3 citations · 10 across the 20 of their papers we have counts for
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Min Generalized Sliced Gromov Wasserstein: A Scalable Path to Gromov Wasserstein
Ashkan Shahbazi, Xinran Liu, Ping He +1
We propose min Generalized Sliced Gromov--Wasserstein (min-GSGW), a sliced formulation for the Gromov--Wasserstein (GW) problem using expressive generalized slicers. The key idea i…
LUNA: Linear Universal Neural Attention with Generalization Guarantees
Ashkan Shahbazi, Ping He, Ali Abbasi +6
Scaling attention faces a critical bottleneck: the quadratic computational cost of softmax attention, which limits its application in long-sequence domains. Whil…
EMPEROR: Efficient Moment-Preserving Representation of Distributions
Xinran Liu, Shansita D. Sharma, Soheil Kolouri
We introduce EMPEROR (Efficient Moment-Preserving Representation of Distributions), a mathematically rigorous and computationally efficient framework for representing high-dimensio…
Constrained Sliced Wasserstein Embedding
Navid NaderiAlizadeh, Darian Salehi, Xinran Liu +1
Sliced Wasserstein (SW) distances offer an efficient method for comparing high-dimensional probability measures by projecting them onto multiple 1-dimensional probability distribut…
Policy Search, Retrieval, and Composition via Task Similarity in Collaborative Agentic Systems
Saptarshi Nath, Christos Peridis, Eseoghene Benjamin +7
Agentic AI aims to create systems that set their own goals, adapt proactively to change, and refine behavior through continuous experience. Recent advances suggest that, when facin…
Fused Partial Gromov-Wasserstein for Structured Objects
Yikun Bai, Shuang Wang, Huy Tran +3
Structured data, such as graphs, is vital in machine learning due to its capacity to capture complex relationships and interactions. In recent years, the Fused Gromov-Wasserstein (…
ESPFormer: Doubly-Stochastic Attention with Expected Sliced Transport Plans
Ashkan Shahbazi, Elaheh Akbari, Darian Salehi +3
While self-attention has been instrumental in the success of Transformers, it can lead to over-concentration on a few tokens during training, resulting in suboptimal information fl…
Linear Spherical Sliced Optimal Transport: A Fast Metric for Comparing Spherical Data
Xinran Liu, Yikun Bai, Rocío Díaz Martín +5
Efficient comparison of spherical probability distributions becomes important in fields such as computer vision, geosciences, and medicine. Sliced optimal transport distances, such…
Expected Sliced Transport Plans
Xinran Liu, Rocío Díaz Martín, Yikun Bai +4
The optimal transport (OT) problem has gained significant traction in modern machine learning for its ability to: (1) provide versatile metrics, such as Wasserstein distances and t…
Stereographic Spherical Sliced Wasserstein Distances
Huy Tran, Yikun Bai, Abihith Kothapalli +4
Comparing spherical probability distributions is of great interest in various fields, including geology, medical domains, computer vision, and deep representation learning. The uti…
LCOT: Linear circular optimal transport
Rocio Diaz Martin, Ivan Medri, Yikun Bai +4
The optimal transport problem for measures supported on non-Euclidean spaces has recently gained ample interest in diverse applications involving representation learning. In this p…
PT: Partial Transport Distances
Xinran Liu, Yikun Bai, Huy Tran +3
Optimal transport and its related problems, including optimal partial transport, have proven to be valuable tools in machine learning for computing meaningful distances between pro…
Sharing Lifelong Reinforcement Learning Knowledge via Modulating Masks
Saptarshi Nath, Christos Peridis, Eseoghene Ben-Iwhiwhu +5
Lifelong learning agents aim to learn multiple tasks sequentially over a lifetime. This involves the ability to exploit previous knowledge when learning new tasks and to avoid forg…
Wasserstein Task Embedding for Measuring Task Similarities
Xinran Liu, Yikun Bai, Yuzhe Lu +2
Measuring similarities between different tasks is critical in a broad spectrum of machine learning problems, including transfer, multi-task, continual, and meta-learning. Most curr…
Teaching Networks to Solve Optimization Problems
Xinran Liu, Yuzhe Lu, Ali Abbasi +3
Leveraging machine learning to facilitate the optimization process is an emerging field that holds the promise to bypass the fundamental computational bottleneck caused by classic…
SLOSH: Set LOcality Sensitive Hashing via Sliced-Wasserstein Embeddings
Yuzhe Lu, Xinran Liu, Andrea Soltoggio +1
Learning from set-structured data is an essential problem with many applications in machine learning and computer vision. This paper focuses on non-parametric and data-independent…