Oceania
The Limit Points of (Optimistic) Gradient Descent in Min-Max Optimization
Constantinos Daskalakis, Ioannis Panageas
We characterize the limit points of two basic first order methods, namely Gradient Descent/Ascent (GDA) and Optimistic Gradient Descent Ascent (OGDA).
BridgingtheGapbetweenObjectandImage-level RepresentationsforOpen-VocabularyDetection
Open-vocabulary detection (OVD) aims to generalize beyond the limited number of base classes labeled during the training phase. The goal is to detect novel classes defined by an unbounded (open)vocabularyatinference.
VIREL: A Variational Inference Framework for Reinforcement Learning
Matthew Fellows, Anuj Mahajan, Tim G. J. Rudner, Shimon Whiteson
Neural Information Processing Systems http://nips.cc/
Revealing Distribution Discrepancy by Sampling Transfer in Unlabeled Data
The assumption that data are independently and identically distributed (IID) is staple in statistical machine learning. It suggests that a hypothesis selected by an algorithm, after observing several training samples, should perform effectively on test samples from the same unknown distribution.
Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer
David Madras, Toni Pitassi, Richard Zemel
Wepropose alearning algorithm which accounts for potential biases held by external decision-makers in a system.
