23.6k citations
- University of California, BerkeleyUS250 papers
- California Institute of TechnologyUS24 papers
- Lawrence Berkeley National LaboratoryUS16 papers
- University of ChicagoUS14 papers
- UCLA HealthUS13 papers
- Google (United States)US12 papers
- Moscow Institute of Thermal TechnologyRU11 papers
- University of MichiganUS11 papers
- Carnegie Mellon UniversityUS10 papers
- Centre National de la Recherche ScientifiqueFR10 papers
- Michigan Science CenterUS10 papers
- Stanford UniversityUS10 papers
5 papers · 2 filters
Improving Fairness and Privacy in Selection Problems
Mohammad Mahdi Khalili, Xueru Zhang, Mahed Abroshan +1
Supervised learning models have been increasingly used for making decisions about individuals in applications such as hiring, lending, and college admission. These models may inher…
Amortized Conditional Normalized Maximum Likelihood: Reliable Out of Distribution Uncertainty Estimation
Aurick Zhou, Sergey Levine
While deep neural networks provide good performance for a range of challenging tasks, calibration and uncertainty estimation remain major challenges, especially under distribution…
Variable Skipping for Autoregressive Range Density Estimation
Eric Liang, Zongheng Yang, Ion Stoica +3
Deep autoregressive models compute point likelihood estimates of individual data points. However, many applications (i.e., database cardinality estimation) require estimating range…
Accelerated Message Passing for Entropy-Regularized MAP Inference
Jonathan N. Lee, Aldo Pacchiano, Peter Bartlett +1
Maximum a posteriori (MAP) inference in discrete-valued Markov random fields is a fundamental problem in machine learning that involves identifying the most likely configuration of…
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?
Angelos Filos, Panagiotis Tigas, Rowan McAllister +3
Out-of-training-distribution (OOD) scenarios are a common challenge of learning agents at deployment, typically leading to arbitrary deductions and poorly-informed decisions. In pr…