1 citations · 1 across the 10 of their papers we have counts for
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Discovering Data Structures: Nearest Neighbor Search and Beyond
Omar Salemohamed, Laurent Charlin, Shivam Garg +2
We propose a general framework for end-to-end learning of data structures. Our framework adapts to the underlying data distribution and provides fine-grained control over query and…
Limitations on Accurate, Trusted, Human-level Reasoning
Rina Panigrahy, Vatsal Sharan
We identify a fundamental incompatibility between the goals of accuracy, trust, and human-level reasoning in artificial intelligence (AI) systems, for strict mathematical definitio…
Understanding Contextual Recall in Transformers: How Finetuning Enables In-Context Reasoning over Pretraining Knowledge
Bhavya Vasudeva, Puneesh Deora, Alberto Bietti +2
Transformer-based language models excel at in-context learning (ICL), where they can adapt to new tasks based on contextual examples, without parameter updates. In a specific form…
Transformers Provably Learn Algorithmic Solutions for Graph Connectivity, But Only with the Right Data
Qilin Ye, Deqing Fu, Robin Jia +1
Transformers often fail to learn generalizable algorithms, instead relying on brittle heuristics. Using graph connectivity as a testbed, we explain this phenomenon both theoretical…
How Muon's Spectral Design Benefits Generalization: A Study on Imbalanced Data
Bhavya Vasudeva, Puneesh Deora, Yize Zhao +2
The growing adoption of spectrum-aware matrix-valued optimizers such as Muon and Shampoo in deep learning motivates a systematic study of their generalization properties and, in pa…
Proper Learnability and the Role of Unlabeled Data
Julian Asilis, Siddartha Devic, Shaddin Dughmi +2
Proper learning refers to the setting in which learners must emit predictors in the underlying hypothesis class , and often leads to learners with simple algorithmic forms (e.g.…