123 citations · 366 across the 30 of their papers we have counts for
11 papers · 1 filter
Disentangling Influence: Using Disentangled Representations to Audit Model Predictions
Charles T. Marx, Richard Lanas Phillips, Sorelle A. Friedler +2
Motivated by the need to audit complex and black box models, there has been extensive research on quantifying how data features influence model predictions. Feature influence can b…
Fairness in representation: quantifying stereotyping as a representational harm
Mohsen Abbasi, Sorelle A. Friedler, Carlos Scheidegger +1
While harms of allocation have been increasingly studied as part of the subfield of algorithmic fairness, harms of representation have received considerably less attention. In this…
A Unified View of Localized Kernel Learning
John Moeller, Sarathkrishna Swaminathan, Suresh Venkatasubramanian
Multiple Kernel Learning, or MKL, extends (kernelized) SVM by attempting to learn not only a classifier/regressor but also the best kernel for the training task, usually from a com…
A Group Theoretic Perspective on Unsupervised Deep Learning
Arnab Paul, Suresh Venkatasubramanian
Why does Deep Learning work? What representations does it capture? How do higher-order representations emerge? We study these questions from the perspective of group theory, thereb…
Why does Deep Learning work? - A perspective from Group Theory
Arnab Paul, Suresh Venkatasubramanian
Why does Deep Learning work? What representations does it capture? How do higher-order representations emerge? We study these questions from the perspective of group theory, thereb…
Power to the Points: Validating Data Memberships in Clusterings
Parasaran Raman, Suresh Venkatasubramanian
A clustering is an implicit assignment of labels of points, based on proximity to other points. It is these labels that are then used for downstream analysis (either focusing on in…
A Geometric Algorithm for Scalable Multiple Kernel Learning
John Moeller, Parasaran Raman, Avishek Saha +1
We present a geometric formulation of the Multiple Kernel Learning (MKL) problem. To do so, we reinterpret the problem of learning kernel weights as searching for a kernel that max…
Efficient Protocols for Distributed Classification and Optimization
Hal Daume, Jeff M. Phillips, Avishek Saha +1
In distributed learning, the goal is to perform a learning task over data distributed across multiple nodes with minimal (expensive) communication. Prior work (Daume III et al., 20…
Generating a Diverse Set of High-Quality Clusterings
Jeff M. Phillips, Parasaran Raman, Suresh Venkatasubramanian
We provide a new framework for generating multiple good quality partitions (clusterings) of a single data set. Our approach decomposes this problem into two components, generating…
Spatially-Aware Comparison and Consensus for Clusterings
Parasaran Raman, Jeff M. Phillips, Suresh Venkatasubramanian
This paper proposes a new distance metric between clusterings that incorporates information about the spatial distribution of points and clusters. Our approach builds on the idea o…
A Unified Algorithmic Framework for Multi-Dimensional Scaling
Arvind Agarwal, Jeff M. Phillips, Suresh Venkatasubramanian
In this paper, we propose a unified algorithmic framework for solving many known variants of \mds. Our algorithm is a simple iterative scheme with guaranteed convergence, and is \e…