Search Results for author: Haihao Lu

Found 18 papers, 7 papers with code

The Power of Linear Programming in Sponsored Listings Ranking: Evidence from Field Experiments

no code implementations21 Mar 2024 Haihao Lu, Luyang Zhang

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

no code implementations12 Feb 2022 Santiago R. Balseiro, Haihao Lu, Vahab Mirrokni, Balasubramanian Sivan

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

no code implementations10 Nov 2021 Haihao Lu, Jinwen Yang

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

Contextual Reserve Price Optimization in Auctions via Mixed Integer Programming

1 code implementation NeurIPS 2020 Joey Huchette, Haihao Lu, Hossein Esfandiari, Vahab Mirrokni

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

no code implementations18 Nov 2020 Santiago Balseiro, Haihao Lu, Vahab Mirrokni

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.

Management

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

no code implementations20 Oct 2020 Benjamin Grimmer, Haihao Lu, Pratik Worah, Vahab Mirrokni

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

no code implementations1 Jul 2020 Santiago Balseiro, Haihao Lu, Vahab Mirrokni

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.

Fairness

The Landscape of the Proximal Point Method for Nonconvex-Nonconcave Minimax Optimization

no code implementations15 Jun 2020 Benjamin Grimmer, Haihao Lu, Pratik Worah, Vahab Mirrokni

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

no code implementations ICML 2020 Haihao Lu, Santiago Balseiro, Vahab Mirrokni

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

1 code implementation20 Feb 2020 Joey Huchette, Haihao Lu, Hossein Esfandiari, Vahab Mirrokni

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

no code implementations23 Jan 2020 Haihao Lu

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

2 code implementations9 Jul 2019 Kenji Kawaguchi, Haihao Lu

The traditional approaches, such as (mini-batch) stochastic gradient descent (SGD), utilize an unbiased gradient estimator of the empirical average loss.

Stochastic Optimization

Accelerating Gradient Boosting Machine

1 code implementation20 Mar 2019 Haihao Lu, Sai Praneeth Karimireddy, Natalia Ponomareva, Vahab Mirrokni

This is the first GBM type of algorithm with theoretically-justified accelerated convergence rate.

Randomized Gradient Boosting Machine

1 code implementation24 Oct 2018 Haihao Lu, Rahul Mazumder

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 Fast Parameter Tuning in High Dimensions

2 code implementations ICML 2018 Shuaiwen Wang, Wenda Zhou, Haihao Lu, Arian Maleki, Vahab Mirrokni

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.

Vocal Bursts Intensity Prediction

Accelerating Greedy Coordinate Descent Methods

no code implementations ICML 2018 Haihao Lu, Robert Freund, Vahab Mirrokni

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

no code implementations27 Feb 2017 Haihao Lu, Kenji Kawaguchi

In deep learning, \textit{depth}, as well as \textit{nonlinearity}, create non-convex loss surfaces.

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