Author name cluster

Zhe Feng

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

12 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

A Unified Approach to Submodular Maximization Under Noise

Kshipra Bhawalkar
Yang Cai
Zhe Feng
Christopher Liaw
Tao Lin

We consider the problem of maximizing a submodular function with access to a _noisy_ value oracle for the function instead of an exact value oracle. Similar to prior work, we assume that the noisy oracle is persistent in that multiple calls to the oracle for a specific set always return the same value. In this model, Hassidim and Singer (2017) design a $(1-1/e)$-approximation algorithm for monotone submodular maximization subject to a cardinality constraint, and Huang et al (2022) design a $(1-1/e)/2$-approximation algorithm for monotone submodular maximization subject to any arbitrary matroid constraint. In this paper, we design a meta-algorithm that allows us to take any "robust" algorithm for exact submodular maximization as a black box and transform it into an algorithm for the noisy setting while retaining the approximation guarantee. By using the meta-algorithm with the measured continuous greedy algorithm, we obtain a $(1-1/e)$-approximation (resp. $1/e$-approximation) for monotone (resp. non-monotone) submodular maximization subject to a matroid constraint under noise. Furthermore, by using the meta-algorithm with the double greedy algorithm, we obtain a $1/2$-approximation for unconstrained (non-monotone) submodular maximization under noise.

PDF Details

IROS Conference 2025 Conference Paper

AccidentX: A Large-Scale Multimodal BEV Dataset for Traffic Accident Analysis and Prevention

Muyang Zhang
Zhe Feng
Jinming Yang
Mingda Jia
Weiliang Meng
Wenxuan Wu
Jiguang Zhang
Xiaopeng Zhang 0001

With the rapid development and widespread application of autonomous driving technology, the accurate analysis and prevention of traffic accidents have become critical challenges. However, current traffic accident datasets are often constrained by limited scale and diversity, impeding progress in this field. To address these limitations, we introduce AccidentX, a large-scale multimodal dataset specifically curated for comprehensive traffic accident analysis and prevention. Our AccidentX comprises over 10, 000 bird’s-eye view (BEV) videos generated using the CARLA simulator, with detailed annotations covering a wide range of traffic scenarios. In comparison to existing datasets such as nuScenes, our AccidentX offers seven times more video frames and leverages Vision-Language Models (VLMs) and GPT-4o for enhanced scene understanding and decision-making. We also establish a benchmark for state-of-the-art Multimodal Large Language Models (MLLMs) on AccidentX, fostering further research and innovation within the community. AccidentX will be made available as a fully open source resource for the advancement of the autonomous driving safety algorithm community.

Details

AAMAS Conference 2025 Conference Paper

Agent-based Modeling and Simulation of Ambiguity in Catastrophe Insurance Markets

Yu Bi
Lingxiao Zhao
Jinyun Tong
Zhe Feng
Carmine Ventre

Pricing covers for catastrophes is challenging for insurers due to uncertainty in loss probabilities. This paper addresses this so-called ambiguity problem in competitive catastrophe insurance markets through three key approaches. First, it introduces ambiguity in premium pricing and capital holdings. Second, it develops an Agentbased Model simulator to mimic general insurance markets and the Lloyd’s market. Third, it applies Empirical Game-Theoretical Analysis to explore insurers’ ambiguity preferences in different markets. The study evaluates the effects of ambiguity by analyzing their impact on individual companies, differences between small and large companies, and overall market performance. Simulation results reveal that the simulator effectively captures underwriting cycles and insurers’ strategic shifts following catastrophes. In markets with equally sized insurers, competition mitigates the negative effects of ambiguity by stabilizing premiums and increasing the number of underwritten risks. In markets with varying-sized insurers, large insurers gain market power while small insurers adopt aggressive ambiguity strategies to compete. In contrast, Lloyd’s lead-follow mechanism encourages conservative ambiguity strategies and reduces bankruptcy.

PDF

AAAI Conference 2025 Conference Paper

Deviate or Not: Learning Coalition Structures with Multiple-bit Observations in Games

Yixuan Even Xu
Zhe Feng
Fei Fang

We consider the Coalition Structure Learning (CSL) problem in multi-agent systems, motivated by the existence of coalitions in many real-world systems, e.g., trading platforms and auction systems. In this problem, there is a hidden coalition structure within a set of n agents, which affects the behavior of the agents in games. Our goal is to actively design a sequence of games for the agents to play, such that observations in these games can be used to learn the hidden coalition structure. In particular, we consider the setting where in each round, we design and present a game together with a strategy profile to the agents, and receive a multiple-bit observation -- for each agent, we observe whether or not they would like to deviate from the specified strategy. We show that we can learn the coalition structure in O(log n) rounds if we are allowed to design any normal-form game, matching the information-theoretical lower bound. For practicality, we extend the result to settings where we can only choose games of a specific format, and design algorithms to learn the coalition structure in these settings. For most settings, our complexity matches the theoretical lower bound up to a constant factor.

PDF Details DOI

ICML Conference 2025 Conference Paper

Unisoma: A Unified Transformer-based Solver for Multi-Solid Systems

Shilong Tao
Zhe Feng
Haonan Sun
Zhanxing Zhu
Yunhuai Liu

Multi-solid systems are foundational to a wide range of real-world applications, yet modeling their complex interactions remains challenging. Existing deep learning methods predominantly rely on implicit modeling, where the factors influencing solid deformation are not explicitly represented but are instead indirectly learned. However, as the number of solids increases, these methods struggle to accurately capture intricate physical interactions. In this paper, we introduce a novel explicit modeling paradigm that incorporates factors influencing solid deformation through structured modules. Specifically, we present Unisoma, a unified and flexible Transformer-based model capable of handling variable numbers of solids. Unisoma directly captures physical interactions using contact modules and adaptive interaction allocation mechanism, and learns the deformation through a triplet relationship. Compared to implicit modeling techniques, explicit modeling is more well-suited for multi-solid systems with diverse coupling patterns, as it enables detailed treatment of each solid while preventing information blending and confusion. Experimentally, Unisoma achieves consistent state-of-the-art performance across seven well-established datasets and two complex multi-solid tasks. Code is avaiable at https: //github. com/therontau0054/Unisoma.

Details

NeurIPS Conference 2023 Conference Paper

Follow-ups Also Matter: Improving Contextual Bandits via Post-serving Contexts

Chaoqi Wang
Ziyu Ye
Zhe Feng
Ashwinkumar Badanidiyuru Varadaraja
Haifeng Xu

Standard contextual bandit problem assumes that all the relevant contexts are observed before the algorithm chooses an arm. This modeling paradigm, while useful, often falls short when dealing with problems in which additional valuable contexts can be observed after arm selection. For example, content recommendation platforms like Youtube, Instagram, Tiktok receive much additional features about a user's reward after the user clicks a content (e. g. , how long the user stayed, what is the user's watch speed, etc. ). To improve online learning efficiency in these applications, we study a novel contextual bandit problem with post-serving contexts and design a new algorithm, poLinUCB, that achieves tight regret under standard assumptions. Core to our technical proof is a robustified and generalized version of the well-known Elliptical Potential Lemma (EPL), which can accommodate noise in data. Such robustification is necessary for tackling our problem, though we believe it could also be of general interest. Extensive empirical tests on both synthetic and real-world datasets demonstrate the significant benefit of utilitzing post-serving contexts as well as the superior performance of our algorithm over the state-of-the-art approaches.

PDF Details

ICML Conference 2023 Conference Paper

Pairwise Ranking Losses of Click-Through Rates Prediction for Welfare Maximization in Ad Auctions

Boxiang Lyu
Zhe Feng
Zachary Robertson
Sanmi Koyejo

We study the design of loss functions for click-through rates (CTR) to optimize (social) welfare in advertising auctions. Existing works either only focus on CTR predictions without consideration of business objectives (e. g. , welfare) in auctions or assume that the distribution over the participants’ expected cost-per-impression (eCPM) is known a priori, then use various additional assumptions on the parametric form of the distribution to derive loss functions for predicting CTRs. In this work, we bring back the welfare objectives of ad auctions into CTR predictions and propose a novel weighted rankloss to train the CTR model. Compared to existing literature, our approach provides a provable guarantee on welfare but without assumptions on the eCPMs’ distribution while also avoiding the intractability of naively applying existing learning-to-rank methods. Further, we propose a theoretically justifiable technique for calibrating the losses using labels generated from a teacher network, only assuming that the teacher network has bounded $\ell_2$ generalization error. Finally, we demonstrate the advantages of the proposed loss on synthetic and real-world data.

Details

NeurIPS Conference 2022 Conference Paper

Incrementality Bidding via Reinforcement Learning under Mixed and Delayed Rewards

Ashwinkumar Badanidiyuru Varadaraja
Zhe Feng
Tianxi Li
Haifeng Xu

Incrementality, which measures the causal effect of showing an ad to a potential customer (e. g. a user in an internet platform) versus not, is a central object for advertisers in online advertising platforms. This paper investigates the problem of how an advertiser can learn to optimize the bidding sequence in an online manner \emph{without} knowing the incrementality parameters in advance. We formulate the offline version of this problem as a specially structured episodic Markov Decision Process (MDP) and then, for its online learning counterpart, propose a novel reinforcement learning (RL) algorithm with regret at most $\widetilde{O}(H^2\sqrt{T})$, which depends on the number of rounds $H$ and number of episodes $T$, but does not depend on the number of actions (i. e. , possible bids). A fundamental difference between our learning problem from standard RL problems is that the realized reward feedback from conversion incrementality is \emph{mixed} and \emph{delayed}. To handle this difficulty we propose and analyze a novel pairwise moment-matching algorithm to learn the conversion incrementality, which we believe is of independent interest.

PDF Details

AAAI Conference 2021 Conference Paper

Convergence Analysis of No-Regret Bidding Algorithms in Repeated Auctions

Zhe Feng
Guru Guruganesh
Christopher Liaw
Aranyak Mehta
Abhishek Sethi

The connection between games and no-regret algorithms has been widely studied in the literature. A fundamental result is that when all players play no-regret strategies, this produces a sequence of actions whose time-average is a coarsecorrelated equilibrium of the game. However, much less is known about equilibrium selection in the case that multiple equilibria exist. In this work, we study the convergence of no-regret bidding algorithms in auctions. Besides being of theoretical interest, bidding dynamics in auctions is an important question from a practical viewpoint as well. We study repeated game between bidders in which a single item is sold at each time step and the bidder’s value is drawn from an unknown distribution. We show that if the bidders use any mean-based learning rule then the bidders converge with high probability to the truthful pure Nash Equilibrium in a second price auction, in VCG auction in the multi-slot setting and to the Bayesian Nash equilibrium in a first price auction. We note mean-based algorithms cover a wide variety of known no-regret algorithms such as Exp3, UCB, ε-Greedy etc. Also, we analyze the convergence of the individual iterates produced by such learning algorithms, as opposed to the time-average of the sequence. Our experiments corroborate our theoretical findings and also find a similar convergence when we use other strategies such as Deep Q-Learning.

PDF Details

AAAI Conference 2020 Conference Paper

Controlling the Amount of Verbatim Copying in Abstractive Summarization

Kaiqiang Song
Bingqing Wang
Zhe Feng
Ren Liu
Fei Liu

An abstract must not change the meaning of the original text. A single most effective way to achieve that is to increase the amount of copying while still allowing for text abstraction. Human editors can usually exercise control over copying, resulting in summaries that are more extractive than abstractive, or vice versa. However, it remains poorly understood whether modern neural abstractive summarizers can provide the same ﬂexibility, i. e. , learning from single reference summaries to generate multiple summary hypotheses with varying degrees of copying. In this paper, we present a neural summarization model that, by learning from single human abstracts, can produce a broad spectrum of summaries ranging from purely extractive to highly generative ones. We frame the task of summarization as language modeling and exploit alternative mechanisms to generate summary hypotheses. Our method allows for control over copying during both training and decoding stages of a neural summarization model. Through extensive experiments we illustrate the signiﬁcance of our proposed method on controlling the amount of verbatim copying and achieve competitive results over strong baselines. Our analysis further reveals interesting and unobvious facts.

PDF Details

JBHI Journal 2020 Journal Article

Towards Domain Invariant Heart Sound Abnormality Detection Using Learnable Filterbanks

Ahmed Imtiaz Humayun
Shabnam Ghaffarzadegan
Md. Istiaq Ansari
Zhe Feng
Taufiq Hasan

Objective: Cardiac auscultation is the most practiced non-invasive and cost-effective procedure for the early diagnosis of heart diseases. While machine learning based systems can aid in automatically screening patients, the robustness of these systems is affected by numerous factors including the stethoscope/sensor, environment, and data collection protocol. This article studies the adverse effect of domain variability on heart sound abnormality detection and develops strategies to address this problem. Methods: We propose a novel Convolutional Neural Network (CNN) layer, consisting of time-convolutional (tConv) units, that emulate Finite Impulse Response (FIR) filters. The filter coefficients can be updated via backpropagation and be stacked in the front-end of the network as a learnable filterbank. Results: On publicly available multi-domain datasets, the proposed method surpasses the top-scoring systems found in the literature for heart sound abnormality detection (a binary classification task). We utilized sensitivity, specificity, F-1 score and Macc (average of sensitivity and specificity) as performance metrics. Our systems achieved relative improvements of up to 11. 84% in terms of MAcc, compared to state-of-the-art methods. Conclusion: The results demonstrate the effectiveness of the proposed learnable filterbank CNN architecture in achieving robustness towards sensor/domain variability in PCG signals. Significance: The proposed methods pave the way for deploying automated cardiac screening systems in diversified and underserved communities.

Details DOI

AAMAS Conference 2018 Conference Paper

Deep Learning for Revenue-Optimal Auctions with Budgets

Zhe Feng
Harikrishna Narasimhan
David C. Parkes

The design of revenue-maximizing auctions for settings with private budgets is a hard task. Even the single-item case is not fully understood, and there are no analytical results for optimal, dominantstrategy incentive compatible, two-item auctions. In this work, we model the rules of an auction as a neural network, and use machine learning for the automated design of optimal auctions. We extend the RegretNet framework (Dütting et al. ’17) to handle private budget constraints, as well as Bayesian incentive compatibility. We discover new auctions with high revenue for multi-unit auctions with private budgets, including problems with unit-demand bidders. For benchmarking purposes, we also demonstrate that RegretNet can obtain essentially optimal designs for simpler settings where analytical solutions are available [12, 24, 29].

PDF