South America
Promises and Pitfalls of Threshold-based Auto-labeling
TBAL is emerging as a widely-used solution in practice.
Optimal cross-learning for contextual bandits with unknown context distributions
We consider the problem of designing contextual bandit algorithms in the "cross-learning" setting of Balseiro et al., where the learner observes the loss for the action
Optimal cross-learning for contextual bandits with unknown context distributions
We consider the problem of designing contextual bandit algorithms in the "cross-learning" setting of Balseiro et al., where the learner observes the loss for the action
Faster Differentially Private Top-k Selection: A Joint Exponential Mechanism with Pruning Hao WU
Their algorithm has a time and space complexity of O ( dk) .
On the Need for a Language Describing Distribution Shifts: Illustrations on Tabular Datasets
Different distribution shifts require different algorithmic and operational interventions. Methodological research must be grounded by the specific shifts they address.
Provably Optimal Memory Capacity for Modern Hopfield Models: Transformer-Compatible Dense Associative Memories as Spherical Codes Jerry Y ao-Chieh Hu Dennis Wu
We study the optimal memorization capacity of modern Hopfield models and Kernelized Hopfield Models (KHMs), a transformer-compatible class of Dense Associative Memories.
9f42f06a54ce3b709ad78d34c73e4363-Paper-Conference.pdf
Therefore, neuro-symbolic RL aims at creating policies that are interpretable in the first place. Unfortunately, interpretability is not explainability. To achieve both, we introduce Neurally gUided Differentiable loGic policiEs (NUDGE).
