29 citations · 144 across the 30 of their papers we have counts for
31 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…
Restructuring Vector Quantization with the Rotation Trick
Christopher Fifty, Ronald G. Junkins, Dennis Duan +5
Vector Quantized Variational AutoEncoders (VQ-VAEs) are designed to compress a continuous input to a discrete latent space and reconstruct it with minimal distortion. They operate…
Learning from straggler clients in federated learning
Andrew Hard, Antonious M. Girgis, Ehsan Amid +4
How well do existing federated learning algorithms learn from client devices that return model updates with a significant time delay? Is it even possible to learn effectively from…
Noise misleads rotation invariant algorithms on sparse targets
Manfred K. Warmuth, Wojciech Kotłowski, Matt Jones +1
It is well known that the class of rotation invariant algorithms are suboptimal even for learning sparse linear problems when the number of examples is below the "dimension" of the…
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…