260 citations · 585 across the 32 of their papers we have counts for
11 papers · 1 filter
Your Transformer Is Secretly Elastic
Walter Simoncini, Dheeraj Varghese, Gertjan J. Burghouts +1
Scaling up transformers has produced models with broad capabilities, deployed across heterogeneous hardware and fluctuating workloads. Yet, model families offer only a few fixed si…
Recurrent Equivariant Constraint Modulation: Learning Per-Layer Symmetry Relaxation from Data
Stefanos Pertigkiozoglou, Mircea Petrache, Shubhendu Trivedi +1
Equivariant neural networks exploit underlying task symmetries to improve generalization, but strict equivariance constraints can induce more complex optimization dynamics that can…
On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning
Shubhendu Trivedi, Brian D. Nord
Quantifying uncertainties for machine learning (ML) models is a foundational challenge in modern data analysis. This challenge is compounded by at least two key aspects of the fiel…
On Universality Classes of Equivariant Networks
Marco Pacini, Gabriele Santin, Bruno Lepri +1
Equivariant neural networks provide a principled framework for incorporating symmetry into learning architectures and have been extensively analyzed through the lens of their separ…
Improving Equivariant Model Training via Constraint Relaxation
Stefanos Pertigkiozoglou, Evangelos Chatzipantazis, Shubhendu Trivedi +1
Equivariant neural networks have been widely used in a variety of applications due to their ability to generalize well in tasks where the underlying data symmetries are known. Desp…
Approximation-Generalization Trade-offs under (Approximate) Group Equivariance
Mircea Petrache, Shubhendu Trivedi
The explicit incorporation of task-specific inductive biases through symmetry has emerged as a general design precept in the development of high-performance machine learning models…
Fast Online Value-Maximizing Prediction Sets with Conformal Cost Control
Zhen Lin, Shubhendu Trivedi, Cao Xiao +1
Many real-world multi-label prediction problems involve set-valued predictions that must satisfy specific requirements dictated by downstream usage. We focus on a typical scenario…
Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views?
Matthew Farrell, Blake Bordelon, Shubhendu Trivedi +1
Equivariance has emerged as a desirable property of representations of objects subject to identity-preserving transformations that constitute a group, such as translations and rota…
Locally Valid and Discriminative Prediction Intervals for Deep Learning Models
Zhen Lin, Shubhendu Trivedi, Jimeng Sun
Crucial for building trust in deep learning models for critical real-world applications is efficient and theoretically sound uncertainty quantification, a task that continues to be…
Covariant Compositional Networks For Learning Graphs
Risi Kondor, Hy Truong Son, Horace Pan +2
Most existing neural networks for learning graphs address permutation invariance by conceiving of the network as a message passing scheme, where each node sums the feature vectors…
The Utility of Clustering in Prediction Tasks
Shubhendu Trivedi, Zachary A. Pardos, Neil T. Heffernan
We explore the utility of clustering in reducing error in various prediction tasks. Previous work has hinted at the improvement in prediction accuracy attributed to clustering algo…