Emergency Vertex Cover
本文提出了紧急顶点覆盖问题(Em-VC)，通过给定足够功率使远端顶点也能覆盖边，旨在解决如城市灾难响应等实际问题，并为此设计了多项式时间算法及近似算法。
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Selected work
Representative Papers
Decoder-Agnostic Token Merging for Vision Transformers: A Systematic Study of G2TM
研究通过Graph-Guided Token Merging（G2TM）方法减少Vision Transformers的计算成本，证明其有效性主要取决于编码器而非解码器。
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HELIOS: From midnight to noon, continuous outdoor urban scene relighting
为解决驾驶图像照明修改难题，HELIOS提出一种基于无标签真实数据的新方法，通过整合反照率条件和循环一致扩散管道等技术，实现日夜连续场景照明调整。
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focus and focus-cpt: Fast Online Changepoint Detection in R and Python
This work addresses the problem of efficient online change-point detection in both univariate and multivariate data streams by introducing a novel method grounded in the Focus algorithm family. Leveraging the generalized likelihood ratio test, the approach enables exact detection of a single change point without requiring approximations. By exploiting the relationship between candidate change-point locations and the geometric structure of the data, it achieves a computational complexity of approximately $\log(n)^d$ per iteration. Notably, this is the first method to support exponential-family models, nonparametric settings, and autoregressive data under no approximation assumptions, integrating natural exponential-family modeling, empirical cumulative distribution functions, and geometric optimization techniques. The accompanying R/Python software package substantially enhances the efficiency and applicability of change-point detection in high-dimensional streaming data.
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A Post-Processing Conformal Prediction Approach for Conditional Coverage via Pivotal Scores
This work addresses the challenge of achieving conditional coverage in conformal prediction without relying on strong structural assumptions. To this end, it proposes the PIT-CP method, which post-processes arbitrary nonconformity scores via one-dimensional conditional density estimation—using tools such as mixture density networks or conditional normalizing flows—to map them into approximately feature-independent pivotal scores. The approach requires no stringent modeling assumptions while preserving marginal coverage and the geometric structure of prediction sets, and it substantially improves conditional coverage performance. Theoretical analysis provides both deterministic and high-probability upper bounds on the conditional coverage gap, along with formal guarantees on the volume and symmetric difference of the resulting prediction sets.
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Recent publications
Latest Papers
Emergency Vertex Cover
本文提出了紧急顶点覆盖问题(Em-VC)，通过给定足够功率使远端顶点也能覆盖边，旨在解决如城市灾难响应等实际问题，并为此设计了多项式时间算法及近似算法。
0 citationsRead paper
Decoder-Agnostic Token Merging for Vision Transformers: A Systematic Study of G2TM
研究通过Graph-Guided Token Merging（G2TM）方法减少Vision Transformers的计算成本，证明其有效性主要取决于编码器而非解码器。
0 citationsRead paper
HELIOS: From midnight to noon, continuous outdoor urban scene relighting
为解决驾驶图像照明修改难题，HELIOS提出一种基于无标签真实数据的新方法，通过整合反照率条件和循环一致扩散管道等技术，实现日夜连续场景照明调整。
0 citationsRead paper
focus and focus-cpt: Fast Online Changepoint Detection in R and Python
This work addresses the problem of efficient online change-point detection in both univariate and multivariate data streams by introducing a novel method grounded in the Focus algorithm family. Leveraging the generalized likelihood ratio test, the approach enables exact detection of a single change point without requiring approximations. By exploiting the relationship between candidate change-point locations and the geometric structure of the data, it achieves a computational complexity of approximately $\log(n)^d$ per iteration. Notably, this is the first method to support exponential-family models, nonparametric settings, and autoregressive data under no approximation assumptions, integrating natural exponential-family modeling, empirical cumulative distribution functions, and geometric optimization techniques. The accompanying R/Python software package substantially enhances the efficiency and applicability of change-point detection in high-dimensional streaming data.
0 citationsRead paper
A Post-Processing Conformal Prediction Approach for Conditional Coverage via Pivotal Scores
This work addresses the challenge of achieving conditional coverage in conformal prediction without relying on strong structural assumptions. To this end, it proposes the PIT-CP method, which post-processes arbitrary nonconformity scores via one-dimensional conditional density estimation—using tools such as mixture density networks or conditional normalizing flows—to map them into approximately feature-independent pivotal scores. The approach requires no stringent modeling assumptions while preserving marginal coverage and the geometric structure of prediction sets, and it substantially improves conditional coverage performance. Theoretical analysis provides both deterministic and high-probability upper bounds on the conditional coverage gap, along with formal guarantees on the volume and symmetric difference of the resulting prediction sets.
0 citationsRead paper