11 citations · 22 across the 6 of their papers we have counts for
11 papers
Toward Understanding Privileged Features Distillation in Learning-to-Rank
Shuo Yang, Sujay Sanghavi, Holakou Rahmanian +2
In learning-to-rank problems, a privileged feature is one that is available during model training, but not available at test time. Such features naturally arise in merchandised rec…
Combinatorial Bandits without Total Order for Arms
Shuo Yang, Tongzheng Ren, Inderjit S. Dhillon +1
We consider the combinatorial bandits problem, where at each time step, the online learner selects a size- subset from the arms set , where $\left|\mathcal{A}\r…
Faster Non-Convex Federated Learning via Global and Local Momentum
Rudrajit Das, Anish Acharya, Abolfazl Hashemi +3
We propose \texttt{FedGLOMO}, a novel federated learning (FL) algorithm with an iteration complexity of to converge to an -stationary point (i.e., $\math…
On Generalization of Adaptive Methods for Over-parameterized Linear Regression
Vatsal Shah, Soumya Basu, Anastasios Kyrillidis +1
Over-parameterization and adaptive methods have played a crucial role in the success of deep learning in the last decade. The widespread use of over-parameterization has forced us…
On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Optimization
Abolfazl Hashemi, Anish Acharya, Rudrajit Das +3
In decentralized optimization, it is common algorithmic practice to have nodes interleave (local) gradient descent iterations with gossip (i.e. averaging over the network) steps. M…
Extreme Multi-label Classification from Aggregated Labels
Yanyao Shen, Hsiang-fu Yu, Sujay Sanghavi +1
Extreme multi-label classification (XMC) is the problem of finding the relevant labels for an input, from a very large universe of possible labels. We consider XMC in the setting w…