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8 papers · 2 filters
On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data
Di Wang, Hanshen Xiao, Srini Devadas +1
In this paper, we consider the problem of designing Differentially Private (DP) algorithms for Stochastic Convex Optimization (SCO) on heavy-tailed data. The irregularity of such d…
CryptoCredit: Securely Training Fair Models
Leo de Castro, Jiahao Chen, Antigoni Polychroniadou
When developing models for regulated decision making, sensitive features like age, race and gender cannot be used and must be obscured from model developers to prevent bias. Howeve…
Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series
Vincent Jacob, Fei Song, Arnaud Stiegler +3
Access to high-quality data repositories and benchmarks have been instrumental in advancing the state of the art in many experimental research domains. While advanced analytics tas…
Trajectory Inspection: A Method for Iterative Clinician-Driven Design of Reinforcement Learning Studies
Christina X. Ji, Michael Oberst, Sanjat Kanjilal +1
Reinforcement learning (RL) has the potential to significantly improve clinical decision making. However, treatment policies learned via RL from observational data are sensitive to…
DiffTune: Optimizing CPU Simulator Parameters with Learned Differentiable Surrogates
Alex Renda, Yishen Chen, Charith Mendis +1
CPU simulators are useful tools for modeling CPU execution behavior. However, they suffer from inaccuracies due to the cost and complexity of setting their fine-grained parameters,…
GraphChallenge.org Sparse Deep Neural Network Performance
Jeremy Kepner, Simon Alford, Vijay Gadepally +5
The MIT/IEEE/Amazon GraphChallenge.org encourages community approaches to developing new solutions for analyzing graphs and sparse data. Sparse AI analytics present unique scalabil…