41 citations · 90 across the 8 of their papers we have counts for
8 papers
Tecnologica cosa: Modeling Storyteller Personalities in Boccaccio's Decameron
A. Feder Cooper, Maria Antoniak, Christopher De Sa +2
We explore Boccaccio's Decameron to see how digital humanities tools can be used for tasks that have limited data in a language no longer in contemporary use: medieval Italian. We…
Model Selection's Disparate Impact in Real-World Deep Learning Applications
Jessica Zosa Forde, A. Feder Cooper, Kweku Kwegyir-Aggrey +2
Algorithmic fairness has emphasized the role of biased data in automated decision outcomes. Recently, there has been a shift in attention to sources of bias that implicate fairness…
Hyperparameter Optimization Is Deceiving Us, and How to Stop It
A. Feder Cooper, Yucheng Lu, Jessica Zosa Forde +1
Recent empirical work shows that inconsistent results based on choice of hyperparameter optimization (HPO) configuration are a widespread problem in ML research. When comparing two…
Emergent Unfairness in Algorithmic Fairness-Accuracy Trade-Off Research
A. Feder Cooper, Ellen Abrams
Across machine learning (ML) sub-disciplines, researchers make explicit mathematical assumptions in order to facilitate proof-writing. We note that, specifically in the area of fai…
Where Is the Normative Proof? Assumptions and Contradictions in ML Fairness Research
A. Feder Cooper
Across machine learning (ML) sub-disciplines researchers make mathematical assumptions to facilitate proof-writing. While such assumptions are necessary for providing mathematical…
Accuracy-Efficiency Trade-Offs and Accountability in Distributed ML Systems
A. Feder Cooper, Karen Levy, Christopher De Sa
Trade-offs between accuracy and efficiency pervade law, public health, and other non-computing domains, which have developed policies to guide how to balance the two in conditions…
Asymptotically Optimal Exact Minibatch Metropolis-Hastings
Ruqi Zhang, A. Feder Cooper, Christopher De Sa
Metropolis-Hastings (MH) is a commonly-used MCMC algorithm, but it can be intractable on large datasets due to requiring computations over the whole dataset. In this paper, we stud…
AMAGOLD: Amortized Metropolis Adjustment for Efficient Stochastic Gradient MCMC
Ruqi Zhang, A. Feder Cooper, Christopher De Sa
Stochastic gradient Hamiltonian Monte Carlo (SGHMC) is an efficient method for sampling from continuous distributions. It is a faster alternative to HMC: instead of using the whole…