Tootfinder

Annotation-Free One-Shot Imitation Learning for Multi-Step Manipulation Tasks
Vijja Wichitwechkarn, Emlyn Williams, Charles Fox, Ruchi Choudhary
https://arxiv.org/abs/2509.24972

Annotation-Free One-Shot Imitation Learning for Multi-Step Manipulation Tasks
Recent advances in one-shot imitation learning have enabled robots to acquire new manipulation skills from a single human demonstration. While existing methods achieve strong performance on single-step tasks, they remain limited in their ability to handle long-horizon, multi-step tasks without additional model training or manual annotation. We propose a method that can be applied to this setting provided a single demonstration without additional model training or manual annotation. We evaluated…

Adaptive Federated Few-Shot Rare-Disease Diagnosis with Energy-Aware Secure Aggregation
Aueaphum Aueawatthanaphisut
https://arxiv.org/abs/2510.00976 https://

Adaptive Federated Few-Shot Rare-Disease Diagnosis with Energy-Aware Secure Aggregation
Rare-disease diagnosis remains one of the most pressing challenges in digital health, hindered by extreme data scarcity, privacy concerns, and the limited resources of edge devices. This paper proposes the Adaptive Federated Few-Shot Rare-Disease Diagnosis (AFFR) framework, which integrates three pillars: (i) few-shot federated optimization with meta-learning to generalize from limited patient samples, (ii) energy-aware client scheduling to mitigate device dropouts and ensure balanced participa…

One-shot Conditional Sampling: MMD meets Nearest Neighbors
Anirban Chatterjee, Sayantan Choudhury, Rohan Hore
https://arxiv.org/abs/2509.25507 https://arxi…

One-shot Conditional Sampling: MMD meets Nearest Neighbors
How can we generate samples from a conditional distribution that we never fully observe? This question arises across a broad range of applications in both modern machine learning and classical statistics, including image post-processing in computer vision, approximate posterior sampling in simulation-based inference, and conditional distribution modeling in complex data settings. In such settings, compared with unconditional sampling, additional feature information can be leveraged to enable mo…

VLBiMan: Vision-Language Anchored One-Shot Demonstration Enables Generalizable Robotic Bimanual Manipulation
Huayi Zhou, Kui Jia
https://arxiv.org/abs/2509.21723 https://…

VLBiMan: Vision-Language Anchored One-Shot Demonstration Enables Generalizable Robotic Bimanual Manipulation
Achieving generalizable bimanual manipulation requires systems that can learn efficiently from minimal human input while adapting to real-world uncertainties and diverse embodiments. Existing approaches face a dilemma: imitation policy learning demands extensive demonstrations to cover task variations, while modular methods often lack flexibility in dynamic scenes. We introduce VLBiMan, a framework that derives reusable skills from a single human example through task-aware decomposition, preser…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[5/8]:
- One-Embedding-Fits-All: Efficient Zero-Shot Time Series Forecasting by a Model Zoo
Hao-Nan Shi, Ting-Ji Huang, Lu Han, De-Chuan Zhan, Han-Jia Ye

Replaced article(s) found for cs.SD. https://arxiv.org/list/cs.SD/new
[1/1]:
- Noro: Noise-Robust One-shot Voice Conversion with Hidden Speaker Representation Learning
He, Song, Wang, Li, Zhang, Wang, Huang, Chng, Wu

Integrated Sensing, Communication, and Computation for Over-the-Air Federated Edge Learning
Dingzhu Wen, Sijing Xie, Xiaowen Cao, Yuanhao Cui, Jie Xu, Yuanming Shi, Shuguang Cui
https://arxiv.org/abs/2508.15185

Integrated Sensing, Communication, and Computation for Over-the-Air Federated Edge Learning
This paper studies an over-the-air federated edge learning (Air-FEEL) system with integrated sensing, communication, and computation (ISCC), in which one edge server coordinates multiple edge devices to wirelessly sense the objects and use the sensing data to collaboratively train a machine learning model for recognition tasks. In this system, over-the-air computation (AirComp) is employed to enable one-shot model aggregation from edge devices. Under this setup, we analyze the convergence behav…

MAAdvisor: Zero-Shot Index Advisor using Multi-Agent LLMs
Zhaodonghui Li, Haitao Yuan, Jiachen Shi, Hao Zhang, Yu Rong, Gao Cong
https://arxiv.org/abs/2508.16044 https://…

MAAdvisor: Zero-Shot Index Advisor using Multi-Agent LLMs
Index recommendation is one of the most important problems in database management system (DBMS) optimization. Given queries and certain index-related constraints, traditional methods rely on heuristic optimization or learning-based models to select effective indexes and improve query performance. However, heuristic optimization suffers from high computation time, and learning-based models lose generalisability due to training for different workloads and database schemas. With the recent rapid d…

Hint: hierarchical inter-frame correlation for one-shot point cloud sequence compression
Yuchen Gao, Qi Zhang
https://arxiv.org/abs/2509.14859 https://arxi…

Hint: hierarchical inter-frame correlation for one-shot point cloud sequence compression
Deep learning has demonstrated strong capability in compressing point clouds. Within this area, entropy modeling for lossless compression is widely investigated. However, most methods rely solely on parent orsibling contexts and level-wise autoregression, which suffers from decoding latency on the order of 10 to 100 seconds. We propose HINT, a method that integrates temporal and spatial correlation for sequential point cloud compression. Specifically, it first uses a two stage temporal feature …

Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models
Wenxuan Ye, Xueli An, Onur Ayan, Junfan Wang, Xueqiang Yan, Georg Carle
https://arxiv.org/abs/2508.13625

Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models
Large models, renowned for superior performance, outperform smaller ones even without billion-parameter scales. While mobile network servers have ample computational resources to support larger models than client devices, privacy constraints prevent clients from directly sharing their raw data. Federated Learning (FL) enables decentralized clients to collaboratively train a shared model by exchanging model parameters instead of transmitting raw data. Yet, it requires a uniform model architectur…

Prompt2Auto: From Motion Prompt to Automated Control via Geometry-Invariant One-Shot Gaussian Process Learning
Zewen Yang, Xiaobing Dai, Dongfa Zhang, Yu Li, Ziyang Meng, Bingkun Huang, Hamid Sadeghian, Sami Haddadin
https://arxiv.org/abs/2509.14040

Prompt2Auto: From Motion Prompt to Automated Control via Geometry-Invariant One-Shot Gaussian Process Learning
Learning from demonstration allows robots to acquire complex skills from human demonstrations, but conventional approaches often require large datasets and fail to generalize across coordinate transformations. In this paper, we propose Prompt2Auto, a geometry-invariant one-shot Gaussian process (GeoGP) learning framework that enables robots to perform human-guided automated control from a single motion prompt. A dataset-construction strategy based on coordinate transformations is introduced tha…

DACTYL: Diverse Adversarial Corpus of Texts Yielded from Large Language Models
Shantanu Thorat, Andrew Caines
https://arxiv.org/abs/2508.00619 https://arxi…

DACTYL: Diverse Adversarial Corpus of Texts Yielded from Large Language Models
Existing AIG (AI-generated) text detectors struggle in real-world settings despite succeeding in internal testing, suggesting that they may not be robust enough. We rigorously examine the machine-learning procedure to build these detectors to address this. Most current AIG text detection datasets focus on zero-shot generations, but little work has been done on few-shot or one-shot generations, where LLMs are given human texts as an example. In response, we introduce the Diverse Adversarial Corp…

PCGBandit: One-shot acceleration of transient PDE solvers via online-learned preconditioners
Mikhail Khodak, Min Ki Jung, Brian Wynne, Edmond chow, Egemen Kolemen
https://arxiv.org/abs/2509.08765

PCGBandit: One-shot acceleration of transient PDE solvers via online-learned preconditioners
Data-driven acceleration of scientific computing workflows has been a high-profile aim of machine learning (ML) for science, with numerical simulation of transient partial differential equations (PDEs) being one of the main applications. The focus thus far has been on methods that require classical simulations to train, which when combined with the data-hungriness and optimization challenges of neural networks has caused difficulties in demonstrating a convincing advantage against strong classi…

KompeteAI: Accelerated Autonomous Multi-Agent System for End-to-End Pipeline Generation for Machine Learning Problems
Stepan Kulibaba, Artem Dzhalilov, Roman Pakhomov, Oleg Svidchenko, Alexander Gasnikov, Aleksei Shpilman
https://arxiv.org/abs/2508.10177

KompeteAI: Accelerated Autonomous Multi-Agent System for End-to-End Pipeline Generation for Machine Learning Problems
Recent Large Language Model (LLM)-based AutoML systems demonstrate impressive capabilities but face significant limitations such as constrained exploration strategies and a severe execution bottleneck. Exploration is hindered by one-shot methods lacking diversity and Monte Carlo Tree Search (MCTS) approaches that fail to recombine strong partial solutions. The execution bottleneck arises from lengthy code validation cycles that stifle iterative refinement. To overcome these challenges, we intro…

DAFMSVC: One-Shot Singing Voice Conversion with Dual Attention Mechanism and Flow Matching
Wei Chen, Binzhu Sha, Dan Luo, Jing Yang, Zhuo Wang, Fan Fan, Zhiyong Wu
https://arxiv.org/abs/2508.05978

DAFMSVC: One-Shot Singing Voice Conversion with Dual Attention Mechanism and Flow Matching
Singing Voice Conversion (SVC) transfers a source singer's timbre to a target while keeping melody and lyrics. The key challenge in any-to-any SVC is adapting unseen speaker timbres to source audio without quality degradation. Existing methods either face timbre leakage or fail to achieve satisfactory timbre similarity and quality in the generated audio. To address these challenges, we propose DAFMSVC, where the self-supervised learning (SSL) features from the source audio are replaced with the…

Data Retrieval with Importance Weights for Few-Shot Imitation Learning
Amber Xie, Rahul Chand, Dorsa Sadigh, Joey Hejna
https://arxiv.org/abs/2509.01657 https://

Data Retrieval with Importance Weights for Few-Shot Imitation Learning
While large-scale robot datasets have propelled recent progress in imitation learning, learning from smaller task specific datasets remains critical for deployment in new environments and unseen tasks. One such approach to few-shot imitation learning is retrieval-based imitation learning, which extracts relevant samples from large, widely available prior datasets to augment a limited demonstration dataset. To determine the relevant data from prior datasets, retrieval-based approaches most commo…

Exploring Strategies for Personalized Radiation Therapy: Part III Identifying genetic determinants for Radiation Response with Meta Learning
Hao Peng, Yuanyuan Zhang, Steve Jiang, Robert Timmerman, John Minna
https://arxiv.org/abs/2508.08030

Exploring Strategies for Personalized Radiation Therapy: Part III Identifying genetic determinants for Radiation Response with Meta Learning
Radiation response in cancer is shaped by complex, patient specific biology, yet current treatment strategies often rely on uniform dose prescriptions without accounting for tumor heterogeneity. In this study, we introduce a meta learning framework for one-shot prediction of radiosensitivity measured by SF2 using cell line level gene expression data. Unlike the widely used Radiosensitivity Index RSI a rank-based linear model trained on a fixed 10-gene signature, our proposed meta-learned model …

Adaptive Federated Few-Shot Rare-Disease Diagnosis with Energy-Aware Secure Aggregation
Aueaphum Aueawatthanaphisut
https://arxiv.org/abs/2510.00976 https://

Adaptive Federated Few-Shot Rare-Disease Diagnosis with Energy-Aware Secure Aggregation
Rare-disease diagnosis remains one of the most pressing challenges in digital health, hindered by extreme data scarcity, privacy concerns, and the limited resources of edge devices. This paper proposes the Adaptive Federated Few-Shot Rare-Disease Diagnosis (AFFR) framework, which integrates three pillars: (i) few-shot federated optimization with meta-learning to generalize from limited patient samples, (ii) energy-aware client scheduling to mitigate device dropouts and ensure balanced participa…