype    = {arxiv},
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  year          = {2021},
}

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@Unpublished{stanton2022bayesian,
  author        = {Stanton, Samuel and Maddox, Wesley and Wilson, Andrew Gordon},
  title         = {Bayesian {{Optimization}} with {{Conformal Coverage Guarantees}}},
  archiveprefix = {arXiv},
  date-added    = {2022-12-13 12:58:01 +0100},
  date-modified = {2022-12-13 12:58:01 +0100},
  eprint        = {2210.12496},
  eprinttype    = {arxiv},
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}

@Article{sturm2014simple,
  author        = {Sturm, Bob L},
  title         = {A Simple Method to Determine If a Music Information Retrieval System Is a ``Horse''},
  number        = {6},
  pages         = {1636--1644},
  volume        = {16},
  date-added    = {2022-12-13 12:58:01 +0100},
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  journal       = {IEEE Transactions on Multimedia},
  year          = {2014},
}

@Article{sunstein2003libertarian,
  author        = {Sunstein, Cass R and Thaler, Richard H},
  title         = {Libertarian Paternalism Is Not an Oxymoron},
  pages         = {1159--1202},
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  year          = {2003},
}

@Book{sutton2018reinforcement,
  author        = {Sutton, Richard S and Barto, Andrew G},
  title         = {Reinforcement Learning: {{An}} Introduction},
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@Unpublished{szegedy2013intriguing,
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  title         = {Intriguing Properties of Neural Networks},
  archiveprefix = {arXiv},
  date-added    = {2022-12-13 12:58:01 +0100},
  date-modified = {2022-12-13 12:58:01 +0100},
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  year          = {2013},
}

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  author        = {Thaler, Richard},
  title         = {Some Empirical Evidence on Dynamic Inconsistency},
  number        = {3},
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  author        = {Thaler, Richard H and Benartzi, Shlomo},
  title         = {Save More Tomorrow{\texttrademark}: {{Using}} Behavioral Economics to Increase Employee Saving},
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}

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  title         = {The Framing of Decisions and the Psychology of Choice},
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  volume        = {211},
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  year          = {1981},
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  author        = {Ungemach, Christoph and Stewart, Neil and Reimers, Stian},
  title         = {How Incidental Values from the Environment Affect Decisions about Money, Risk, and Delay},
  number        = {2},
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}

@Unpublished{upadhyay2021robust,
  author        = {Upadhyay, Sohini and Joshi, Shalmali and Lakkaraju, Himabindu},
  title         = {Towards {{Robust}} and {{Reliable Algorithmic Recourse}}},
  archiveprefix = {arXiv},
  date-added    = {2022-12-13 12:58:01 +0100},
  date-modified = {2022-12-13 12:58:01 +0100},
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  year          = {2021},
}

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  booktitle     = {Proceedings of the {{Conference}} on {{Fairness}}, {{Accountability}}, and {{Transparency}}},
  title         = {Actionable Recourse in Linear Classification},
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  title         = {Egocentric Empathy Gaps between Owners and Buyers: Misperceptions of the Endowment Effect.},
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@Book{varshney2022trustworthy,
  author        = {Varshney, Kush R.},
  title         = {Trustworthy {{Machine Learning}}},
  publisher     = {{Independently Published}},
  address       = {{Chappaqua, NY, USA}},
  date-added    = {2022-12-13 12:58:01 +0100},
  date-modified = {2022-12-13 12:58:01 +0100},
  year          = {2022},
}

@Unpublished{verma2020counterfactual,
  author        = {Verma, Sahil and Dickerson, John and Hines, Keegan},
  title         = {Counterfactual Explanations for Machine Learning: {{A}} Review},
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  date-added    = {2022-12-13 12:58:01 +0100},
  date-modified = {2022-12-13 12:58:01 +0100},
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  eprinttype    = {arxiv},
  year          = {2020},
}

@Article{verstyuk2020modeling,
  author        = {Verstyuk, Sergiy},
  title         = {Modeling Multivariate Time Series in Economics: {{From}} Auto-Regressions to Recurrent Neural Networks},
  date-added    = {2022-12-13 12:58:01 +0100},
  date-modified = {2022-12-13 12:58:01 +0100},
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  year          = {2020},
}

@Article{wachter2017counterfactual,
  author        = {Wachter, Sandra and Mittelstadt, Brent and Russell, Chris},
  title         = {Counterfactual Explanations without Opening the Black Box: {{Automated}} Decisions and the {{GDPR}}},
  pages         = {841},
  volume        = {31},
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  year          = {2018},
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@Book{wasserman2006all,
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@Book{wasserman2013all,
  author        = {Wasserman, Larry},
  title         = {All of Statistics: A Concise Course in Statistical Inference},
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@Unpublished{wilson2020case,
  author        = {Wilson, Andrew Gordon},
  title         = {The Case for {{Bayesian}} Deep Learning},
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@Article{xu2020epidemiological,
  author        = {Xu, Bo and Gutierrez, Bernardo and Mekaru, Sumiko and Sewalk, Kara and Goodwin, Lauren and Loskill, Alyssa and Cohn, Emily and Hswen, Yulin and Hill, Sarah C. and Cobo, Maria M and Zarebski, Alexander and Li, Sabrina and Wu, Chieh-Hsi and Hulland, Erin and Morgan, Julia and Wang, Lin and O'Brien, Katelynn and Scarpino, Samuel V. and Brownstein, John S. and Pybus, Oliver G. and Pigott, David M. and Kraemer, Moritz U. G.},
  title         = {Epidemiological Data from the {{COVID-19}} Outbreak, Real-Time Case Information},
  doi           = {doi.org/10.1038/s41597-020-0448-0},
  number        = {106},
  volume        = {7},
  bdsk-url-1    = {https://doi.org/10.1038/s41597-020-0448-0},
  date-added    = {2022-12-13 12:58:01 +0100},
  date-modified = {2022-12-13 12:58:01 +0100},
  journal       = {Scientific Data},
  year          = {2020},
}

@Article{yeh2009comparisons,
  author        = {Yeh, I-Cheng and Lien, Che-hui},
  title         = {The Comparisons of Data Mining Techniques for the Predictive Accuracy of Probability of Default of Credit Card Clients},
  number        = {2},
  pages         = {2473--2480},
  volume        = {36},
  date-added    = {2022-12-13 12:58:01 +0100},
  date-modified = {2022-12-13 12:58:01 +0100},
  journal       = {Expert systems with applications},
  year          = {2009},
}

@Article{zhang1998forecasting,
  author        = {Zhang, Guoqiang and Patuwo, B Eddy and Hu, Michael Y},
  title         = {Forecasting with Artificial Neural Networks:: {{The}} State of the Art},
  number        = {1},
  pages         = {35--62},
  volume        = {14},
  date-added    = {2022-12-13 12:58:01 +0100},
  date-modified = {2022-12-13 12:58:01 +0100},
  journal       = {International journal of forecasting},
  year          = {1998},
}

@Article{zhang2003time,
  author        = {Zhang, G Peter},
  title         = {Time Series Forecasting Using a Hybrid {{ARIMA}} and Neural Network Model},
  pages         = {159--175},
  volume        = {50},
  date-added    = {2022-12-13 12:58:01 +0100},
  date-modified = {2022-12-13 12:58:01 +0100},
  journal       = {Neurocomputing},
  year          = {2003},
}

@Unpublished{zheng2018dags,
  author        = {Zheng, Xun and Aragam, Bryon and Ravikumar, Pradeep and Xing, Eric P},
  title         = {Dags with No Tears: {{Continuous}} Optimization for Structure Learning},
  archiveprefix = {arXiv},
  date-added    = {2022-12-13 12:58:01 +0100},
  date-modified = {2022-12-13 12:58:01 +0100},
  eprint        = {1803.01422},
  eprinttype    = {arxiv},
  year          = {2018},
}

@Article{zhu2015optimal,
  author        = {Zhu, Rong and Ma, Ping and Mahoney, Michael W and Yu, Bin},
  title         = {Optimal Subsampling Approaches for Large Sample Linear Regression},
  pages         = {arXiv--1509},
  date-added    = {2022-12-13 12:58:01 +0100},
  date-modified = {2022-12-13 12:58:01 +0100},
  journal       = {arXiv},
  year          = {2015},
}

@Article{barber2021predictive,
  author    = {Barber, Rina Foygel and Candès, Emmanuel J. and Ramdas, Aaditya and Tibshirani, Ryan J.},
  title     = {Predictive inference with the jackknife+},
  doi       = {10.1214/20-AOS1965},
  issn      = {0090-5364, 2168-8966},
  number    = {1},
  pages     = {486--507},
  urldate   = {2022-12-13},
  volume    = {49},
  abstract  = {This paper introduces the jackknife+, which is a novel method for constructing predictive confidence intervals. Whereas the jackknife outputs an interval centered at the predicted response of a test point, with the width of the interval determined by the quantiles of leave-one-out residuals, the jackknife+ also uses the leave-one-out predictions at the test point to account for the variability in the fitted regression function. Assuming exchangeable training samples, we prove that this crucial modification permits rigorous coverage guarantees regardless of the distribution of the data points, for any algorithm that treats the training points symmetrically. Such guarantees are not possible for the original jackknife and we demonstrate examples where the coverage rate may actually vanish. Our theoretical and empirical analysis reveals that the jackknife and the jackknife+ intervals achieve nearly exact coverage and have similar lengths whenever the fitting algorithm obeys some form of stability. Further, we extend the jackknife+ to \$K\$-fold cross validation and similarly establish rigorous coverage properties. Our methods are related to cross-conformal prediction proposed by Vovk (Ann. Math. Artif. Intell. 74 (2015) 9–28) and we discuss connections.},
  file      = {:Barber2021 - Predictive Inference with the Jackknife+.pdf:PDF},
  journal   = {The Annals of Statistics},
  keywords  = {62F40, 62G08, 62G09, conformal inference, cross-validation, distribution-free, jackknife, leave-one-out, stability},
  month     = feb,
  publisher = {Institute of Mathematical Statistics},
  year      = {2021},
}

@TechReport{chouldechova2018frontiers,
  author        = {Chouldechova, Alexandra and Roth, Aaron},
  title         = {The {Frontiers} of {Fairness} in {Machine} {Learning}},
  doi           = {10.48550/arXiv.1810.08810},
  eprint        = {1810.08810},
  note          = {arXiv:1810.08810 [cs, stat] type: article},
  abstract      = {The last few years have seen an explosion of academic and popular interest in algorithmic fairness. Despite this interest and the volume and velocity of work that has been produced recently, the fundamental science of fairness in machine learning is still in a nascent state. In March 2018, we convened a group of experts as part of a CCC visioning workshop to assess the state of the field, and distill the most promising research directions going forward. This report summarizes the findings of that workshop. Along the way, it surveys recent theoretical work in the field and points towards promising directions for research.},
  archiveprefix = {arxiv},
  file          = {:chouldechova2018frontiers - The Frontiers of Fairness in Machine Learning.pdf:PDF},
  keywords      = {Computer Science - Machine Learning, Computer Science - Data Structures and Algorithms, Computer Science - Computer Science and Game Theory, Statistics - Machine Learning},
  month         = oct,
  school        = {arXiv},
  year          = {2018},
}

@Article{pawelczyk2022probabilistically,
  author     = {Pawelczyk, Martin and Datta, Teresa and van-den-Heuvel, Johannes and Kasneci, Gjergji and Lakkaraju, Himabindu},
  title      = {Probabilistically {Robust} {Recourse}: {Navigating} the {Trade}-offs between {Costs} and {Robustness} in {Algorithmic} {Recourse}},
  file       = {:pawelczyk2022probabilistically - Probabilistically Robust Recourse_ Navigating the Trade Offs between Costs and Robustness in Algorithmic Recourse.pdf:PDF},
  journal    = {arXiv preprint arXiv:2203.06768},
  shorttitle = {Probabilistically {Robust} {Recourse}},
  year       = {2022},
}

@InProceedings{stutz2022learning,
  author   = {Stutz, David and Dvijotham, Krishnamurthy Dj and Cemgil, Ali Taylan and Doucet, Arnaud},
  title    = {Learning {Optimal} {Conformal} {Classifiers}},
  language = {en},
  url      = {https://openreview.net/forum?id=t8O-4LKFVx},
  urldate  = {2023-02-13},
  abstract = {Modern deep learning based classifiers show very high accuracy on test data but this does not provide sufficient guarantees for safe deployment, especially in high-stake AI applications such as medical diagnosis. Usually, predictions are obtained without a reliable uncertainty estimate or a formal guarantee. Conformal prediction (CP) addresses these issues by using the classifier's predictions, e.g., its probability estimates, to predict confidence sets containing the true class with a user-specified probability. However, using CP as a separate processing step after training prevents the underlying model from adapting to the prediction of confidence sets. Thus, this paper explores strategies to differentiate through CP during training with the goal of training model with the conformal wrapper end-to-end. In our approach, conformal training (ConfTr), we specifically "simulate" conformalization on mini-batches during training. Compared to standard training, ConfTr reduces the average confidence set size (inefficiency) of state-of-the-art CP methods applied after training. Moreover, it allows to "shape" the confidence sets predicted at test time, which is difficult for standard CP. On experiments with several datasets, we show ConfTr can influence how inefficiency is distributed across classes, or guide the composition of confidence sets in terms of the included classes, while retaining the guarantees offered by CP.},
  file     = {:stutz2022learning - Learning Optimal Conformal Classifiers.pdf:PDF},
  month    = may,
  year     = {2022},
}

@InProceedings{grathwohl2020your,
  author   = {Grathwohl, Will and Wang, Kuan-Chieh and Jacobsen, Joern-Henrik and Duvenaud, David and Norouzi, Mohammad and Swersky, Kevin},
  title    = {Your classifier is secretly an energy based model and you should treat it like one},
  language = {en},
  url      = {https://openreview.net/forum?id=Hkxzx0NtDB},
  urldate  = {2023-02-13},
  abstract = {We propose to reinterpret a standard discriminative classifier of p(y{\textbar}x) as an energy based model for the joint distribution p(x, y). In this setting, the standard class probabilities can be easily computed as well as unnormalized values of p(x) and p(x{\textbar}y). Within this framework, standard discriminative architectures may be used and the model can also be trained on unlabeled data. We demonstrate that energy based training of the joint distribution improves calibration, robustness, and out-of-distribution detection while also enabling our models to generate samples rivaling the quality of recent GAN approaches. We improve upon recently proposed techniques for scaling up the training of energy based models and present an approach which adds little overhead compared to standard classification training. Our approach is the first to achieve performance rivaling the state-of-the-art in both generative and discriminative learning within one hybrid model.},
  file     = {:grathwohl2020your - Your Classifier Is Secretly an Energy Based Model and You Should Treat It like One.pdf:PDF},
  month    = mar,
  year     = {2020},
}

@Book{murphy2023probabilistic,
  author     = {Murphy, Kevin P.},
  date       = {2023},
  title      = {Probabilistic machine learning: {Advanced} topics},
  publisher  = {MIT Press},
  shorttitle = {Probabilistic machine learning},
}

@TechReport{artelt2021evaluating,
  author      = {Artelt, André and Vaquet, Valerie and Velioglu, Riza and Hinder, Fabian and Brinkrolf, Johannes and Schilling, Malte and Hammer, Barbara},
  date        = {2021-07},
  institution = {arXiv},
  title       = {Evaluating {Robustness} of {Counterfactual} {Explanations}},
  note        = {arXiv:2103.02354 [cs] type: article},
  url         = {http://arxiv.org/abs/2103.02354},
  urldate     = {2023-03-24},
  abstract    = {Transparency is a fundamental requirement for decision making systems when these should be deployed in the real world. It is usually achieved by providing explanations of the system's behavior. A prominent and intuitive type of explanations are counterfactual explanations. Counterfactual explanations explain a behavior to the user by proposing actions -- as changes to the input -- that would cause a different (specified) behavior of the system. However, such explanation methods can be unstable with respect to small changes to the input -- i.e. even a small change in the input can lead to huge or arbitrary changes in the output and of the explanation. This could be problematic for counterfactual explanations, as two similar individuals might get very different explanations. Even worse, if the recommended actions differ considerably in their complexity, one would consider such unstable (counterfactual) explanations as individually unfair. In this work, we formally and empirically study the robustness of counterfactual explanations in general, as well as under different models and different kinds of perturbations. Furthermore, we propose that plausible counterfactual explanations can be used instead of closest counterfactual explanations to improve the robustness and consequently the individual fairness of counterfactual explanations.},
  annotation  = {Comment: Rewrite paper to make things more clear; Remove one theorem \& corollary due to buggy proof},
  file        = {:artelt2021evaluating - Evaluating Robustness of Counterfactual Explanations.pdf:PDF},
  keywords    = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence},
}

@Article{guidotti2022counterfactual,
  author       = {Guidotti, Riccardo},
  date         = {2022-04},
  journaltitle = {Data Mining and Knowledge Discovery},
  title        = {Counterfactual explanations and how to find them: literature review and benchmarking},
  doi          = {10.1007/s10618-022-00831-6},
  issn         = {1573-756X},
  language     = {en},
  url          = {https://doi.org/10.1007/s10618-022-00831-6},
  urldate      = {2023-03-24},
  abstract     = {Interpretable machine learning aims at unveiling the reasons behind predictions returned by uninterpretable classifiers. One of the most valuable types of explanation consists of counterfactuals. A counterfactual explanation reveals what should have been different in an instance to observe a diverse outcome. For instance, a bank customer asks for a loan that is rejected. The counterfactual explanation consists of what should have been different for the customer in order to have the loan accepted. Recently, there has been an explosion of proposals for counterfactual explainers. The aim of this work is to survey the most recent explainers returning counterfactual explanations. We categorize explainers based on the approach adopted to return the counterfactuals, and we label them according to characteristics of the method and properties of the counterfactuals returned. In addition, we visually compare the explanations, and we report quantitative benchmarking assessing minimality, actionability, stability, diversity, discriminative power, and running time. The results make evident that the current state of the art does not provide a counterfactual explainer able to guarantee all these properties simultaneously.},
  file         = {Full Text PDF:https\://link.springer.com/content/pdf/10.1007%2Fs10618-022-00831-6.pdf:application/pdf},
  keywords     = {Explainable AI, Counterfactual explanations, Contrastive explanations, Interpretable machine learning},
  shorttitle   = {Counterfactual explanations and how to find them},
}

@TechReport{mahajan2020preserving,
  author      = {Mahajan, Divyat and Tan, Chenhao and Sharma, Amit},
  date        = {2020-06},
  institution = {arXiv},
  title       = {Preserving {Causal} {Constraints} in {Counterfactual} {Explanations} for {Machine} {Learning} {Classifiers}},
  doi         = {10.48550/arXiv.1912.03277},
  note        = {arXiv:1912.03277 [cs, stat] type: article},
  url         = {http://arxiv.org/abs/1912.03277},
  urldate     = {2023-03-24},
  abstract    = {To construct interpretable explanations that are consistent with the original ML model, counterfactual examples---showing how the model's output changes with small perturbations to the input---have been proposed. This paper extends the work in counterfactual explanations by addressing the challenge of feasibility of such examples. For explanations of ML models in critical domains such as healthcare and finance, counterfactual examples are useful for an end-user only to the extent that perturbation of feature inputs is feasible in the real world. We formulate the problem of feasibility as preserving causal relationships among input features and present a method that uses (partial) structural causal models to generate actionable counterfactuals. When feasibility constraints cannot be easily expressed, we consider an alternative mechanism where people can label generated CF examples on feasibility: whether it is feasible to intervene and realize the candidate CF example from the original input. To learn from this labelled feasibility data, we propose a modified variational auto encoder loss for generating CF examples that optimizes for feasibility as people interact with its output. Our experiments on Bayesian networks and the widely used ''Adult-Income'' dataset show that our proposed methods can generate counterfactual explanations that better satisfy feasibility constraints than existing methods.. Code repository can be accessed here: {\textbackslash}textit\{https://github.com/divyat09/cf-feasibility\}},
  annotation  = {Comment: 2019 NeurIPS Workshop on Do the right thing: Machine learning and Causal Inference for improved decision making},
  file        = {:mahajan2020preserving - Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers.pdf:PDF},
  keywords    = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Machine Learning},
}

@TechReport{antoran2023sampling,
  author      = {Antorán, Javier and Padhy, Shreyas and Barbano, Riccardo and Nalisnick, Eric and Janz, David and Hernández-Lobato, José Miguel},
  date        = {2023-03},
  institution = {arXiv},
  title       = {Sampling-based inference for large linear models, with application to linearised {Laplace}},
  note        = {arXiv:2210.04994 [cs, stat] type: article},
  url         = {http://arxiv.org/abs/2210.04994},
  urldate     = {2023-03-25},
  abstract    = {Large-scale linear models are ubiquitous throughout machine learning, with contemporary application as surrogate models for neural network uncertainty quantification; that is, the linearised Laplace method. Alas, the computational cost associated with Bayesian linear models constrains this method's application to small networks, small output spaces and small datasets. We address this limitation by introducing a scalable sample-based Bayesian inference method for conjugate Gaussian multi-output linear models, together with a matching method for hyperparameter (regularisation) selection. Furthermore, we use a classic feature normalisation method (the g-prior) to resolve a previously highlighted pathology of the linearised Laplace method. Together, these contributions allow us to perform linearised neural network inference with ResNet-18 on CIFAR100 (11M parameters, 100 outputs x 50k datapoints), with ResNet-50 on Imagenet (50M parameters, 1000 outputs x 1.2M datapoints) and with a U-Net on a high-resolution tomographic reconstruction task (2M parameters, 251k output{\textasciitilde}dimensions).},
  annotation  = {Comment: Published at ICLR 2023. This latest Arxiv version is extended with a demonstration of the proposed methods on the Imagenet dataset},
  file        = {arXiv Fulltext PDF:https\://arxiv.org/pdf/2210.04994.pdf:application/pdf},
  keywords    = {Statistics - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Machine Learning},
}

@Misc{altmeyer2022conformal,
  author   = {Altmeyer, Patrick},
  date     = {2022-10},
  title    = {{Conformal} {Prediction} in {Julia}},
  language = {en},
  url      = {https://www.paltmeyer.com/blog/posts/conformal-prediction/},
  urldate  = {2023-03-27},
  abstract = {A (very) gentle introduction to Conformal Prediction in Julia using my new package ConformalPrediction.jl.},
}

@InProceedings{welling2011bayesian,
  author     = {Welling, M. and Teh, Y.},
  date       = {2011-06},
  title      = {Bayesian {Learning} via {Stochastic} {Gradient} {Langevin} {Dynamics}},
  url        = {https://www.semanticscholar.org/paper/Bayesian-Learning-via-Stochastic-Gradient-Langevin-Welling-Teh/aeed631d6a84100b5e9a021ec1914095c66de415},
  urldate    = {2023-05-15},
  abstract   = {In this paper we propose a new framework for learning from large scale datasets based on iterative learning from small mini-batches. By adding the right amount of noise to a standard stochastic gradient optimization algorithm we show that the iterates will converge to samples from the true posterior distribution as we anneal the stepsize. This seamless transition between optimization and Bayesian posterior sampling provides an inbuilt protection against overfitting. We also propose a practical method for Monte Carlo estimates of posterior statistics which monitors a "sampling threshold" and collects samples after it has been surpassed. We apply the method to three models: a mixture of Gaussians, logistic regression and ICA with natural gradients.},
  annotation = {[TLDR] This paper proposes a new framework for learning from large scale datasets based on iterative learning from small mini-batches by adding the right amount of noise to a standard stochastic gradient optimization algorithm and shows that the iterates will converge to samples from the true posterior distribution as the authors anneal the stepsize.},
  file       = {:welling_bayesian_2011 - Bayesian Learning Via Stochastic Gradient Langevin Dynamics.html:URL;:welling2011bayesian - Bayesian Learning Via Stochastic Gradient Langevin Dynamics.pdf:PDF},
}

@Article{gill2010circular,
  author       = {Gill, Jeff and Hangartner, Dominik},
  date         = {2010},
  journaltitle = {Political Analysis},
  title        = {Circular {Data} in {Political} {Science} and {How} to {Handle} {It}},
  doi          = {10.1093/pan/mpq009},
  issn         = {1047-1987, 1476-4989},
  language     = {en},
  number       = {3},
  pages        = {316--336},
  url          = {https://www.cambridge.org/core/journals/political-analysis/article/circular-data-in-political-science-and-how-to-handle-it/6DF2D9DA60C455E6A48FFB0FF011F747},
  urldate      = {2023-05-15},
  volume       = {18},
  abstract     = {There has been no attention to circular (purely cyclical) data in political science research. We show that such data exist and are mishandled by models that do not take into account the inherently recycling nature of some phenomenon. Clock and calendar effects are the obvious cases, but directional data are observed as well. We describe a standard maximum likelihood regression modeling framework based on the von Mises distribution, then develop a general Bayesian regression procedure for the first time, providing an easy-to-use Metropolis-Hastings sampler for this approach. Applications include a chronographic analysis of U.S. domestic terrorism and directional party preferences in a two-dimensional ideological space for German Bundestag elections. The results demonstrate the importance of circular models to handle periodic and directional data in political science.},
  file         = {Full Text PDF:https\://www.cambridge.org/core/services/aop-cambridge-core/content/view/6DF2D9DA60C455E6A48FFB0FF011F747/S1047198700012493a.pdf/div-class-title-circular-data-in-political-science-and-how-to-handle-it-div.pdf:application/pdf},
  publisher    = {Cambridge University Press},
}

@InProceedings{liu2023goggle,
  author     = {Liu, Tennison and Qian, Zhaozhi and Berrevoets, Jeroen and Schaar, Mihaela van der},
  date       = {2023-02},
  title      = {{GOGGLE}: {Generative} {Modelling} for {Tabular} {Data} by {Learning} {Relational} {Structure}},
  language   = {en},
  url        = {https://openreview.net/forum?id=fPVRcJqspu},
  urldate    = {2023-05-15},
  abstract   = {Deep generative models learn highly complex and non-linear representations to generate realistic synthetic data. While they have achieved notable success in computer vision and natural language processing, similar advances have been less demonstrable in the tabular domain. This is partially because generative modelling of tabular data entails a particular set of challenges, including heterogeneous relationships, limited number of samples, and difficulties in incorporating prior knowledge. Additionally, unlike their counterparts in image and sequence domain, deep generative models for tabular data almost exclusively employ fully-connected layers, which encode weak inductive biases about relationships between inputs. Real-world data generating processes can often be represented using relational structures, which encode sparse, heterogeneous relationships between variables. In this work, we learn and exploit relational structure underlying tabular data to better model variable dependence, and as a natural means to introduce regularization on relationships and include prior knowledge. Specifically, we introduce GOGGLE, an end-to-end message passing scheme that jointly learns the relational structure and corresponding functional relationships as the basis of generating synthetic samples. Using real-world datasets, we provide empirical evidence that the proposed method is effective in generating realistic synthetic data and exploiting domain knowledge for downstream tasks.},
  file       = {Full Text PDF:https\://openreview.net/pdf?id=fPVRcJqspu:application/pdf},
  shorttitle = {{GOGGLE}},
}

@TechReport{du2020implicit,
  author      = {Du, Yilun and Mordatch, Igor},
  date        = {2020-06},
  institution = {arXiv},
  title       = {Implicit {Generation} and {Generalization} in {Energy}-{Based} {Models}},
  doi         = {10.48550/arXiv.1903.08689},
  note        = {arXiv:1903.08689 [cs, stat] type: article},
  url         = {http://arxiv.org/abs/1903.08689},
  urldate     = {2023-05-16},
  abstract    = {Energy based models (EBMs) are appealing due to their generality and simplicity in likelihood modeling, but have been traditionally difficult to train. We present techniques to scale MCMC based EBM training on continuous neural networks, and we show its success on the high-dimensional data domains of ImageNet32x32, ImageNet128x128, CIFAR-10, and robotic hand trajectories, achieving better samples than other likelihood models and nearing the performance of contemporary GAN approaches, while covering all modes of the data. We highlight some unique capabilities of implicit generation such as compositionality and corrupt image reconstruction and inpainting. Finally, we show that EBMs are useful models across a wide variety of tasks, achieving state-of-the-art out-of-distribution classification, adversarially robust classification, state-of-the-art continual online class learning, and coherent long term predicted trajectory rollouts.},
  file        = {arXiv Fulltext PDF:https\://arxiv.org/pdf/1903.08689.pdf:application/pdf},
  keywords    = {Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning},
}

@Comment{jabref-meta: databaseType:biblatex;}