431 citations · 1.2k across the 62 of their papers we have counts for
66 papers
DPO Unchained: Your Training Algorithm is Secretly Disentangled in Human Choice Theory (and its Loss' Convexity is Dispensable)
Wenxuan Zhou, Shujian Zhang, Brice Magdalou +4
Normative theories allow one to elicit key parts of a ML algorithm from first principles, which is crucial at a time of championed scrutiny for ML work. Direct Preference Optimizat…
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…
Generative Forests
Richard Nock, Mathieu Guillame-Bert
We focus on generative AI for a type of data that still represent one of the most prevalent form of data: tabular data. Our paper introduces two key contributions: a new powerful c…
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…