# Garbled highlights from NeurIPS 2020

September 17, 2020 — December 11, 2020

## 1 workshops

- Machine Learning for Creativity and Design
- Workshop on Deep Learning and Inverse Problems
- Differentiable vision, graphics, and physics applied to machine learning
- Learning Meaningful Representations of Life
- Tackling Climate Change with Machine Learning
- AI for Earth Sciences
- Causal Discovery & Causality-Inspired Machine Learning
- Interpretable Inductive Biases and Physically Structured Learning

## 2 interesting papers by *ad hoc* theme

### 2.1 causality

- Causal Imitation Learning With Unobserved Confounders
- Causal Learning
- Domain Adaptation as a Problem of Inference on Graphical Models
- Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding
- Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs
- Differentiable Causal Discovery from Interventional Data

### 2.2 learning in continuous time/depth

- Almost surely stable deep dynamics
- Learning Differential Equations that are Easy to Solve
- Dissecting Neural ODEs
- STEER : Simple Temporal Regularization For Neural ODE
- Training Generative Adversarial Networks by Solving Ordinary Differential Equations
- Ode to an ODE
- Time-Reversal Symmetric ODE Network
- Hypersolvers: Toward Fast Continuous-Depth Models
- On Second Order Behaviour in Augmented Neural ODEs
- Neural Controlled Differential Equations for Irregular Time Series

## 3 Learning with weird losses

## 4 ML physical sciences

## 5 References

Bai, Koltun, and Kolter. 2020. “Multiscale Deep Equilibrium Models.” In

*Advances in Neural Information Processing Systems*.
Bolte, and Pauwels. 2020. “A Mathematical Model for Automatic Differentiation in Machine Learning.” In

*Advances in Neural Information Processing Systems*.
Chi, Jiang, and Mu. 2020. “Fast Fourier Convolution.” In

*Advances in Neural Information Processing Systems*.
Choromanski, Davis, Likhosherstov, et al. 2020. “An Ode to an ODE.” In

*Advances in Neural Information Processing Systems*.
Course, Evans, and Nair. 2020. “Weak Form Generalized Hamiltonian Learning.” In

*Advances in Neural Information Processing Systems*.
Dorado-Rojas, Vinzamuri, and Vanfretti. 2020. “Orthogonal Laguerre Recurrent Neural Networks.” In.

Fan, and Wang. 2020. “Spectra of the Conjugate Kernel and Neural Tangent Kernel for Linear-Width Neural Networks.” In

*Advances in Neural Information Processing Systems*.
Finzi, Wang, and Wilson. 2020. “Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints.” In

*Advances in Neural Information Processing Systems*.
Fort, Dziugaite, Paul, et al. 2020. “Deep Learning Versus Kernel Learning: An Empirical Study of Loss Landscape Geometry and the Time Evolution of the Neural Tangent Kernel.” In

*Advances in Neural Information Processing Systems*.
Gardner. 1988.

*Statistical Spectral Analysis: A Non-Probabilistic Theory*.
Geifman, Yadav, Kasten, et al. 2020. “On the Similarity Between the Laplace and Neural Tangent Kernels.” In

*arXiv:2007.01580 [Cs, Stat]*.
Ghosh, Behl, Dupont, et al. 2020. “STEER : Simple Temporal Regularization For Neural ODE.” In

*Advances in Neural Information Processing Systems*.
He, Lakshminarayanan, and Teh. 2020. “Bayesian Deep Ensembles via the Neural Tangent Kernel.” In

*Advances in Neural Information Processing Systems*.
Hortúa, Volpi, Marinelli, et al. 2020. “Accelerating MCMC Algorithms Through Bayesian Deep Networks.” In.

Huh, Yang, Hwang, et al. 2020. “Time-Reversal Symmetric ODE Network.” In

*Advances in Neural Information Processing Systems*.
Karimi, Barthe, Schölkopf, et al. 2021. “A Survey of Algorithmic Recourse: Definitions, Formulations, Solutions, and Prospects.”

Kaul. 2020. “Linear Dynamical Systems as a Core Computational Primitive.” In

*Advances in Neural Information Processing Systems*.
Kelly, Bettencourt, Johnson, et al. 2020. “Learning Differential Equations That Are Easy to Solve.” In.

Kidger, Chen, and Lyons. 2021. “‘Hey, That’s Not an ODE’: Faster ODE Adjoints via Seminorms.” In

*Proceedings of the 38th International Conference on Machine Learning*.
Kochkov, Sanchez-Gonzalez, Smith, et al. 2020. “Learning Latent FIeld Dynamics of PDEs.” In

*Machine Learning and the Physical Sciences Workshop at the 33rd Conference on Neural Information Processing Systems (NeurIPS)*.
Kothari, de Hoop, and Dokmanić. 2020. “Learning the Geometry of Wave-Based Imaging.” In

*Advances in Neural Information Processing Systems*.
Krämer, Köhler, and Noé. n.d. “Preserving Properties of Neural Networks by Perturbative Updates.” In.

Krishnamurthy, Can, and Schwab. 2022. “Theory of Gating in Recurrent Neural Networks.”

*Physical Review. X*.
Lawrence, Loewen, Forbes, et al. 2020. “Almost Surely Stable Deep Dynamics.” In

*Advances in Neural Information Processing Systems*.
Liu, and Scarlett. 2020. “The Generalized Lasso with Nonlinear Observations and Generative Priors.” In

*Advances in Neural Information Processing Systems*.
Lou, Lim, Katsman, et al. 2020. “Neural Manifold Ordinary Differential Equations.” In

*Advances in Neural Information Processing Systems*.
Lu, You, and Huang. 2020. “Woodbury Transformations for Deep Generative Flows.” In

*Advances in Neural Information Processing Systems*.
Lu, Yulong, and Lu. 2020. “A Universal Approximation Theorem of Deep Neural Networks for Expressing Probability Distributions.” In

*Advances in Neural Information Processing Systems*.
Massaroli, Poli, Park, et al. 2020. “Dissecting Neural ODEs.” In

*arXiv:2002.08071 [Cs, Stat]*.
Meronen, Irwanto, and Solin. 2020. “Stationary Activations for Uncertainty Calibration in Deep Learning.” In

*Advances in Neural Information Processing Systems*.
Mhammedi, Foster, Simchowitz, et al. 2020. “Learning the Linear Quadratic Regulator from Nonlinear Observations.” In

*Advances in Neural Information Processing Systems*.
Miller, Cole, and Louppe. n.d. “Simulation-Efﬁcient Marginal Posterior Estimation with Swyft: Stop Wasting Your Precious Time.” In.

Morrill, Kidger, Salvi, et al. 2020. “Neural CDEs for Long Time Series via the Log-ODE Method.” In.

Norcliffe, Bodnar, Day, et al. 2020. “Neural ODE Processes.” In.

Pfau, and Rezende. 2020. “Integrable Nonparametric Flows.” In.

Poli, Massaroli, Yamashita, et al. 2020. “Hypersolvers: Toward Fast Continuous-Depth Models.” In

*Advances in Neural Information Processing Systems*.
Priestley. 2004.

*Spectral analysis and time series*. Probability and mathematical statistics.
Qin, Wu, Springenberg, et al. 2020. “Training Generative Adversarial Networks by Solving Ordinary Differential Equations.” In

*Advances in Neural Information Processing Systems*.
Rashidinejad, Jiao, and Russell. 2020. “SLIP: Learning to Predict in Unknown Dynamical Systems with Long-Term Memory.” In

*Advances in Neural Information Processing Systems*.
Rojas-Gómez, Yang, Lin, et al. 2020. “Physics-Consistent Data-Driven Seismic Inversion with Adaptive Data Augmentation.” In.

Saha, and Balamurugan. 2020. “Learning with Operator-Valued Kernels in Reproducing Kernel Krein Spaces.” In

*Advances in Neural Information Processing Systems*.
Salim, Korba, and Luise. 2020. “The Wasserstein Proximal Gradient Algorithm.” In

*Advances in Neural Information Processing Systems*.
Shen, Wang, Ribeiro, et al. 2020. “Sinkhorn Natural Gradient for Generative Models.” In

*Advances in Neural Information Processing Systems*.
Shukla, and Marlin. n.d. “A Survey on Principles, Models and Methods for Learning from Irregularly Sampled Time Series: From Discretization to Attention and Invariance.” In.

Um, and Holl. 2021. “Differentiable Physics for Improving the Accuracy of Iterative PDE-Solvers with Neural Networks.” In.

Vahdat, and Kautz. 2020. “NVAE: A Deep Hierarchical Variational Autoencoder.” In

*Advances in Neural Information Processing Systems*.
van Gelder, Wortsman, and Ehsani. 2020. “Deconstructing the Structure of Sparse Neural Networks.” In.

Verma, Dickerson, and Hines. 2020. “Counterfactual Explanations for Machine Learning: A Review.” In.

Walder, and Nock. 2020. “All Your Loss Are Belong to Bayes.” In

*Advances in Neural Information Processing Systems*.
Wang, Bentivegna, Zhou, et al. 2020. “Physics-Informed Neural Network Super Resolution for Advection-Diffusion Models.” In.

Xu, and Darve. 2020. “ADCME: Learning Spatially-Varying Physical Fields Using Deep Neural Networks.” In

*arXiv:2011.11955 [Cs, Math]*.
Zhang, Kun, Gong, Stojanov, et al. 2020. “Domain Adaptation as a Problem of Inference on Graphical Models.” In

*Advances in Neural Information Processing Systems*.
Zhang, Rui, Walder, Bonilla, et al. 2020. “Quantile Propagation for Wasserstein-Approximate Gaussian Processes.” In

*Proceedings of NeurIPS 2020*.