Search Results for author:
Found 18 papers, 7 papers with code
The Power of Linear Programming in Sponsored Listings Ranking: Evidence from Field Experiments
Determining how to rank these sponsored items for each incoming visit is a crucial challenge for online marketplaces, a problem known as sponsored listings ranking (SLR).
Analysis of Dual-Based PID Controllers through Convolutional Mirror Descent
As a byproduct of our proofs, we provide the first regret bound for CMD for non-smooth convex optimization, which might be of independent interest.
Nearly Optimal Linear Convergence of Stochastic Primal-Dual Methods for Linear Programming
There is a recent interest on first-order methods for linear programming (LP).
Contextual Reserve Price Optimization in Auctions via Mixed Integer Programming
Moreover, we show that this MIP formulation is ideal (i. e. the strongest possible formulation) for the revenue function of a single impression.
The Best of Many Worlds: Dual Mirror Descent for Online Allocation Problems
In this paper, we consider a data-driven setting in which the reward and resource consumption of each request are generated using an input model that is unknown to the decision maker.
Limiting Behaviors of Nonconvex-Nonconcave Minimax Optimization via Continuous-Time Systems
Unlike nonconvex optimization, where gradient descent is guaranteed to converge to a local optimizer, algorithms for nonconvex-nonconcave minimax optimization can have topologically different solution paths: sometimes converging to a solution, sometimes never converging and instead following a limit cycle, and sometimes diverging.
Regularized Online Allocation Problems: Fairness and Beyond
In this paper, we introduce the \emph{regularized online allocation problem}, a variant that includes a non-linear regularizer acting on the total resource consumption.
The Landscape of the Proximal Point Method for Nonconvex-Nonconcave Minimax Optimization
Critically, we show this envelope not only smooths the objective but can convexify and concavify it based on the level of interaction present between the minimizing and maximizing variables.
Dual Mirror Descent for Online Allocation Problems
The revenue function and resource consumption of each request are drawn independently and at random from a probability distribution that is unknown to the decision maker.
Optimization and Control
Contextual Reserve Price Optimization in Auctions via Mixed-Integer Programming
Moreover, we show that this MIP formulation is ideal (i. e. the strongest possible formulation) for the revenue function of a single impression.
An $O(s^r)$-Resolution ODE Framework for Understanding Discrete-Time Algorithms and Applications to the Linear Convergence of Minimax Problems
Surprisingly, there are still two fundamental and unanswered questions: (i) it is unclear how to obtain a \emph{suitable} ODE from a given DTA, and (ii) it is unclear the connection between the convergence of a DTA and its corresponding ODEs.
Ordered SGD: A New Stochastic Optimization Framework for Empirical Risk Minimization
The traditional approaches, such as (mini-batch) stochastic gradient descent (SGD), utilize an unbiased gradient estimator of the empirical average loss.
Accelerating Gradient Boosting Machine
This is the first GBM type of algorithm with theoretically-justified accelerated convergence rate.
Randomized Gradient Boosting Machine
Gradient Boosting Machine (GBM) introduced by Friedman is a powerful supervised learning algorithm that is very widely used in practice---it routinely features as a leading algorithm in machine learning competitions such as Kaggle and the KDDCup.
Approximate Leave-One-Out for High-Dimensional Non-Differentiable Learning Problems
$\mathcal{C} \subset \mathbb{R}^{p}$ is a closed convex set.
Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions
Consider the following class of learning schemes: $$\hat{\boldsymbol{\beta}} := \arg\min_{\boldsymbol{\beta}}\;\sum_{j=1}^n \ell(\boldsymbol{x}_j^\top\boldsymbol{\beta}; y_j) + \lambda R(\boldsymbol{\beta}),\qquad\qquad (1) $$ where $\boldsymbol{x}_i \in \mathbb{R}^p$ and $y_i \in \mathbb{R}$ denote the $i^{\text{th}}$ feature and response variable respectively.
Accelerating Greedy Coordinate Descent Methods
On the empirical side, while both AGCD and ASCD outperform Accelerated Randomized Coordinate Descent on most instances in our numerical experiments, we note that AGCD significantly outperforms the other two methods in our experiments, in spite of a lack of theoretical guarantees for this method.
Depth Creates No Bad Local Minima
In deep learning, \textit{depth}, as well as \textit{nonlinearity}, create non-convex loss surfaces.
