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Found 5 papers, 1 papers with code
Binary Noise for Binary Tasks: Masked Bernoulli Diffusion for Unsupervised Anomaly Detection
As diffusion-based methods require a lot of GPU memory and have long sampling times, we present a novel and fast unsupervised anomaly detection approach based on latent Bernoulli diffusion models.
Distributionally Robust Principal-Agent Problems and Optimality of Contracts
We construct a theoretical framework to certify whether any surjective contract family is optimal, and bound its sub-optimality.
A Mathematical Programming Approach to Optimal Classification Forests
In this paper, we introduce Optimal Classification Forests, a new family of classifiers that takes advantage of an optimal ensemble of decision trees to derive accurate and interpretable classifiers.
Model Mis-specification and Algorithmic Bias
We show that even with unbiased input data, when a model is mis-specified: (1) population-level mean prediction error can still be negligible, but group-level mean prediction errors can be large; (2) such errors are not equal across groups; and (3) the difference between errors, i. e., bias, can take the worst-case realization.
DEFT: Detection Embeddings for Tracking
DEFT has comparable accuracy and speed to the top methods on 2D online tracking leaderboards while having significant advantages in robustness when applied to more challenging tracking data.
Ranked #3 on
Multi-Object Tracking
on KITTI Tracking test
