Tao Xiao

AAAI Conference 2025 Conference Paper

Optimal Auction Design for Mixed Bidders

• Xiaohui Bei
• Pinyan Lu
• Zhiqi Wang
• Tao Xiao
• Xiang Yan

The predominant setting in classic auction theory considers bidders as utility maximizers (UMs), who aim to maximize quasi-linear utility functions. Recent autobidding strategies in online advertising have sparked interest in auction design with value maximizers (VMs), who aim to maximize the total value obtained. In this work, we investigate revenue-maximizing auction design for selling a single item to a mix of UMs and VMs. Crucially, we assume the UM/VM type is private information of a bidder. This shift to a multi-parameter domain complicates the design of incentive compatible mechanisms. Under this setting, we first characterize the optimal auction structure for auctions with a single bidder. We observe that the optimal auction moves gradually from a first-price auction to a Myerson auction as the probability of the bidder being a UM increases from 0 to 1. We also extend our study to multi-bidder setting and present an algorithm for deriving the optimal lookahead auction with multiple mixed types of bidders.

ICLR Conference 2025 Conference Paper

Private Mechanism Design via Quantile Estimation

• Yuan-Yuan Yang
• Tao Xiao
• Bhuvesh Kumar
• Jamie Morgenstern

We investigate the problem of designing differentially private (DP), revenue-maximizing single item auction. Specifically, we consider broadly applicable settings in mechanism design where agents' valuation distributions are **independent**, **non-identical**, and can be either **bounded** or **unbounded**. Our goal is to design such auctions with **pure**, i.e., $(\epsilon,0)$ privacy in polynomial time. In this paper, we propose two computationally efficient auction learning framework that achieves **pure** privacy under bounded and unbounded distribution settings. These frameworks reduces the problem of privately releasing a revenue-maximizing auction to the private estimation of pre-specified quantiles. Our solutions increase the running time by polylog factors compared to the non-private version. As an application, we show how to extend our results to the multi-round online auction setting with non-myopic bidders. To our best knowledge, this paper is the first to efficiently deliver a Myerson auction with **pure** privacy and near-optimal revenue, and the first to provide such auctions for **unbounded** distributions.

NeurIPS Conference 2024 Conference Paper

Con4m: Context-aware Consistency Learning Framework for Segmented Time Series Classification

• Junru Chen
• Tianyu Cao
• Jing Xu
• Jiahe Li
• Zhilong Chen
• Tao Xiao
• Yang Yang

Time Series Classification (TSC) encompasses two settings: classifying entire sequences or classifying segmented subsequences. The raw time series for segmented TSC usually contain Multiple classes with Varying Duration of each class (MVD). Therefore, the characteristics of MVD pose unique challenges for segmented TSC, yet have been largely overlooked by existing works. Specifically, there exists a natural temporal dependency between consecutive instances (segments) to be classified within MVD. However, mainstream TSC models rely on the assumption of independent and identically distributed (i. i. d. ), focusing on independently modeling each segment. Additionally, annotators with varying expertise may provide inconsistent boundary labels, leading to unstable performance of noise-free TSC models. To address these challenges, we first formally demonstrate that valuable contextual information enhances the discriminative power of classification instances. Leveraging the contextual priors of MVD at both the data and label levels, we propose a novel consistency learning framework Con4m, which effectively utilizes contextual information more conducive to discriminating consecutive segments in segmented TSC tasks, while harmonizing inconsistent boundary labels for training. Extensive experiments across multiple datasets validate the effectiveness of Con4m in handling segmented TSC tasks on MVD. The source code is available at https: //github. com/MrNobodyCali/Con4m.

IJCAI Conference 2023 Conference Paper

Local-Global Transformer Enhanced Unfolding Network for Pan-sharpening

• Mingsong Li
• Yikun Liu
• Tao Xiao
• Yuwen Huang
• Gongping Yang

Pan-sharpening aims to increase the spatial resolution of the low-resolution multispectral (LrMS) image with the guidance of the corresponding panchromatic (PAN) image. Although deep learning (DL)-based pan-sharpening methods have achieved promising performance, most of them have a two-fold deficiency. For one thing, the universally adopted black box principle limits the model interpretability. For another thing, existing DL-based methods fail to efficiently capture local and global dependencies at the same time, inevitably limiting the overall performance. To address these mentioned issues, we first formulate the degradation process of the high-resolution multispectral (HrMS) image as a unified variational optimization problem, and alternately solve its data and prior subproblems by the designed iterative proximal gradient descent (PGD) algorithm. Moreover, we customize a Local-Global Transformer (LGT) to simultaneously model local and global dependencies, and further formulate an LGT-based prior module for image denoising. Besides the prior module, we also design a lightweight data module. Finally, by serially integrating the data and prior modules in each iterative stage, we unfold the iterative algorithm into a stage-wise unfolding network, Local-Global Transformer Enhanced Unfolding Network (LGTEUN), for the interpretable MS pan-sharpening. Comprehensive experimental results on three satellite data sets demonstrate the effectiveness and efficiency of LGTEUN compared with state-of-the-art (SOTA) methods. The source code is available at https: //github. com/lms-07/LGTEUN.

ICRA Conference 2021 Conference Paper

Design and soft-landing control of a six-legged mobile repetitive lander for lunar exploration

• Ke Yin
• Feng Gao 0011
• Qiao Sun 0002
• Jimu Liu
• Tao Xiao
• Jianzhong Yang
• Shuiqing Jiang
• Xianbao Chen

The autonomous robots consisting of an immovable lander and a rover are widely deployed to explore extraterrestrial planets. However, these robots have two main limitations: (1) the separate design for lander and rover respectively results in heavy mass and big volume of the whole system, which increases the launching cost sharply; (2) the rover’s detection area has to be restricted to the vicinity of the immovable lander. To overcome these problems, we designed a novel six-legged mobile repetitive lander called "HexaMRL", which integrates the functions of both lander and rover, including folding, deploying, repetitive soft-landing, and walking. A hybrid compliant mechanism taking advantages of both active and passive compliances was adopted on its leg. An integrated drive unit (IDU) was utilized to imitate the dynamics of a spring and a damper to absorb the landing impact energy, while the structure remains intact. Moreover, a control method based on state machine for soft-landing on the Moon was proposed. HexaMRL achieved repetitive soft-landing on a 5-DoF lunar gravity testing platform (5-DoF-LGTP) with a vertical landing velocity of 1. 9 m/s and a payload of 140 kg. The drive torque safety margin is improved by 23. 4%p based on the hybrid compliant leg comparing with the standalone active compliant leg.

STOC Conference 2019 Conference Paper

Tight approximation ratio of anonymous pricing

• Yaonan Jin
• Pinyan Lu
• Qi Qi 0003
• Zhihao Gavin Tang
• Tao Xiao

This paper considers two canonical Bayesian mechanism design settings. In the single-item setting, the tight approximation ratio of Anonymous Pricing is obtained: (1) compared to Myerson Auction, Anonymous Pricing always generates at least a 1/2.62-fraction of the revenue; (2) there is a matching lower-bound instance. In the unit-demand single-buyer setting, the tight approximation ratio between the simplest deterministic mechanism and the optimal deterministic mechanism is attained: in terms of revenue, (1) Uniform Pricing admits a 2.62-approximation to Item Pricing; (2) a matching lower-bound instance is presented also. These results answer two open questions asked by Alaei et al. (FOCS’15) and Cai and Daskalakis (GEB’15). As an implication, in the single-item setting: the approximation ratio of Second-Price Auction with Anonymous Reserve (Hartline and Roughgarden EC’09) is improved to 2.62, which breaks the best known upper bound of e ≈ 2.72.

SODA Conference 2019 Conference Paper

Tight Revenue Gaps among Simple Mechanisms

• Yaonan Jin
• Pinyan Lu
• Zhihao Gavin Tang
• Tao Xiao

We consider a fundamental problem in microeconomics: Selling a single item among a number of buyers whose values are drawn from known independent and regular distributions. There are four widely-used and widely-studied mechanisms in this literature: Anonymous Posted-Pricing (AP), Second-Price Auction with Anonymous Reserve (AR), Sequential Posted-Pricing (SPM), and Myerson Auction (OPT). Myerson Auction is optimal but complicated, which also suffers a few issues in practice such as fairness; AP is the simplest mechanism, but its revenue is also the lowest among these four; AR and SPM are of intermediate complexity and revenue. We study the revenue gaps among these four mechanisms, which is defined as the largest ratio between revenues from two mechanisms. We establish two tight ratios and one tighter bound: 1. SPM/AP. This ratio studies the power of discrimination in pricing schemes. We obtain the tight ratio of roughly 2. 62, closing the previous known bounds [ e /( e – 1), e ]. 2. AR/AP. This ratio studies the relative power of auction vs. pricing schemes, when no discrimination is allowed. We get the tight ratio of π 2 /6 ≈ 1. 64, closing the previous known bounds [ e /( e – 1), e ]. 3. OPT/AR. This ratio studies the power of discrimination in auctions. Previously, the revenue gap is known to be in interval [2, e ], and the lower-bound of 2 is conjectured to be tight [38, 37, 4]. We disprove this conjecture by obtaining a better lower-bound of 2. 15.

SODA Conference 2018 Conference Paper

Nearly Tight Bounds for Sandpile Transience on the Grid

• David Durfee
• Matthew Fahrbach
• Yu Gao 0001
• Tao Xiao

IROS Conference 2006 Conference Paper

A Visual Tele-operation System for the Humanoid Robot BHR-02

• Lei Zhang
• Qiang Huang
• Yuepin Lu
• Tao Xiao
• Jiapeng Yang
• Muhammad Usman Keerio

This paper presents a method to reconstruct the virtual scene of the humanoid robot teleoperation to overcome the problem of the poor vision images feedback. A virtual robot interface is built to render the data of the real robot. It has the same DOF set and the same size scale as the real robot. The interface can render the multiple real-time feedback data from the robot. In the data-fusion module an algorithm is adopted to determine the position and attitude of the robot body. Some experiments are done to confirm the effectiveness of the virtual scene

IROS Conference 2006 Conference Paper

Online Walking Pattern Generation and System Software of Humanoid BHR-2

• Zhaoqin Peng
• Yongling Fu
• Zhiyong Tang
• Qiang Huang
• Tao Xiao

This paper proposed a method of online walking pattern generation based on key parameters of off-line typical walking patterns for humanoid robot. The key parameters include hip parameters, step length, walking cycle and so on. The offline walking pattern generation method is based on ZMP. The design of the system software of humanoid BHR-2 is also described in this paper, in order to ensure the real task can be finished on time, the process of online walking patterns generation is placed outside the processor which used for real-time control. The effectiveness of the proposed method is confirmed by simulations and experiments with our developed humanoid robot with 32 DOF.