Fall 2026

Course bibliography

CS 6786 · Probabilistic Machine Learning for Scientific Discovery

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Readings

Compiled automatically from required and additional readings in the course schedule.

  1. Amsel, Persson, Musco, and Gower. “The Polar Express: Optimal Matrix Sign Methods and their Application to the Muon Algorithm.” International Conference on Learning Representations (2026).Required reading · Week 9
  2. Azizzadenesheli et al. “Neural operators for accelerating scientific simulations and design.” Nature Reviews Physics (2024).Additional reading · Week 10
  3. Bernstein and Newhouse. “Old Optimizer, New Norm: An Anthology.” arXiv preprint arXiv:2409.20325 (2024).Additional reading · Week 9
  4. Betancourt. “A Conceptual Introduction to Hamiltonian Monte Carlo.” arXiv preprint arXiv:1701.02434 (2017).Course topic · Week 1
  5. Bishop and Bishop. Deep Learning: Foundations and Concepts. Springer, 2024.Course topic · Week 2
  6. Blei, Kucukelbir, and McAuliffe. “Variational Inference: A Review for Statisticians.” Journal of the American Statistical Association 112(518): 859–877 (2017).Course topic · Week 2
  7. Bottou, Curtis, and Nocedal. “Optimization Methods for Large-Scale Machine Learning.” SIAM Review 60(2): 223–311 (2018).Required reading · Week 8
  8. Cabezas and Nemeth. “Transport Elliptical Slice Sampling.” Proceedings of the 26th International Conference on Artificial Intelligence and Statistics 206: 3664–3676 (2023).Required and additional reading · Weeks 1, 10
  9. Cai, Modi, Margossian, Gower, Blei, and Saul. “EigenVI: score-based variational inference with orthogonal function expansions.” Advances in Neural Information Processing Systems 37: 132691–132721 (2024).Additional reading · Week 3
  10. Cai, Modi, Pillaud-Vivien, Margossian, Gower, Blei, and Saul. “Batch and match: black-box variational inference with a score-based divergence.” Proceedings of the 41st International Conference on Machine Learning 235: 5258–5297 (2024).Required reading · Week 3
  11. Cao et al. “Derivative-Informed Neural Operator Acceleration of Geometric MCMC for Infinite-Dimensional Bayesian Inverse Problems.” Journal of Machine Learning Research 26 (2025).Additional reading · Week 10
  12. Coscia, de Haan, and Welling. “BLIPs: Bayesian Learned Interatomic Potentials.” Proceedings of the 43rd International Conference on Machine Learning (2026).Required reading · Week 6
  13. Cranmer, Brehmer, and Louppe. “The frontier of simulation-based inference.” Proceedings of the National Academy of Sciences 117(48): 30055–30062 (2020).Required reading · Week 2
  14. Daxberger, Kristiadi, Immer, Eschenhagen, Bauer, and Hennig. “Laplace Redux—Effortless Bayesian Deep Learning.” Advances in Neural Information Processing Systems 34 (2021).Required reading · Week 5
  15. Deistler et al. “Simulation-Based Inference: A Practical Guide.” arXiv preprint arXiv:2508.12939 (2025).Additional reading · Week 2
  16. Dhaka, Catalina, Welandawe, Andersen, Huggins, and Vehtari. “Challenges and Opportunities in High Dimensional Variational Inference.” Advances in Neural Information Processing Systems 34 (2021).Additional reading · Week 2
  17. Doumont et al. “We Still Don't Understand High-Dimensional Bayesian Optimization.” (2026).Additional reading · Week 7
  18. Eijkelboom, Bartosh, Naesseth, Welling, and van de Meent. “Variational Flow Matching for Graph Generation.” Advances in Neural Information Processing Systems 37 (2024).Required reading · Week 4
  19. Eriksson and Jankowiak. “High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspaces.” Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence 161 (2021).Additional reading · Week 7
  20. Fong, Holmes, and Walker. “Martingale posterior distributions.” Journal of the Royal Statistical Society Series B: Statistical Methodology 85(5): 1357–1391 (2023).Required reading · Week 5
  21. Frazier. “A Tutorial on Bayesian Optimization.” Tutorials in Operations Research (2018).Additional reading · Week 6
  22. Gabrié, Rotskoff, and Vanden-Eijnden. “Adaptive Monte Carlo augmented with normalizing flows.” Proceedings of the National Academy of Sciences (2022).Required reading · Week 10
  23. Garnett. Bayesian Optimization. Cambridge University Press, 2023.Additional reading · Week 6
  24. Garrigos and Gower. “Handbook of Convergence Theorems for (Stochastic) Gradient Methods.” arXiv preprint arXiv:2301.11235 (2023).Additional reading · Week 8
  25. Gelman, Carlin, Stern, Dunson, Vehtari, and Rubin. Bayesian Data Analysis. 3rd ed. Chapman & Hall/CRC Press, 2013.Additional reading · Week 1
  26. Ghahramani. “Probabilistic machine learning and artificial intelligence.” Nature 521(7553): 452–459 (2015).Additional reading · Week 1
  27. Grasselli, Chong, Kapil, Bonfanti, and Rossi. “Uncertainty in the era of machine learning for atomistic modeling.” Digital Discovery 4: 2654–2675 (2025).Additional reading · Week 6
  28. Griffiths and Hernández-Lobato. “Constrained Bayesian optimization for automatic chemical design using variational autoencoders.” Chemical Science 11 (2020).Additional reading · Week 7
  29. Gupta et al. “Shampoo: Preconditioned Stochastic Tensor Optimization.” Proceedings of the 35th International Conference on Machine Learning 80 (2018).Additional reading · Week 9
  30. Gómez-Bombarelli et al. “Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules.” ACS Central Science 4(2): 268–276 (2018).Additional reading · Week 7
  31. Harrison, Willes, and Snoek. “Variational Bayesian Last Layers.” International Conference on Learning Representations (2024).Required reading · Week 4
  32. Havens et al. “Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching.” Proceedings of the 42nd International Conference on Machine Learning 267: 22204–22237 (2025).Required reading · Week 13
  33. Hennig, Osborne, and Girolami. “Probabilistic numerics and uncertainty in computations.” Proceedings of the Royal Society A 471(2179): 20150142 (2015).Additional reading · Week 1
  34. Hoffman and Gelman. “The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo.” Journal of Machine Learning Research 15(47): 1593–1623 (2014).Additional reading · Week 1
  35. Hollmann, Müller, Purucker, Krishnakumar, Körfer, Hoo, Schirrmeister, and Hutter. “Accurate predictions on small data with a tabular foundation model.” Nature 637: 319–326 (2025).Additional reading · Week 5
  36. Hvarfner et al. “Vanilla Bayesian Optimization Performs Great in High Dimensions.” Proceedings of the 41st International Conference on Machine Learning 235 (2024).Required reading · Week 7
  37. Kingma and Ba. “Adam: A Method for Stochastic Optimization.” International Conference on Learning Representations (2015).Additional reading · Week 9
  38. Kingma and Welling. “An Introduction to Variational Autoencoders.” Foundations and Trends in Machine Learning 12(4): 307–392 (2019).Required reading · Week 2
  39. Kingma, Salimans, Jozefowicz, Chen, Sutskever, and Welling. “Improved Variational Inference with Inverse Autoregressive Flow.” Advances in Neural Information Processing Systems 29 (2016).Required reading · Week 3
  40. Kucukelbir, Tran, Ranganath, Gelman, and Blei. “Automatic Differentiation Variational Inference.” Journal of Machine Learning Research 18(14): 1–45 (2017).Required reading and course topic · Week 2
  41. Lakshminarayanan, Pritzel, and Blundell. “Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles.” Advances in Neural Information Processing Systems 30 (2017).Additional reading · Week 5
  42. Li et al. “Physics-Informed Neural Operator for Learning Partial Differential Equations.” ACM / IMS Journal of Data Science (2024).Required reading · Week 10
  43. Lin et al. “Understanding and improving Shampoo and SOAP via Kullback-Leibler Minimization.” International Conference on Learning Representations (2026).Required reading · Week 9
  44. Lipman, Chen, Ben-Hamu, Nickel, and Le. “Flow Matching for Generative Modeling.” International Conference on Learning Representations (2023).Additional reading · Week 4
  45. Liu and Wang. “Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm.” Advances in Neural Information Processing Systems 29 (2016).Required reading · Week 3
  46. Liu, Choi, Y. Chen, Miller, and R. T. Q. Chen. “Adjoint Schrödinger Bridge Sampler.” Advances in Neural Information Processing Systems 38 (2025).Required reading · Week 13
  47. Liu, Zhu, Ton, Wynne, and Duncan. “Grassmann Stein Variational Gradient Descent.” Proceedings of the 25th International Conference on Artificial Intelligence and Statistics 151: 2002–2021 (2022).Additional reading · Week 3
  48. Louizos and Welling. “Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors.” Proceedings of the 33rd International Conference on Machine Learning 48: 1708–1716 (2016).Required reading · Week 4
  49. MacKay. Information Theory, Inference, and Learning Algorithms. Cambridge University Press, 2003.Additional reading · Week 1
  50. Martens and Grosse. “Optimizing Neural Networks with Kronecker-factored Approximate Curvature.” Proceedings of the 32nd International Conference on Machine Learning 37: 2408–2417 (2015).Additional reading · Week 9
  51. Maus et al. “Local Latent Space Bayesian Optimization over Structured Inputs.” Advances in Neural Information Processing Systems 35 (2022).Required reading · Week 7
  52. Modi, Cai, and Saul. “Batch, match, and patch: low-rank approximations for score-based variational inference.” Proceedings of the 28th International Conference on Artificial Intelligence and Statistics 258: 4510–4518 (2025).Additional reading · Week 4
  53. Mohamed, Rosca, Figurnov, and Mnih. “Monte Carlo Gradient Estimation in Machine Learning.” Journal of Machine Learning Research 21(132): 1–62 (2020).Required reading and course topic · Week 2
  54. Moss, Ober, and Diethe. “Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spaces.” Proceedings of the 42nd International Conference on Machine Learning 267: 44956–44970 (2025).Required reading · Week 7
  55. Mucsányi, Kirchhof, and Oh. “Benchmarking Uncertainty Disentanglement: Specialized Uncertainties for Specialized Tasks.” Advances in Neural Information Processing Systems 37 (2024).Additional reading · Week 5
  56. Murphy. Probabilistic Machine Learning: Advanced Topics. MIT Press, 2023.Additional reading · Week 1
  57. Murray, Adams, and MacKay. “Elliptical Slice Sampling.” Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics 9: 541–548 (2010).Additional reading · Week 1
  58. Müller, Hollmann, Pineda Arango, Grabocka, and Hutter. “Transformers Can Do Bayesian Inference.” International Conference on Learning Representations (2022).Required reading · Week 5
  59. Neal. “Slice Sampling.” The Annals of Statistics 31(3): 705–767 (2003).Course topic · Week 1
  60. Ng, Fong, Frazier, Knoblauch, and Wei. “TabMGP: Martingale Posterior with TabPFN.” Proceedings of the 43rd International Conference on Machine Learning (2026).Additional reading · Week 5
  61. Orvieto and Gower. “In Search of Adam's Secret Sauce.” Advances in Neural Information Processing Systems (2025).Required reading · Week 9
  62. Papamakarios and Murray. “Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation.” Advances in Neural Information Processing Systems 29 (2016).Additional reading · Week 2
  63. Papamakarios, Nalisnick, Rezende, Mohamed, and Lakshminarayanan. “Normalizing Flows for Probabilistic Modeling and Inference.” Journal of Machine Learning Research 22(57): 1–64 (2021).Additional reading · Week 3
  64. Papenmeier et al. “Understanding High-Dimensional Bayesian Optimization.” Proceedings of the 42nd International Conference on Machine Learning 267 (2025).Required reading · Week 7
  65. Rahaman and Thiery. “Uncertainty Quantification and Deep Ensembles.” Advances in Neural Information Processing Systems 34 (2021).Required reading · Week 5
  66. Rainforth, Kosiorek, Le, Maddison, Igl, Wood, and Teh. “Tighter Variational Bounds are Not Necessarily Better.” Proceedings of the 35th International Conference on Machine Learning 80: 4277–4285 (2018).Additional reading · Week 2
  67. Ranganath, Gerrish, and Blei. “Black Box Variational Inference.” Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics 33: 814–822 (2014).Course topic · Week 2
  68. Rathore et al. “Challenges in Training PINNs: A Loss Landscape Perspective.” Proceedings of the 41st International Conference on Machine Learning 235 (2024).Required reading · Week 10
  69. Rezende and Mohamed. “Variational Inference with Normalizing Flows.” Proceedings of the 32nd International Conference on Machine Learning 37: 1530–1538 (2015).Required reading · Week 3
  70. Robnik, Cohn-Gordon, and Seljak. “Practical and Scalable Hamiltonian Monte Carlo Without the Metropolis Test.” (2026).Additional reading · Week 10
  71. Ruder. “An Overview of Gradient Descent Optimization Algorithms.” arXiv preprint arXiv:1609.04747 (2016).Additional reading · Week 8
  72. Snoek, Larochelle, and Adams. “Practical Bayesian Optimization of Machine Learning Algorithms.” Advances in Neural Information Processing Systems 25: 2951–2959 (2012).Required reading · Week 6
  73. Tan, Urata, Goldman, Dietschreit, and Gómez-Bombarelli. “Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles.” npj Computational Materials 9: 225 (2023).Required reading · Week 6
  74. Tomczak, Swaroop, and Turner. “Efficient Low Rank Gaussian Variational Inference for Neural Networks.” Advances in Neural Information Processing Systems 33 (2020).Additional reading · Week 4
  75. Tripp et al. “Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining.” Advances in Neural Information Processing Systems 33 (2020).Additional reading · Week 7
  76. Unke, Chmiela, Sauceda, Gastegger, Poltavsky, Schütt, Tkatchenko, and Müller. “Machine Learning Force Fields.” Chemical Reviews 121(16): 10142–10186 (2021).Additional reading · Week 6
  77. Vargas, Grathwohl, and Doucet. “Denoising Diffusion Samplers.” The Eleventh International Conference on Learning Representations (2023).Required reading · Week 11
  78. Vyas et al. “SOAP: Improving and Stabilizing Shampoo using Adam for Language Modeling.” International Conference on Learning Representations (2025).Required reading · Week 9
  79. Wang et al. “Scientific discovery in the age of artificial intelligence.” Nature 620: 47–60 (2023).Additional reading · Week 1
  80. Wild, Ghalebikesabi, Sejdinovic, and Knoblauch. “A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods.” Advances in Neural Information Processing Systems 36 (2023).Additional reading · Week 5
  81. Wildberger, Dax, Buchholz, Green, Macke, and Schölkopf. “Flow Matching for Scalable Simulation-Based Inference.” Advances in Neural Information Processing Systems 36 (2023).Required reading · Week 4
  82. Wilson and Izmailov. “Bayesian Deep Learning and a Probabilistic Perspective of Generalization.” Advances in Neural Information Processing Systems 33 (2020).Additional reading · Week 5
  83. Xu et al. “Standard Gaussian Process is All You Need for High-Dimensional Bayesian Optimization.” International Conference on Learning Representations (2025).Additional reading · Week 7
  84. Yang et al. “MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures.” arXiv preprint arXiv:2405.04967 (2024).Additional reading · Week 6
  85. Yao, Vehtari, Simpson, and Gelman. “Yes, but Did It Work?: Evaluating Variational Inference.” Proceedings of the 35th International Conference on Machine Learning 80: 5581–5590 (2018).Additional reading · Week 2
  86. Zhang, Sun, Duvenaud, and Grosse. “Noisy Natural Gradient as Variational Inference.” Proceedings of the 35th International Conference on Machine Learning 80: 5852–5861 (2018).Additional reading · Week 9
  87. Akhound-Sadegh et al., 2024Required reading · Week 13
  88. Arbel, Matthews, and Doucet, 2021Additional reading · Week 11
  89. Dance et al., 2025Required reading · Week 11
  90. Del Moral, Doucet, and Jasra, 2006Additional reading · Week 11
  91. Doucet et al., 2022Additional reading · Week 11
  92. Gao and Günnemann, 2024Required reading · Week 12
  93. Geffner and Domke, 2021Additional reading · Week 11
  94. He et al., 2025Required reading · Week 13
  95. Hermann et al., 2023Required reading · Week 12
  96. Hertrich and Gruhlke, 2025Additional reading · Week 13
  97. Lim et al., 2025Additional reading · Week 13
  98. Liu et al., 2025Additional reading · Week 10
  99. Miller et al., 2024Additional reading · Week 14
  100. Neal, 2001Additional reading · Week 11
  101. Noé et al., 2019Additional reading · Week 14
  102. Pfau et al., 2020Additional reading · Week 12
  103. Phillips et al., 2024Required reading · Week 11
  104. Rissanen et al., 2025Additional reading · Week 13
  105. Robnik and Seljak, 2024Additional reading · Week 10
  106. Robnik et al., 2025Additional reading · Week 10
  107. Scherbela, Gerard, and Grohs, 2023Additional reading · Week 12
  108. Tan et al., 2025Required reading · Week 14
  109. Titsias, 2023Additional reading · Week 10
  110. Yao et al., 2025Additional reading · Week 13
  111. Zeni et al., 2025Required reading · Week 14
  112. Zoltowski et al., 2025Required reading · Week 11