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Shi Li

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9 papers

2 author rows

AAAI Conference 2026 Conference Paper

Where It Moves, It Matters: Referring Surgical Instrument Segmentation via Motion

Meng Wei
Kun Yuan
Shi Li
Yue Zhou
Long Bai
Nassir Navab
Hongliang Ren
Hong Joo Lee

Enabling intuitive, language-driven interaction with surgical scenes is a critical step toward intelligent operating rooms and autonomous surgical robotic assistance. However, the task of referring segmentation, localizing surgical instruments based on natural language descriptions, remains underexplored in surgical videos, with existing approaches struggling to generalize due to reliance on static visual cues and predefined instrument names. In this work, we introduce SurgRef, a novel motion-guided framework that grounds free-form language expressions in instrument motion, capturing how tools move and interact across time, rather than what they look like. This allows models to understand and segment instruments even under occlusion, ambiguity, or unfamiliar terminology. To train and evaluate SurgRef, we present Ref-IMotion, a diverse, multi-institutional video dataset with dense spatiotemporal masks and rich motion-centric expressions. SurgRef achieves state-of-the-art accuracy and generalization across surgical procedures, setting a new benchmark for robust, language-driven surgical video segmentation.

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NeurIPS Conference 2025 Conference Paper

Learning-Augmented Streaming Algorithms for Correlation Clustering

Yinhao Dong
Shan Jiang
Shi Li
Pan Peng

We study streaming algorithms for Correlation Clustering. Given a graph as an arbitrary-order stream of edges, with each edge labeled as positive or negative, the goal is to partition the vertices into disjoint clusters, such that the number of disagreements is minimized. In this paper, we give the first learning-augmented streaming algorithms for the problem on both complete and general graphs, improving the best-known space-approximation tradeoffs. Based on the works of Cambus et al. (SODA'24) and Ahn et al. (ICML'15), our algorithms use the predictions of pairwise distances between vertices provided by a predictor. For complete graphs, our algorithm achieves a better-than-$3$ approximation under good prediction quality, while using $\tilde{O}(n)$ total space. For general graphs, our algorithm achieves an $O(\log |E^-|)$ approximation under good prediction quality using $\tilde{O}(n)$ total space, improving the best-known non-learning algorithm in terms of space efficiency. Experimental results on synthetic and real-world datasets demonstrate the superiority of our proposed algorithms over their non-learning counterparts.

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IJCAI Conference 2025 Conference Paper

Logarithmic Approximations for Fair k-Set Selection

Shi Li
Chenyang Xu
Ruilong Zhang

We study the fair k-set selection problem where we aim to select k sets from a given set system such that the (weighted) occurrence times that each element appears in these k selected sets are balanced, i. e. , the maximum (weighted) occurrence times are minimized. By observing that a set system can be formulated into a bipartite graph G: =(L cup R, E), our problem is equivalent to selecting k vertices from R such that the maximum (weighted) number selected neighbors of vertices in L is minimized. The problem arises in a wide range of applications in various fields, such as machine learning, artificial intelligence, and operations research. We first prove that the problem is NP-hard even if the maximum degree Delta of the input bipartite graph is 3, and the problem is in P when Delta=2. We then show that the problem is also in P when the input set system forms a laminar family. Based on intuitive linear programming, we show that two rounding algorithms achieve O(log n/(log log n))-approximation on general bipartite graphs, and an independent rounding algorithm achieves O(log(Delta))-approximation on bipartite graphs with a maximum degree Delta. We demonstrate that our analysis is almost tight by providing a hard instance for this linear programming.

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AAAI Conference 2020 Conference Paper

Estimating Stochastic Linear Combination of Non-Linear Regressions

Di Wang
Xiangyu Guo
Chaowen Guan
Shi Li
Jinhui Xu

In this paper we study the problem of estimating stochastic linear combination of non-linear regressions, which has a close connection with many machine learning and statistical models such as non-linear regressions, the Single Index, Multi-index, Varying Coefﬁcient Index Models and Two-layer Neural Networks. Speciﬁcally, we ﬁrst show that with some mild assumptions, if the variate vector x is multivariate Gaussian, then there is an algorithm whose output vectors have 2-norm estimation errors of O( p n ) with high probability, where p is the dimension of x and n is the number of samples. Then we extend our result to the case where x is sub-Gaussian using the zero-bias transformation, which could be seen as a generalization of the classic Stein’s lemma. We also show that with some additional assumptions there is an algorithm whose output vectors have ∞-norm estimation errors of O( 1 √ p + p n ) with high probability. Finally, for both Gaussian and sub-Gaussian cases we propose a faster sub-sampling based algorithm and show that when the sub-sample sizes are large enough then the estimation errors will not be sacriﬁced by too much. Experiments for both cases support our theoretical results. To the best of our knowledge, this is the ﬁrst work that studies and provides theoretical guarantees for the stochastic linear combination of non-linear regressions model.

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JMLR Journal 2019 Journal Article

Approximation Algorithms for Stochastic Clustering

David G. Harris
Shi Li
Thomas Pensyl
Aravind Srinivasan
Khoa Trinh

We consider stochastic settings for clustering, and develop provably-good approximation algorithms for a number of these notions. These algorithms yield better approximation ratios compared to the usual deterministic clustering setting. Additionally, they offer a number of advantages including clustering which is fairer and has better long-term behavior for each user. In particular, they ensure that every user is guaranteed to get good service (on average). We also complement some of these with impossibility results. [abs] [ pdf ][ bib ] &copy JMLR 2019. ( edit, beta )

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NeurIPS Conference 2019 Conference Paper

Facility Location Problem in Differential Privacy Model Revisited

Yunus Esencayi
Marco Gaboardi
Shi Li
Di Wang

In this paper we study the facility location problem in the model of differential privacy (DP) with uniform facility cost. Specifically, we first show that under the hierarchically well-separated tree (HST) metrics and the super-set output setting that was introduced in Gupta et. al. , there is an $\epsilon$-DP algorithm that achieves an $O(\frac{1}{\epsilon})$(expected multiplicative) approximation ratio; this implies an $O(\frac{\log n}{\epsilon})$ approximation ratio for the general metric case, where $n$ is the size of the input metric. These bounds improve the best-known results given by Gupta et. al. In particular, our approximation ratio for HST-metrics is independent of $n$, and the ratio for general metrics is independent of the aspect ratio of the input metric. On the negative side, we show that the approximation ratio of any $\epsilon$-DP algorithm is lower bounded by $\Omega(\frac{1}{\sqrt{\epsilon}})$, even for instances on HST metrics with uniform facility cost, under the super-set output setting. The lower bound shows that the dependence of the approximation ratio for HST metrics on $\epsilon$ can not be removed or greatly improved. Our novel methods and techniques for both the upper and lower bound may find additional applications.

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NeurIPS Conference 2018 Conference Paper

Approximation algorithms for stochastic clustering

David Harris
Shi Li
Aravind Srinivasan
Khoa Trinh
Thomas Pensyl

We consider stochastic settings for clustering, and develop provably-good (approximation) algorithms for a number of these notions. These algorithms allow one to obtain better approximation ratios compared to the usual deterministic clustering setting. Additionally, they offer a number of advantages including providing fairer clustering and clustering which has better long-term behavior for each user. In particular, they ensure that every user is guaranteed to get good service (on average). We also complement some of these with impossibility results.

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NeurIPS Conference 2018 Conference Paper

Distributed $k$-Clustering for Data with Heavy Noise

Shi Li
Xiangyu Guo

In this paper, we consider the $k$-center/median/means clustering with outliers problems (or the $(k, z)$-center/median/means problems) in the distributed setting. Most previous distributed algorithms have their communication costs linearly depending on $z$, the number of outliers. Recently Guha et al. [10] overcame this dependence issue by considering bi-criteria approximation algorithms that output solutions with $2z$ outliers. For the case where $z$ is large, the extra $z$ outliers discarded by the algorithms might be too large, considering that the data gathering process might be costly. In this paper, we improve the number of outliers to the best possible $(1+\epsilon)z$, while maintaining the $O(1)$-approximation ratio and independence of communication cost on $z$. The problems we consider include the $(k, z)$-center problem, and $(k, z)$-median/means problems in Euclidean metrics. Implementation of the our algorithm for $(k, z)$-center shows that it outperforms many previous algorithms, both in terms of the communication cost and quality of the output solution.

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ICRA Conference 1996 Conference Paper

Optimal trajectories of open-chain mechanical systems: Explicit optimality equation with multiple shooting solution

Sushil K. Agrawal
Shi Li
Brian C. Fabien

This paper addresses the problem of finding globally optimal trajectories of multi-degree-of-freedom open-chain systems while minimizing integral cost functionals. The contributions of this paper are: (i) explicit optimality equations in terms of inertia matrix and potential energy of the system, (ii) compatability conditions on the terminal conditions of the system consistent with the cost functional, (iii) robust computational procedure for solving the two-point boundary value problem in differential-algebraic equations using multiple shooting method, and (iv) an integrated software for dynamic optimization and simulation of open-chain systems.

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