2 citations · 2 across the 3 of their papers we have counts for
3 papers
Superquantiles at Work: Machine Learning Applications and Efficient Subgradient Computation
Yassine Laguel, Krishna Pillutla, Jérôme Malick +1
R. Tyrell Rockafellar and collaborators introduced, in a series of works, new regression modeling methods based on the notion of superquantile (or conditional value-at-risk). These…
LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes
Aditya Kusupati, Matthew Wallingford, Vivek Ramanujan +6
Learning binary representations of instances and classes is a classical problem with several high potential applications. In modern settings, the compression of high-dimensional ne…
A Smoother Way to Train Structured Prediction Models
Krishna Pillutla, Vincent Roulet, Sham M. Kakade +1
We present a framework to train a structured prediction model by performing smoothing on the inference algorithm it builds upon. Smoothing overcomes the non-smoothness inherent to…