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The Best of Many Worlds: Dual Mirror Descent for Online Allocation Problems
Online allocation problems with resource constraints are central problem...
11/18/2020 ∙ by Santiago Balseiro, et al. ∙
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Limiting Behaviors of Nonconvex-Nonconcave Minimax Optimization via Continuous-Time Systems
Unlike nonconvex optimization, where gradient descent is guaranteed to c...
10/20/2020 ∙ by Benjamin Grimmer, et al. ∙
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Regularized Online Allocation Problems: Fairness and Beyond
Online allocation problems with resource constraints have a rich history...
07/01/2020 ∙ by Santiago Balseiro, et al. ∙
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The Landscape of Nonconvex-Nonconcave Minimax Optimization
Minimax optimization has become a central tool for modern machine learni...
06/15/2020 ∙ by Benjamin Grimmer, et al. ∙
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Contextual Reserve Price Optimization in Auctions
We study the problem of learning a linear model to set the reserve price...
02/20/2020 ∙ by Joey Huchette, et al. ∙
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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 (...
01/23/2020 ∙ by Haihao Lu, et al. ∙
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A Stochastic First-Order Method for Ordered Empirical Risk Minimization
We propose a new stochastic first-order method for empirical risk minimi...
07/09/2019 ∙ by Kenji Kawaguchi, et al. ∙
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Accelerating Gradient Boosting Machine
Gradient Boosting Machine (GBM) is an extremely powerful supervised lear...
03/20/2019 ∙ by Haihao Lu, et al. ∙
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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...
10/25/2018 ∙ by Jonathan Amar, et al. ∙
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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...
10/25/2018 ∙ by Jonathan Amar, et al. ∙
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Randomized Gradient Boosting Machine
Gradient Boosting Machine (GBM) introduced by Friedman is an extremely p...
10/24/2018 ∙ by Haihao Lu, et al. ∙
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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_...
10/04/2018 ∙ by Shuaiwen Wang, et al. ∙
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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...
07/07/2018 ∙ by Shuaiwen Wang, et al. ∙
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Depth Creates No Bad Local Minima
In deep learning, depth, as well as nonlinearity, create non-convex loss...
02/27/2017 ∙ by Haihao Lu, et al. ∙
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