Search Results for author: Samuel C. Hoffman

Found 13 papers, 3 papers with code

Function Composition in Trustworthy Machine Learning: Implementation Choices, Insights, and Questions

no code implementations17 Feb 2023 Manish Nagireddy, Moninder Singh, Samuel C. Hoffman, Evaline Ju, Karthikeyan Natesan Ramamurthy, Kush R. Varshney

In this paper, focusing specifically on compositions of functions arising from the different pillars, we aim to reduce this gap, develop new insights for trustworthy ML, and answer questions such as the following.

Adversarial Robustness Fairness

Navigating Ensemble Configurations for Algorithmic Fairness

no code implementations11 Oct 2022 Michael Feffer, Martin Hirzel, Samuel C. Hoffman, Kiran Kate, Parikshit Ram, Avraham Shinnar

Bias mitigators can improve algorithmic fairness in machine learning models, but their effect on fairness is often not stable across data splits.

Ensemble Learning Fairness +1

Causal Graphs Underlying Generative Models: Path to Learning with Limited Data

no code implementations14 Jul 2022 Samuel C. Hoffman, Kahini Wadhawan, Payel Das, Prasanna Sattigeri, Karthikeyan Shanmugam

In this work, we provide a simple algorithm that relies on perturbation experiments on latent codes of a pre-trained generative autoencoder to uncover a causal graph that is implied by the generative model.

CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models

no code implementations NeurIPS 2020 Vijil Chenthamarakshan, Payel Das, Samuel C. Hoffman, Hendrik Strobelt, Inkit Padhi, Kar Wai Lim, Benjamin Hoover, Matteo Manica, Jannis Born, Teodoro Laino, Aleksandra Mojsilovic

CogMol also includes insilico screening for assessing toxicity of parent molecules and their metabolites with a multi-task toxicity classifier, synthetic feasibility with a chemical retrosynthesis predictor, and target structure binding with docking simulations.

Retrosynthesis Specificity

Surrogate-Based Constrained Langevin Sampling With Applications to Optimal Material Configuration Design

no code implementations25 Sep 2019 Thanh V Nguyen, Youssef Mroueh, Samuel C. Hoffman, Payel Das, Pierre Dognin, Giuseppe Romano, Chinmay Hegde

We consider the problem of generating configurations that satisfy physical constraints for optimal material nano-pattern design, where multiple (and often conflicting) properties need to be simultaneously satisfied.

Fairness GAN

no code implementations24 May 2018 Prasanna Sattigeri, Samuel C. Hoffman, Vijil Chenthamarakshan, Kush R. Varshney

In this paper, we introduce the Fairness GAN, an approach for generating a dataset that is plausibly similar to a given multimedia dataset, but is more fair with respect to protected attributes in allocative decision making.

Decision Making Fairness

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