41 citations · 90 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
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…