7 citations · 22 across the 11 of their papers we have counts for
6 papers · 1 filter
Efficient Online Inverse Optimization with Regret
Yang Cai, Anupam Gupta, Vineet Gupta +7
We give a deterministic algorithm for online inverse linear optimization with regret , uniform in the horizon and time per round. A bound of this order was obtaine…
Functional Interpolation for Relative Positions Improves Long Context Transformers
Shanda Li, Chong You, Guru Guruganesh +7
Preventing the performance decay of Transformers on inputs longer than those used for training has been an important challenge in extending the context length of these models. Thou…
A Fourier Approach to Mixture Learning
Mingda Qiao, Guru Guruganesh, Ankit Singh Rawat +2
We revisit the problem of learning mixtures of spherical Gaussians. Given samples from mixture , the goal is to estimate the means $…
Contextual Recommendations and Low-Regret Cutting-Plane Algorithms
Sreenivas Gollapudi, Guru Guruganesh, Kostas Kollias +3
We consider the following variant of contextual linear bandits motivated by routing applications in navigational engines and recommendation systems. We wish to learn a hidden -d…
Scalable Hierarchical Agglomerative Clustering
Nicholas Monath, Avinava Dubey, Guru Guruganesh +9
The applicability of agglomerative clustering, for inferring both hierarchical and flat clustering, is limited by its scalability. Existing scalable hierarchical clustering methods…
Big Bird: Transformers for Longer Sequences
Manzil Zaheer, Guru Guruganesh, Avinava Dubey +8
Transformers-based models, such as BERT, have been one of the most successful deep learning models for NLP. Unfortunately, one of their core limitations is the quadratic dependency…