10 citations · 16 across the 6 of their papers we have counts for
6 papers
SureMap: Simultaneous Mean Estimation for Single-Task and Multi-Task Disaggregated Evaluation
Mikhail Khodak, Lester Mackey, Alexandra Chouldechova +1
Disaggregated evaluation -- estimation of performance of a machine learning model on different subpopulations -- is a core task when assessing performance and group-fairness of AI…
Provably Sample-Efficient RL with Side Information about Latent Dynamics
Yao Liu, Dipendra Misra, Miro Dudík +1
We study reinforcement learning (RL) in settings where observations are high-dimensional, but where an RL agent has access to abstract knowledge about the structure of the state sp…
Astral Space: Convex Analysis at Infinity
Miroslav Dudík, Robert E. Schapire, Matus Telgarsky
Not all convex functions on have finite minimizers; some can only be minimized by a sequence as it heads to infinity. In this work, we aim to develop a theory for un…
Bayesian decision-making under misspecified priors with applications to meta-learning
Max Simchowitz, Christopher Tosh, Akshay Krishnamurthy +4
Thompson sampling and other Bayesian sequential decision-making algorithms are among the most popular approaches to tackle explore/exploit trade-offs in (contextual) bandits. The c…
Interactive Learning from Activity Description
Khanh Nguyen, Dipendra Misra, Robert Schapire +2
We present a novel interactive learning protocol that enables training request-fulfilling agents by verbally describing their activities. Unlike imitation learning (IL), our protoc…
Gradient descent follows the regularization path for general losses
Ziwei Ji, Miroslav Dudík, Robert E. Schapire +1
Recent work across many machine learning disciplines has highlighted that standard descent methods, even without explicit regularization, do not merely minimize the training error,…