no code implementations • 3 Jan 2024 • Bobak T. Kiani, Thien Le, Hannah Lawrence, Stefanie Jegelka, Melanie Weber
We study the problem of learning equivariant neural networks via gradient descent.
1 code implementation • 24 Nov 2023 • Lukas Fesser, Melanie Weber
We further show that combining local structural encodings, such as LCP, with global positional encodings improves downstream performance, suggesting that they capture complementary geometric information.
1 code implementation • 17 Sep 2023 • Lukas Fesser, Melanie Weber
Several rewiring approaches utilizing graph characteristics, such as curvature or the spectrum of the graph Laplacian, have been proposed.
no code implementations • 19 Jul 2023 • Yu Tian, Zachary Lubberts, Melanie Weber
We consider several discrete curvature notions and analyze the utility of the resulting algorithms.
no code implementations • 5 Jul 2023 • Nicolas Garcia Trillos, Melanie Weber
Let $\mathcal{M} \subseteq \mathbb{R}^d$ denote a low-dimensional manifold and let $\mathcal{X}= \{ x_1, \dots, x_n \}$ be a collection of points uniformly sampled from $\mathcal{M}$.
no code implementations • 16 Dec 2022 • Wenyue Hua, Yuchen Zhang, Zhe Chen, Josie Li, Melanie Weber
We show that our model improves over general-domain and single-domain medical and legal language models when processing mixed-domain (personal injury) text.
no code implementations • 22 Jun 2022 • Melanie Weber, Suvrit Sra
We study geodesically convex (g-convex) problems that can be written as a difference of Euclidean convex functions.
no code implementations • 21 Sep 2021 • Jackson Sargent, Melanie Weber
The need to address representation biases and sentencing disparities in legal case data has long been recognized.
no code implementations • NeurIPS 2020 • Melanie Weber, Manzil Zaheer, Ankit Singh Rawat, Aditya Menon, Sanjiv Kumar
In this paper, we present, to our knowledge, the first theoretical guarantees for learning a classifier in hyperbolic rather than Euclidean space.
no code implementations • 12 Oct 2019 • Melanie Weber
For canonical graphs, the algorithm's prediction provably matches classical results.
no code implementations • 9 Oct 2019 • Melanie Weber, Suvrit Sra
We present algorithms for both purely stochastic optimization and finite-sum problems.
no code implementations • 17 Jan 2019 • Dominik Alfke, Weston Baines, Jan Blechschmidt, Mauricio J. del Razo Sarmina, Amnon Drory, Dennis Elbrächter, Nando Farchmin, Matteo Gambara, Silke Glas, Philipp Grohs, Peter Hinz, Danijel Kivaranovic, Christian Kümmerle, Gitta Kutyniok, Sebastian Lunz, Jan Macdonald, Ryan Malthaner, Gregory Naisat, Ariel Neufeld, Philipp Christian Petersen, Rafael Reisenhofer, Jun-Da Sheng, Laura Thesing, Philipp Trunschke, Johannes von Lindheim, David Weber, Melanie Weber
We present a novel technique based on deep learning and set theory which yields exceptional classification and prediction results.
1 code implementation • 1 Jul 2018 • F. Patricia Medina, Linda Ness, Melanie Weber, Karamatou Yacoubou Djima
When analyzing empirical data, we often find that global linear models overestimate the number of parameters required.
1 code implementation • 30 Oct 2017 • Melanie Weber, Suvrit Sra
Both tasks involve geodesically convex interval constraints, for which we show that the Riemannian "linear" oracle required by RFW admits a closed-form solution; this result may be of independent interest.