661 citations · 3.5k across the 73 of their papers we have counts for
9 papers · 1 filter
Ideal Abstractions for Decision-Focused Learning
Michael Poli, Stefano Massaroli, Stefano Ermon +2
We present a methodology for formulating simplifying abstractions in machine learning systems by identifying and harnessing the utility structure of decisions. Machine learning tas…
An Empirical Analysis of Backward Compatibility in Machine Learning Systems
Megha Srivastava, Besmira Nushi, Ece Kamar +2
In many applications of machine learning (ML), updates are performed with the goal of enhancing model performance. However, current practices for updating models rely solely on iso…
Efficient Forward Architecture Search
Hanzhang Hu, John Langford, Rich Caruana +3
We propose a neural architecture search (NAS) algorithm, Petridish, to iteratively add shortcut connections to existing network layers. The added shortcut connections effectively p…
Metareasoning in Modular Software Systems: On-the-Fly Configuration using Reinforcement Learning with Rich Contextual Representations
Aditya Modi, Debadeepta Dey, Alekh Agarwal +4
Assemblies of modular subsystems are being pressed into service to perform sensing, reasoning, and decision making in high-stakes, time-critical tasks in such areas as transportati…
Towards Accountable AI: Hybrid Human-Machine Analyses for Characterizing System Failure
Besmira Nushi, Ece Kamar, Eric Horvitz
As machine learning systems move from computer-science laboratories into the open world, their accountability becomes a high priority problem. Accountability requires deep understa…
Discovering Blind Spots in Reinforcement Learning
Ramya Ramakrishnan, Ece Kamar, Debadeepta Dey +2
Agents trained in simulation may make errors in the real world due to mismatches between training and execution environments. These mistakes can be dangerous and difficult to disco…