42 citations · 319 across the 36 of their papers we have counts for
38 papers
Tempered Calculus for ML: Application to Hyperbolic Model Embedding
Richard Nock, Ehsan Amid, Frank Nielsen +2
Most mathematical distortions used in ML are fundamentally integral in nature: -divergences, Bregman divergences, (regularized) optimal transport distances, integral probability…
The Tempered Hilbert Simplex Distance and Its Application To Non-linear Embeddings of TEMs
Ehsan Amid, Frank Nielsen, Richard Nock +1
Tempered Exponential Measures (TEMs) are a parametric generalization of the exponential family of distributions maximizing the tempered entropy function among positive measures sub…
Optimal Transport with Tempered Exponential Measures
Ehsan Amid, Frank Nielsen, Richard Nock +1
In the field of optimal transport, two prominent subfields face each other: (i) unregularized optimal transport, "à-la-Kantorovich", which leads to extremely sparse plans but with…
Boosting with Tempered Exponential Measures
Richard Nock, Ehsan Amid, Manfred K. Warmuth
One of the most popular ML algorithms, AdaBoost, can be derived from the dual of a relative entropy minimization problem subject to the fact that the positive weights on the exampl…
A Mechanism for Sample-Efficient In-Context Learning for Sparse Retrieval Tasks
Jacob Abernethy, Alekh Agarwal, Teodor V. Marinov +1
We study the phenomenon of \textit{in-context learning} (ICL) exhibited by large language models, where they can adapt to a new learning task, given a handful of labeled examples,…
Clustering above Exponential Families with Tempered Exponential Measures
Ehsan Amid, Richard Nock, Manfred Warmuth
The link with exponential families has allowed -means clustering to be generalized to a wide variety of data generating distributions in exponential families and clustering dist…