14 citations · 58 across the 43 of their papers we have counts for
7 papers · 1 filter
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…
Pre-trained Large Language Models Use Fourier Features to Compute Addition
Tianyi Zhou, Deqing Fu, Vatsal Sharan +1
Pre-trained large language models (LLMs) exhibit impressive mathematical reasoning capabilities, yet how they compute basic arithmetic, such as addition, remains unclear. This pape…
When is Multicalibration Post-Processing Necessary?
Dutch Hansen, Siddartha Devic, Preetum Nakkiran +1
Calibration is a well-studied property of predictors which guarantees meaningful uncertainty estimates. Multicalibration is a related notion -- originating in algorithmic fairness…
Optimal Multiclass U-Calibration Error and Beyond
Haipeng Luo, Spandan Senapati, Vatsal Sharan
We consider the problem of online multiclass U-calibration, where a forecaster aims to make sequential distributional predictions over classes with low U-calibration error, tha…
Transformers Learn Low Sensitivity Functions: Investigations and Implications
Bhavya Vasudeva, Deqing Fu, Tianyi Zhou +3
Transformers achieve state-of-the-art accuracy and robustness across many tasks, but an understanding of their inductive biases and how those biases differ from other neural networ…
Stability and Multigroup Fairness in Ranking with Uncertain Predictions
Siddartha Devic, Aleksandra Korolova, David Kempe +1
Rankings are ubiquitous across many applications, from search engines to hiring committees. In practice, many rankings are derived from the output of predictors. However, when pred…
Transductive Learning Is Compact
Julian Asilis, Siddartha Devic, Shaddin Dughmi +2
We demonstrate a compactness result holding broadly across supervised learning with a general class of loss functions: Any hypothesis class is learnable with transductive sampl…