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Chao Lei

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5 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

DUAL: Learning Diverse Kernels for Aggregated Two-sample and Independence Testing

Zhijian Zhou
Xunye Tian
Liuhua Peng
Chao Lei
Antonin Schrab
Danica J. Sutherland
Feng Liu

To adapt kernel two-sample and independence testing to complex structured data, aggregation of multiple kernels is frequently employed to boost testing power compared to single-kernel tests. However, we observe a phenomenon that directly maximizing multiple kernel-based statistics may result in highly similar kernels that capture highly overlapping information, limiting the effectiveness of aggregation. To address this, we propose an aggregated statistic that explicitly incorporates kernel diversity based on the covariance between different kernels. Moreover, we identify a fundamental challenge: a trade-off between the diversity among kernels and the test power of individual kernels, i. e. , the selected kernels should be both effective and diverse. This motivates a testing framework with selection inference, which leverages information from the training phase to select kernels with strong individual performance from the learned diverse kernel pool. We provide rigorous theoretical statements and proofs to show the consistency on the test power and control of Type-I error, along with asymptotic analysis of the proposed statistics. Lastly, we conducted extensive empirical experiments demonstrating the superior performance of our proposed approach across various benchmarks for both two-sample and independence testing.

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AAAI Conference 2025 Conference Paper

State-Based Disassembly Planning

Chao Lei
Nir Lipovetzky
Krista A. Ehinger

It has been shown recently that physics-based simulation significantly enhances the disassembly capabilities of real-world assemblies with diverse 3D shapes and stringent motion constraints. However, the efficiency suffers when tackling intricate disassembly tasks that require numerous simulations and increased simulation time. In this work, we propose a State-Based Disassembly Planning (SBDP) approach, prioritizing physics-based simulation with translational motion over rotational motion to facilitate autonomy, reducing dependency on human input, while storing intermediate motion states to improve search scalability. We introduce two novel evaluation functions derived from new Directional Blocking Graphs (DBGs) enriched with state information to scale up the search. Our experiments show that SBDP with new evaluation functions and DBGs constraints outperforms the state-of-the-art in disassembly planning in terms of success rate and computational efficiency over benchmark datasets consisting of thousands of physically valid industrial assemblies.

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AAAI Conference 2024 Conference Paper

Generalized Planning for the Abstraction and Reasoning Corpus

Chao Lei
Nir Lipovetzky
Krista A. Ehinger

The Abstraction and Reasoning Corpus (ARC) is a general artificial intelligence benchmark that poses difficulties for pure machine learning methods due to its requirement for fluid intelligence with a focus on reasoning and abstraction. In this work, we introduce an ARC solver, Generalized Planning for Abstract Reasoning (GPAR). It casts an ARC problem as a generalized planning (GP) problem, where a solution is formalized as a planning program with pointers. We express each ARC problem using the standard Planning Domain Definition Language (PDDL) coupled with external functions representing object-centric abstractions. We show how to scale up GP solvers via domain knowledge specific to ARC in the form of restrictions over the actions model, predicates, arguments and valid structure of planning programs. Our experiments demonstrate that GPAR outperforms the state-of-the-art solvers on the object-centric tasks of the ARC, showing the effectiveness of GP and the expressiveness of PDDL to model ARC problems. The challenges provided by the ARC benchmark motivate research to advance existing GP solvers and understand new relations with other planning computational models. Code is available at github.com/you68681/GPAR.

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SoCS Conference 2023 Conference Paper

Novelty and Lifted Helpful Actions in Generalized Planning

Chao Lei
Nir Lipovetzky
Krista A. Ehinger

It has been shown recently that successful techniques in classical planning, such as goal-oriented heuristics and landmarks, can improve the ability to compute planning programs for generalized planning (GP) problems. In this work, we introduce the notion of action novelty rank, which computes novelty with respect to a planning program, and propose novelty-based generalized planning solvers, which prune a newly generated planning program if its most frequent action repetition is greater than a given bound v, implemented by novelty-based best-first search BFS(v) and its progressive variant PGP(v). Besides, we introduce lifted helpful actions in GP derived from action schemes, and propose new evaluation functions and structural program restrictions to scale up the search. Our experiments show that the new algorithms BFS(v) and PGP(v) outperform the state-of-the-art in GP over the standard generalized planning benchmarks. Practical findings on the above-mentioned methods in generalized planning are briefly discussed.

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ICAPS Conference 2021 Conference Paper

Width-Based Backward Search

Chao Lei
Nir Lipovetzky

It has been shown recently that duality mapping is a viable strategy to turn progression (forward search) into regression (backward search), but the experimental results suggest that the dual versions of standard IPC benchmarks are quite difficult to solve for heuristic search planners. We aim to study the performance of width-based planners over regression. Our experiments show that width-based search can solve dual problems efficiently when the goal state is restricted to single fluent, but it becomes challenging when the goal state contains conjunctive fluents. We then show that the backward version of best-first width-search (BFWS) with the evaluation function f5, BFWS(f5), and its polynomial variant, k-BFWS(f5), are not competitive with their forward versions, but can be orthogonal over the IPC benchmarks. Hence, we propose a front-to-end bidirectional search k-BDWS-e and its front-to-front variant by integrating forward and backward k-BFWS(f5) with the additional intersection check between expanded states whose novelty is 1 in the opposite Close list. Practical findings on the challenges of regression in classical planning are briefly discussed.

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