When trying to gain better visibility into a machine learning model in order to understand and
mitigate the associated risks, a potentially valuable source of evidence is: which training …

Optimization is a ubiquitous modeling tool and is often deployed in settings which
repeatedly solve similar instances of the same problem. Amortized optimization methods …

Learned optimizers---neural networks that are trained to act as optimizers---have the
potential to dramatically accelerate training of machine learning models. However, even …

This paper focuses on computing the convex conjugate operation that arises when solving
Euclidean Wasserstein-2 optimal transport problems. This conjugation, which is also …

Artificial neural networks have revolutionized machine learning in recent years, but a
complete theoretical framework for their learning process is still lacking. Substantial …

The backtracking line-search is an effective technique to automatically tune the step-size in
smooth optimization. It guarantees similar performance to using the theoretically optimal …

It is often useful to compactly summarize important properties of model parameters and
training data so that they can be used later without storing and/or iterating over the entire …

Second-order optimization algorithms exhibit excellent convergence properties for training
deep learning models, but often incur significant computation and memory overheads. This …

Many training data attribution (TDA) methods aim to estimate how a model's behavior would
change if one or more data points were removed from the training set. Methods based on …

Many training data attribution (TDA) methods aim to estimate how a model's behavior would
change if one or more data points were removed from the training set. Methods based on …