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Kanye Ye Wang

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4 papers

1 author row

AAAI Conference 2026 Conference Paper

Differentiable Semantic Meta-Learning Framework for Long-Tail Motion Forecasting in Autonomous Driving

Bin Rao
Chengyue Wang
Haicheng Liao
Qianfang Wang
Yanchen Guan
Jiaxun Zhang
Xingcheng Liu
Meixin Zhu

Long-tail motion forecasting is a core challenge for autonomous driving, where rare yet safety-critical events-such as abrupt maneuvers and dense multi-agent interactions-dominate real-world risk. Existing approaches struggle in these scenarios because they rely on either non-interpretable clustering or model-dependent error heuristics, providing neither a differentiable notion of “tailness” nor a mechanism for rapid adaptation. We propose SAML, a Semantic-Aware Meta-Learning framework that introduces the first differentiable definition of tailness for motion forecasting. SAML quantifies motion rarity via semantically meaningful intrinsic (kinematic, geometric, temporal) and interactive (local and global risk) properties, which are fused by a Bayesian Tail Perceiver into a continuous, uncertainty-aware Tail Index. This Tail Index drives a meta-memory adaptation module that couples a dynamic prototype memory with an MAML-based cognitive set mechanism, enabling fast adaptation to rare or evolving patterns. Experiments on nuScenes, NGSIM, and HighD show that SAML achieves state-of-the-art overall accuracy and substantial gains on top 1-5% worst-case events, while maintaining high efficiency. Our findings highlight semantic meta-learning as a pathway toward robust and safety-critical motion forecasting.

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TCS Journal 2025 Journal Article

Optimizing resource allocation: An active learning approach to iterative combinatorial auctions

Benjamin Estermann
Stefan Kramer
Roger Wattenhofer
Kanye Ye Wang

In deep learning-based iterative combinatorial auctions (DL-ICA), bidders are not required to report valuations for all bundles upfront. Instead, DL-ICA iteratively requests bidders to report their values for specific bundles and determines item allocation using a winner determination problem, with bidder profiles modeled by neural networks. However, due to the limited number of reported bundles, DL-ICA may not always achieve optimal winner allocation, leading to reduced economic efficiency. In this work, we enhance the economic efficiency, specifically the social welfare, of DL-ICA by optimizing the underlying machine learning-based elicitation algorithm. We introduce two novel active learning-based initial sampling strategies: GALI and GALO. GALI ensures optimal coverage of the entire bundle space during sampling, while GALO identifies bundles with high diversity in bidders' estimated values as determined by the neural network. This approach extends the application of active learning beyond small pool sizes. We demonstrate how linear programs can be utilized for active learning to manage pool sizes exceeding 1030 samples. Our approach is theoretically validated and experimentally verified, showcasing significant improvements in performance.

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IJCAI Conference 2025 Conference Paper

Zero-shot Federated Unlearning via Transforming from Data-Dependent to Personalized Model-Centric

Wenhan Wu
Huanghuang Liang
Jingling Yuan
Jiawei Jiang
Kanye Ye Wang
Chuang Hu
Xiaobo Zhou
Dazhao Cheng

Federated Unlearning (FU) addresses the "right to be forgotten" in federated learning by removing specific client data's contribution without retraining from scratch. Existing FUs are data-dependent, which make the assumption that systems can access original training data or stored historical parameter updates during unlearning. However, the assumption cannot always hold in practice, as users usually request the deletion of client data and historical parameter updates due to privacy concerns or storage limitations. Therefore, it is crucial to develop a zero-shot FU method without such data access. The key challenge is how to distinguish and remove the impact of target clients without data-level information. Motivated by the idea that if we can learn client-specific personalized information from the model instead of data, FU can be model-centric and data-free, we present the first zero-shot FU framework ZeroFU. By embedding client contributions into the model during learning via condition computation, ZeroFU enables the model to possess personalized features for unlearning. The unlearning is achieved using a proposed GAN-based distillation framework that obfuscates the personalized feature of the target client. Evaluations demonstrate its effectiveness in unlearning under non-IID settings.

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TCS Journal 2024 Journal Article

The impact of core constraints on truthful bidding in combinatorial auctions

Robin Fritsch
Younjoo Lee
Adrian Meier
Kanye Ye Wang
Roger Wattenhofer

Combinatorial auctions (CAs) offer the flexibility for bidders to articulate complex preferences when competing for multiple assets. However, the behavior of bidders under different payment rules is often unclear. Our research explores the relationship between core constraints and several core-selecting payment rules. Specifically, we examine the natural and desirable property of payment rules of being non-decreasing, which ensures that bidding higher does not lead to lower payments. Earlier studies revealed that the VCG-nearest payment method – a commonly employed payment rule – fails to adhere to this principle even for single-minded CAs. We establish that when a single effective core constraint exists, the payment maintains the non-decreasing property in single-minded CAs. To identify auctions where such a constraint is present, we introduce a novel framework using conflict graphs to represent single-minded CAs and establish sufficient conditions for the existence of single effective core constraints. We proceed with an analysis of the implications on bidder behavior, demonstrating that there is no overbidding in any Nash equilibrium when considering non-decreasing core-selecting payment rules. Our study concludes by establishing the non-decreasing nature of two additional payment rules, namely the proxy and proportional payment rules, for single-minded CAs.

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