This is an (incomplete) list of papers that you ight consider. Some are not particularly numerical, but many are. You are not expected to be able to immediately understand all of these, and you are not restricted to this list!


Bayesian computation survey [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

On randomized trace estimates (Cortinovis and Kressner) [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Bayesian Probabilistic Numerical Methods [[paper]{.smallcaps}]{.tag tag-name=”paper”}

Multivariate Rational Approximation [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Exact GPs on a Million Data Points [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Three-precision GMRES [[paper]{.smallcaps}]{.tag tag-name=”paper”}

Sparse data-driven quadrature rules [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Lower-precision arith in SPD systems and LS problems [[paper]{.smallcaps}]{.tag tag-name=”paper”}

Control and RL

Deep Bayesian quadrature policy opt [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Trust region policy opt [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Robust regression for safe exploration in control [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Logistic Q-learning [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Provable multi-obj RL with gen models [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Nearly Minimax Optimal Reinforcement Learning for Linear Mixture MDPs


ML and Computational Mathematics [[paper]{.smallcaps}]{.tag tag-name=”paper”}

Generalized energy based models [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Centering data improves DMD [[paper]{.smallcaps}]{.tag tag-name=”paper”}

Physics-guided AI to accelerate sci discovery

ML: Mathematical Theory and Scientific Applications [[paper]{.smallcaps}]{.tag tag-name=”paper”}

Physics meets ML

ML in science

Masked graph modeling for molecule generation [[paper]{.smallcaps}]{.tag tag-name=”paper”}

Data-driven stabilization of periodic orbits [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

ML for phys sci

Physics-preserving ROM via constrained opt [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

IPAM workshop on ML for Physics

The frontier of simulation-based inference [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

On Empirical System Gramians [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Learning to Simulate Complex Physics with Graph Networks [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

PySINDy paper [[paper]{.smallcaps}]{.tag tag-name=”paper”}

Survey of Deep Learning for Scientific Discovery [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Hybrid FEM-NN models

Deep Ritz Method for High-D

Global opt, BO, sampling, etc

Matern GPs on manifolds [[paper]{.smallcaps}]{.tag tag-name=”paper”}

Student t processes as alternatives to GPs [[paper]{.smallcaps}]{.tag tag-name=”paper”}

Finding Global Minima via Kernel Approximations [[paper]{.smallcaps}]{.tag tag-name=”paper”}

MOTS: Minimax Optimal Thompson Sampling [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Tutorial on Thompson sampling [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Parameterizing Branch-and-Bound Search trees [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Intro to Multi-Armed Bandits [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Improved Regret for Zeroth-Order Adversarial Bandit Cvx Opt [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Smooth contextual bandits [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

KeOps symbolic matrices [[paper]{.smallcaps}]{.tag tag-name=”paper”}

Large nbhd search for solving ILPs [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Learning search space partition for black-box opt using MCTS [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Upgrading from Gaussian Processes to Student's-T Processes [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}


Natural gradients in practice [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Player sampling for faster Nash [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

One sample stochastic Frank-Wolfe [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Momentum-based variance reduction in non-convex SGD [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Superfast second-order methods for unconstrained convex opt [[paper]{.smallcaps}]{.tag tag-name=”paper”}

Solving cvs programming with cvg O(k^-2^)) [[paper]{.smallcaps}]{.tag tag-name=”paper”}

Perspective on NA and Opt [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

From Proximal Point Method to Nesterov's Acceleration [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Self-Tuning Stochastic Optimization [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Resid-based distributionally robust optimization [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Approx theory

Approx by linear combo of translates [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Functional Tucker approx (Chebfun3F) [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Bivariate Crouzeix [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

On the power of adaptation [[paper]{.smallcaps}]{.tag tag-name=”paper”}

Multivariate rational approx [[paper]{.smallcaps}]{.tag tag-name=”paper”}

rbTensor paper (Ballani and Kressner)


Scaling choice models of relational social data [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Mixtures of network models that vary in time [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Causal network inference by optimal causation entropy [[paper]{.smallcaps}]{.tag tag-name=”paper”}

Node Embeddings and Exact Low-Rank Reps of Complex Networks [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

An experimental study of structural diversity in social networks [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Nonlinearity + Networks: A 2020 Vision [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

From networks to optimal higher-order models of complex systems

Networks beyond pairwise interactions: structure and dynamics

The why, how, and when of representations for complex systems

Higher-order network analysis takes off

Disentangling homophily, community structure and triadic closure

Modularity maximization for graphons

Matrix and tensor data methods

Matrix methods for data sci [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Probabilistic contrastive PCA [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}


Path-Based Spectral Clustering [[paper]{.smallcaps}]{.tag tag-name=”paper”}

Clustering - What Theoreticians and Practitioners are Doing Wrong [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Understanding regularized spec clustering via graph conductance [[paper]{.smallcaps}]{.tag tag-name=”paper”}

Misc stats and ML

Towards falsifiable interpretability research [[paper]{.smallcaps}]{.tag tag-name=”paper”}

Feature removal a unifying principle for model explanation [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Automatic finite-sample robustness [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Benefits of overparam [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Balancing covariates using the Gram-Schmidt Walk [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Fifty Years of Test Unfairness [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Reconciling modern ML practice and bias-variance trade-off [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Statistical Paradises and Paradoxes in Big Data [[paper]{.smallcaps}]{.tag tag-name=”paper”}

Communicating and Teaching Intuition for Influence Functions [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Data Analysis Recipes: MCMC [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Beyond subjective and objective in statistics [[paper]{.smallcaps}]{.tag tag-name=”paper”}

Regularization in Statistics [[paper]{.smallcaps}]{.tag tag-name=”paper”}

On the Statistical Formalism of UQ [[paper]{.smallcaps}]{.tag tag-name=”paper”}

Data analysis recipes: Probability Calculus for Inference [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Data analysis recipes: Fitting a model to data [[arxiv]{.smallcaps}]{.tag tag-name=”arxiv”}

Statistical modeling: the three cultures [[paper]{.smallcaps}]{.tag tag-name=”paper”}

Review of conterfactuals for ML [[paper]{.smallcaps}]{.tag tag-name=”paper”}

ML retrospectives

Survey on distribution testing [[paper]{.smallcaps}]{.tag tag-name=”paper”}

Polynomial methods in statistical inference [[paper]{.smallcaps}]{.tag tag-name=”paper”}

KL-based kernel for SVM classification [[paper]{.smallcaps}]{.tag tag-name=”paper”}

Gauss-Legendre Features for GP Regression

Geometry and Dynamics for MCMC

Stochastic trace est with Cheb product identity

Rao-Blackwellization in the MCMC era

Online multi-valid learning

The Tradeoffs of Large Scale Learning