Alert button
Picture for Lalitha Sankar

Lalitha Sankar

Alert button

AugLoss: A Learning Methodology for Real-World Dataset Corruption

Jun 05, 2022
Kyle Otstot, John Kevin Cava, Tyler Sypherd, Lalitha Sankar

Figure 1 for AugLoss: A Learning Methodology for Real-World Dataset Corruption
Figure 2 for AugLoss: A Learning Methodology for Real-World Dataset Corruption
Figure 3 for AugLoss: A Learning Methodology for Real-World Dataset Corruption
Figure 4 for AugLoss: A Learning Methodology for Real-World Dataset Corruption
Viaarxiv icon

$α$-GAN: Convergence and Estimation Guarantees

May 12, 2022
Gowtham R. Kurri, Monica Welfert, Tyler Sypherd, Lalitha Sankar

Figure 1 for $α$-GAN: Convergence and Estimation Guarantees
Figure 2 for $α$-GAN: Convergence and Estimation Guarantees
Figure 3 for $α$-GAN: Convergence and Estimation Guarantees
Figure 4 for $α$-GAN: Convergence and Estimation Guarantees
Viaarxiv icon

A Machine Learning Framework for Event Identification via Modal Analysis of PMU Data

Feb 14, 2022
Nima T. Bazargani, Gautam Dasarathy, Lalitha Sankar, Oliver Kosut

Figure 1 for A Machine Learning Framework for Event Identification via Modal Analysis of PMU Data
Figure 2 for A Machine Learning Framework for Event Identification via Modal Analysis of PMU Data
Figure 3 for A Machine Learning Framework for Event Identification via Modal Analysis of PMU Data
Figure 4 for A Machine Learning Framework for Event Identification via Modal Analysis of PMU Data
Viaarxiv icon

Being Properly Improper

Jun 18, 2021
Richard Nock, Tyler Sypherd, Lalitha Sankar

Figure 1 for Being Properly Improper
Figure 2 for Being Properly Improper
Figure 3 for Being Properly Improper
Figure 4 for Being Properly Improper
Viaarxiv icon

Realizing GANs via a Tunable Loss Function

Jun 09, 2021
Gowtham R. Kurri, Tyler Sypherd, Lalitha Sankar

Figure 1 for Realizing GANs via a Tunable Loss Function
Figure 2 for Realizing GANs via a Tunable Loss Function
Viaarxiv icon

Three Variants of Differential Privacy: Lossless Conversion and Applications

Aug 14, 2020
Shahab Asoodeh, Jiachun Liao, Flavio P. Calmon, Oliver Kosut, Lalitha Sankar

Figure 1 for Three Variants of Differential Privacy: Lossless Conversion and Applications
Figure 2 for Three Variants of Differential Privacy: Lossless Conversion and Applications
Figure 3 for Three Variants of Differential Privacy: Lossless Conversion and Applications
Figure 4 for Three Variants of Differential Privacy: Lossless Conversion and Applications
Viaarxiv icon

On the alpha-loss Landscape in the Logistic Model

Jun 22, 2020
Tyler Sypherd, Mario Diaz, Lalitha Sankar, Gautam Dasarathy

Figure 1 for On the alpha-loss Landscape in the Logistic Model
Figure 2 for On the alpha-loss Landscape in the Logistic Model
Figure 3 for On the alpha-loss Landscape in the Logistic Model
Viaarxiv icon

A Better Bound Gives a Hundred Rounds: Enhanced Privacy Guarantees via $f$-Divergences

Jan 16, 2020
Shahab Asoodeh, Jiachun Liao, Flavio P. Calmon, Oliver Kosut, Lalitha Sankar

Figure 1 for A Better Bound Gives a Hundred Rounds: Enhanced Privacy Guarantees via $f$-Divergences
Figure 2 for A Better Bound Gives a Hundred Rounds: Enhanced Privacy Guarantees via $f$-Divergences
Figure 3 for A Better Bound Gives a Hundred Rounds: Enhanced Privacy Guarantees via $f$-Divergences
Viaarxiv icon

Theoretical Guarantees for Model Auditing with Finite Adversaries

Nov 08, 2019
Mario Diaz, Peter Kairouz, Jiachun Liao, Lalitha Sankar

Figure 1 for Theoretical Guarantees for Model Auditing with Finite Adversaries
Viaarxiv icon

Learning Generative Adversarial RePresentations (GAP) under Fairness and Censoring Constraints

Sep 27, 2019
Jiachun Liao, Chong Huang, Peter Kairouz, Lalitha Sankar

Figure 1 for Learning Generative Adversarial RePresentations (GAP) under Fairness and Censoring Constraints
Figure 2 for Learning Generative Adversarial RePresentations (GAP) under Fairness and Censoring Constraints
Figure 3 for Learning Generative Adversarial RePresentations (GAP) under Fairness and Censoring Constraints
Figure 4 for Learning Generative Adversarial RePresentations (GAP) under Fairness and Censoring Constraints
Viaarxiv icon