Search Results for author: Holakou Rahmanian

Found 8 papers, 0 papers with code

A Sinkhorn-type Algorithm for Constrained Optimal Transport

no code implementations8 Mar 2024 Xun Tang, Holakou Rahmanian, Michael Shavlovsky, Kiran Koshy Thekumparampil, Tesi Xiao, Lexing Ying

We derive the corresponding entropy regularization formulation and introduce a Sinkhorn-type algorithm for such constrained OT problems supported by theoretical guarantees.

Scheduling

Accelerating Sinkhorn Algorithm with Sparse Newton Iterations

no code implementations20 Jan 2024 Xun Tang, Michael Shavlovsky, Holakou Rahmanian, Elisa Tardini, Kiran Koshy Thekumparampil, Tesi Xiao, Lexing Ying

To achieve possibly super-exponential convergence, we present Sinkhorn-Newton-Sparse (SNS), an extension to the Sinkhorn algorithm, by introducing early stopping for the matrix scaling steps and a second stage featuring a Newton-type subroutine.

Multi-Objective Optimization via Wasserstein-Fisher-Rao Gradient Flow

no code implementations22 Nov 2023 Yinuo Ren, Tesi Xiao, Tanmay Gangwani, Anshuka Rangi, Holakou Rahmanian, Lexing Ying, Subhajit Sanyal

Multi-objective optimization (MOO) aims to optimize multiple, possibly conflicting objectives with widespread applications.

Toward Understanding Privileged Features Distillation in Learning-to-Rank

no code implementations19 Sep 2022 Shuo Yang, Sujay Sanghavi, Holakou Rahmanian, Jan Bakus, S. V. N. Vishwanathan

Such features naturally arise in merchandised recommendation systems; for instance, "user clicked this item" as a feature is predictive of "user purchased this item" in the offline data, but is clearly not available during online serving.

Learning-To-Rank Recommendation Systems

Online Non-Additive Path Learning under Full and Partial Information

no code implementations18 Apr 2018 Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Holakou Rahmanian, Manfred K. Warmuth

We study the problem of online path learning with non-additive gains, which is a central problem appearing in several applications, including ensemble structured prediction.

Structured Prediction

Online Dynamic Programming

no code implementations NeurIPS 2017 Holakou Rahmanian, Manfred K. Warmuth

We consider the problem of repeatedly solving a variant of the same dynamic programming problem in successive trials.

Deep Embedding Forest: Forest-based Serving with Deep Embedding Features

no code implementations15 Mar 2017 Jie Zhu, Ying Shan, JC Mao, Dong Yu, Holakou Rahmanian, Yi Zhang

Built on top of a representative DNN model called Deep Crossing, and two forest/tree-based models including XGBoost and LightGBM, a two-step Deep Embedding Forest algorithm is demonstrated to achieve on-par or slightly better performance as compared with the DNN counterpart, with only a fraction of serving time on conventional hardware.

Online Learning of Combinatorial Objects via Extended Formulation

no code implementations17 Sep 2016 Holakou Rahmanian, David P. Helmbold, S. V. N. Vishwanathan

We present applications of our framework to online learning of Huffman trees and permutations.

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