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∙ 07/10/2023

Online Ad Procurement in Non-stationary Autobidding Worlds

Today's online advertisers procure digital ad impressions through intera...

0 Jason Cheuk Nam Liang, et al. ∙

∙ 02/16/2023

Joint Feedback Loop for Spend and Return-On-Spend Constraints

Budget pacing is a popular service that has been offered by major intern...

0 Santiago R. Balseiro, et al. ∙

∙ 02/12/2022

From Online Optimization to PID Controllers: Mirror Descent with Momentum

We study a family of first-order methods with momentum based on mirror d...

0 Santiago R. Balseiro, et al. ∙

∙ 02/04/2022

A J-Symmetric Quasi-Newton Method for Minimax Problems

Minimax problems have gained tremendous attentions across the optimizati...

0 Azam Asl, et al. ∙

∙ 11/10/2021

Linear Convergence of Stochastic Primal Dual Methods for Linear Programming Using Variance Reduction and Restarts

There is a recent interest on first-order methods for linear programming...

0 Haihao Lu, et al. ∙

∙ 11/18/2020

The Best of Many Worlds: Dual Mirror Descent for Online Allocation Problems

Online allocation problems with resource constraints are central problem...

0 Santiago Balseiro, et al. ∙

∙ 10/20/2020

Limiting Behaviors of Nonconvex-Nonconcave Minimax Optimization via Continuous-Time Systems

Unlike nonconvex optimization, where gradient descent is guaranteed to c...

0 Benjamin Grimmer, et al. ∙

∙ 07/01/2020

Regularized Online Allocation Problems: Fairness and Beyond

Online allocation problems with resource constraints have a rich history...

0 Santiago Balseiro, et al. ∙

∙ 06/15/2020

The Landscape of Nonconvex-Nonconcave Minimax Optimization

Minimax optimization has become a central tool for modern machine learni...

0 Benjamin Grimmer, et al. ∙

∙ 02/20/2020

Contextual Reserve Price Optimization in Auctions

We study the problem of learning a linear model to set the reserve price...

0 Joey Huchette, et al. ∙

∙ 01/23/2020

An O(s^r)-Resolution ODE Framework for Discrete-Time Optimization Algorithms and Applications to Convex-Concave Saddle-Point Problems

There has been a long history of using Ordinary Differential Equations (...

0 Haihao Lu, et al. ∙

∙ 07/09/2019

A Stochastic First-Order Method for Ordered Empirical Risk Minimization

We propose a new stochastic first-order method for empirical risk minimi...

5 Kenji Kawaguchi, et al. ∙

∙ 03/20/2019

Accelerating Gradient Boosting Machine

Gradient Boosting Machine (GBM) is an extremely powerful supervised lear...

0 Haihao Lu, et al. ∙

∙ 10/25/2018

The Second-Price Knapsack Problem: Near-Optimal Real Time Bidding in Internet Advertisement

In the past decade, Real Time Bidding (RTB) has become one of the most c...

0 Jonathan Amar, et al. ∙

∙ 10/25/2018

Near-Optimal Online Knapsack Strategy for Real-Time Bidding in Internet Advertising

In the past decade, Real Time Bidding (RTB) has become one of the most c...

0 Jonathan Amar, et al. ∙

∙ 10/24/2018

Randomized Gradient Boosting Machine

Gradient Boosting Machine (GBM) introduced by Friedman is an extremely p...

0 Haihao Lu, et al. ∙

∙ 10/04/2018

Approximate Leave-One-Out for High-Dimensional Non-Differentiable Learning Problems

Consider the following class of learning schemes: β̂ := β∈C ∑_j=1^n ℓ(x_...

0 Shuaiwen Wang, et al. ∙

∙ 07/07/2018

Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions

Consider the following class of learning schemes: β̂ := _β ∑_j=1^n ℓ(x_j...

0 Shuaiwen Wang, et al. ∙

∙ 02/27/2017

Depth Creates No Bad Local Minima

In deep learning, depth, as well as nonlinearity, create non-convex loss...

0 Haihao Lu, et al. ∙

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