57 citations · 70 across the 7 of their papers we have counts for
10 papers
Certification of Distributional Individual Fairness
Matthew Wicker, Vihari Piratia, Adrian Weller
Providing formal guarantees of algorithmic fairness is of paramount importance to socially responsible deployment of machine learning algorithms. In this work, we study formal guar…
Implicit Training of Energy Model for Structure Prediction
Shiv Shankar, Vihari Piratla
Most deep learning research has focused on developing new model and training procedures. On the other hand the training objective has usually been restricted to combinations of sta…
Active Assessment of Prediction Services as Accuracy Surface Over Attribute Combinations
Vihari Piratla, Soumen Chakrabarty, Sunita Sarawagi
Our goal is to evaluate the accuracy of a black-box classification model, not as a single aggregate on a given test data distribution, but as a surface over a large number of combi…
An Analysis of Frame-skipping in Reinforcement Learning
Shivaram Kalyanakrishnan, Siddharth Aravindan, Vishwajeet Bagdawat +5
In the practice of sequential decision making, agents are often designed to sense state at regular intervals of time steps, , ignoring state information in between sensi…
NLP Service APIs and Models for Efficient Registration of New Clients
Sahil Shah, Vihari Piratla, Soumen Chakrabarti +1
State-of-the-art NLP inference uses enormous neural architectures and models trained for GPU-months, well beyond the reach of most consumers of NLP. This has led to one-size-fits-a…
Untapped Potential of Data Augmentation: A Domain Generalization Viewpoint
Vihari Piratla, Shiv Shankar
Data augmentation is a popular pre-processing trick to improve generalization accuracy. It is believed that by processing augmented inputs in tandem with the original ones, the mod…