Tootfinder

Vendi Information Gain for Active Learning and its Application to Ecology
Quan Nguyen, Adji Bousso Dieng
https://arxiv.org/abs/2509.10390 https://arxiv.org…

Vendi Information Gain for Active Learning and its Application to Ecology
While monitoring biodiversity through camera traps has become an important endeavor for ecological research, identifying species in the captured image data remains a major bottleneck due to limited labeling resources. Active learning -- a machine learning paradigm that selects the most informative data to label and train a predictive model -- offers a promising solution, but typically focuses on uncertainty in the individual predictions without considering uncertainty across the entire dataset.…

$\mathbf{T^3}$: Reducing Belief Deviation in Reinforcement Learning for Active Reasoning
Deyu Zou, Yongqiang Chen, Jianxiang Wang, Haochen Yang, Mufei Li, James Cheng, Pan Li, Yu Gong
https://arxiv.org/abs/2510.12264

$\mathbf{T^3}$: Reducing Belief Deviation in Reinforcement Learning for Active Reasoning
Active reasoning requires large language models (LLMs) to interact with external sources and strategically gather information to solve problems. Central to this process is belief tracking: maintaining a coherent understanding of the problem state and the missing information toward the solution. However, due to limited reasoning capabilities, LLM-based agents often suffer from belief deviation: they struggle to correctly model beliefs, lose track of problem states, and fall into uninformative or…

Deep Reinforcement Learning for Active Flow Control around a Three-Dimensional Flow-Separated Wing at Re = 1,000
R. Montal\`a, B. Font, P. Su\'arez, J. Rabault, O. Lehmkuhl, R. Vinuesa, I. Rodriguez
https://arxiv.org/abs/2509.10195

Deep Reinforcement Learning for Active Flow Control around a Three-Dimensional Flow-Separated Wing at Re = 1,000
This study explores the use of deep reinforcement learning (DRL) for active flow control (AFC) to reduce flow separation on wings at high angles of attack. Concretely, here the DRL agent controls the flow over the three-dimensional NACA0012 wing section at the Reynolds number Re = 1,000 and angle of attack AoA = 20 degrees, autonomously identifying optimal control actions through real-time flow data and a reward function focused on improving aerodynamic performance. The framework integrates the…

Unveiling Gamer Archetypes through Multi modal feature Correlations and Unsupervised Learning
Moona Kanwal, Muhammad Sami Siddiqui, Syed Anael Ali
https://arxiv.org/abs/2510.10263

Unveiling Gamer Archetypes through Multi modal feature Correlations and Unsupervised Learning
Profiling gamers provides critical insights for adaptive game design, behavioral understanding, and digital well-being. This study proposes an integrated, data-driven framework that combines psychological measures, behavioral analytics, and machine learning to reveal underlying gamer personas. A structured survey of 250 participants, including 113 active gamers, captured multidimensional behavioral, motivational, and social data. The analysis pipeline integrated feature engineering, association…

Learning-To-Measure: In-context Active Feature Acquisition
Yuta Kobayashi, Zilin Jing, Jiayu Yao, Hongseok Namkoong, Shalmali Joshi
https://arxiv.org/abs/2510.12624 https://

Learning-To-Measure: In-context Active Feature Acquisition
Active feature acquisition (AFA) is a sequential decision-making problem where the goal is to improve model performance for test instances by adaptively selecting which features to acquire. In practice, AFA methods often learn from retrospective data with systematic missingness in the features and limited task-specific labels. Most prior work addresses acquisition for a single predetermined task, limiting scalability. To address this limitation, we formalize the meta-AFA problem, where the goal…

Zero-shot Structure Learning and Planning for Autonomous Robot Navigation using Active Inference
Daria de tinguy, Tim Verbelen, Emilio Gamba, Bart Dhoedt
https://arxiv.org/abs/2510.09574

Zero-shot Structure Learning and Planning for Autonomous Robot Navigation using Active Inference
Autonomous navigation in unfamiliar environments requires robots to simultaneously explore, localise, and plan under uncertainty, without relying on predefined maps or extensive training. We present a biologically inspired, Active Inference-based framework, Active Inference MAPping and Planning (AIMAPP). This model unifies mapping, localisation, and decision-making within a single generative model. Inspired by hippocampal navigation, it uses topological reasoning, place-cell encoding, and episo…

A framework for realisable data-driven active flow control using model predictive control applied to a simplified truck wake
Alberto Solera-Rico, Carlos Sanmiguel Vila, Stefano Discetti
https://arxiv.org/abs/2510.11600

A framework for realisable data-driven active flow control using model predictive control applied to a simplified truck wake
We present an efficient and realisable active flow control framework with few non-intrusive sensors. The method builds upon data-driven, reduced-order predictive models based on Long-Short-Term Memory (LSTM) networks and efficient gradient-based Model Predictive Control (MPC). The model uses only surface-mounted pressure probes to infer the wake state, and is trained entirely offline on a dataset built with open-loop actuations, thus avoiding the complexities of online learning. Sparsification …

Discovering Flow Separation Control Strategies in 3D Wings via Deep Reinforcement Learning
R. Montal\`a, B. Font, P. Su\'arez, J. Rabault, O. Lehmkuhl, R. Vinuesa, I. Rodriguez
https://arxiv.org/abs/2509.10185

Discovering Flow Separation Control Strategies in 3D Wings via Deep Reinforcement Learning
In this work, deep reinforcement learning (DRL) is applied to active flow control (AFC) over a threedimensional SD7003 wing at a Reynolds number of Re = 60,000 and angle of attack of AoA = 14 degrees. In the uncontrolled baseline case, the flow exhibits massive separation and a fully turbulent wake. Using a GPU-accelerated CFD solver and multi-agent training, DRL discovers control strategies that enhance lift (79%), reduce drag (65%), and improve aerodynamic efficiency (408%). Flow visualizatio…

Unsupervised Active Learning via Natural Feature Progressive Framework
Yuxi Liu, Catherine Lalman, Yimin Yang
https://arxiv.org/abs/2510.04939 https://arxi…

Unsupervised Active Learning via Natural Feature Progressive Framework
The effectiveness of modern deep learning models is predicated on the availability of large-scale, human-annotated datasets, a process that is notoriously expensive and time-consuming. While Active Learning (AL) offers a strategic solution by labeling only the most informative and representative data, its iterative nature still necessitates significant human involvement. Unsupervised Active Learning (UAL) presents an alternative by shifting the annotation burden to a single, post-selection step…

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields
Rui-Yang Zhang, Henry B. Moss, Lachlan Astfalck, Edward Cripps, David S. Leslie
https://arxiv.org/abs/2509.26005

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields
We introduce a formal active learning methodology for guiding the placement of Lagrangian observers to infer time-dependent vector fields -- a key task in oceanography, marine science, and ocean engineering -- using a physics-informed spatio-temporal Gaussian process surrogate model. The majority of existing placement campaigns either follow standard `space-filling' designs or relatively ad-hoc expert opinions. A key challenge to applying principled active learning in this setting is that Lagra…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/5]:
- MedCAL-Bench: A Comprehensive Benchmark on Cold-Start Active Learning with Foundation Models for ...
Ning Zhu, Xiaochuan Ma, Shaoting Zhang, Guotai Wang

Chrysalis: A Unified System for Comparing Active Teaching and Passive Learning with AI Agents in Education
Prashanth Arun, Vinita Vader, Erya Xu, Brent McCready-Branch, Sarah Seabrook, Kyle Scholz, Ana Crisan, Igor Grossmann, Pascal Poupart
https://arxiv.org/abs/2510.05271

Chrysalis: A Unified System for Comparing Active Teaching and Passive Learning with AI Agents in Education
AI-assisted learning has seen a remarkable uptick over the last few years, mainly due to the rise in popularity of Large Language Models (LLMs). Their ability to hold long-form, natural language interactions with users makes them excellent resources for exploring school- and university-level topics in a dynamic, active manner. We compare students' experiences when interacting with an LLM companion in two capacities: tutored learning and learning-by-teaching. We do this using Chrysalis, an LLM-b…

Redefining Cost Estimation in Database Systems: The Role of Execution Plan Features and Machine Learning
Utsav Pathak, Amit Mankodi
https://arxiv.org/abs/2510.05612 https://

Redefining Cost Estimation in Database Systems: The Role of Execution Plan Features and Machine Learning
Accurate query runtime prediction is a critical component of effective query optimization in modern database systems. Traditional cost models, such as those used in PostgreSQL, rely on static heuristics that often fail to reflect actual query performance under complex and evolving workloads. This remains an active area of research, with recent work exploring machine learning techniques to replace or augment traditional cost estimators. In this paper, we present a machine learning-based framewor…

Active learning and explicit electrostatics enable accurate modeling of electrolytes
Olga Chalykh, Mikhail Polovinkin, Dmitry Korogod, Nikita Rybin, Alexander Shapeev
https://arxiv.org/abs/2510.03479

Active learning and explicit electrostatics enable accurate modeling of electrolytes
Machine learning interatomic potentials (MLIPs) offer near-ab initio accuracy with the efficiency of classical force fields, making them attractive for modeling electrolytes. Collecting a diverse training set is essential for their accuracy and reliability, and explicit treatment of strong electrostatic interactions may be necessary. In this work, we demonstrated that active learning can automatically generate diverse training sets for moment tensor potentials (MTPs), enabling reliable molecula…

From Learning to Mastery: Achieving Safe and Efficient Real-World Autonomous Driving with Human-In-The-Loop Reinforcement Learning
Li Zeqiao, Wang Yijing, Wang Haoyu, Li Zheng, Li Peng, Liu Wenfei, Zuo Zhiqiang
https://arxiv.org/abs/2510.06038

From Learning to Mastery: Achieving Safe and Efficient Real-World Autonomous Driving with Human-In-The-Loop Reinforcement Learning
Autonomous driving with reinforcement learning (RL) has significant potential. However, applying RL in real-world settings remains challenging due to the need for safe, efficient, and robust learning. Incorporating human expertise into the learning process can help overcome these challenges by reducing risky exploration and improving sample efficiency. In this work, we propose a reward-free, active human-in-the-loop learning method called Human-Guided Distributional Soft Actor-Critic (H-DSAC). …

Crosslisted article(s) found for physics.comp-ph. https://arxiv.org/list/physics.comp-ph/new
[1/1]:
- Multi-fidelity Batch Active Learning for Gaussian Process Classifiers
Murray Cutforth, Yiming Yang, Tiffany Fan, Serge Guillas, Eric Darve

Beyond Pass/Fail: The Story of Learning-Based Testing
Sheikh Md. Mushfiqur Rahman, Nasir Eisty
https://arxiv.org/abs/2510.00450 https://arxiv.org/pdf/2510.…

Beyond Pass/Fail: The Story of Learning-Based Testing
Learning-Based Testing (LBT) merges learning and testing processes to achieve both testing and behavioral adequacy. LBT utilizes active learning to infer the model of the System Under Test (SUT), enabling scalability for large and complex programs by requiring only a minimal set of initial test cases. The core principle of LBT is that the SUT's behavior can be thoroughly inferred by progressively generating test cases and subjecting the SUT to testing, thereby ensuring comprehensive testing. De…

Identifying non-equilibrium fluctuations in Intracellular Motion Using Recurrent Neural Networks
Tomas Basile, Natascha Leijnse, Malte Slot Lauridsen, Younes Farhangi Barooji, Amin Doostmohammadi, Karel Proesmans
https://arxiv.org/abs/2510.04485

Identifying non-equilibrium fluctuations in Intracellular Motion Using Recurrent Neural Networks
Distinguishing active from passive dynamics is a fundamental challenge in understanding the motion of living cells and other active matter systems. Here, we introduce a framework that combines physical modeling, analytical theory, and machine learning to identify and characterize active fluctuations from trajectory data. We train a long short-term memory (LSTM) neural network on synthetic trajectories generated from well-defined stochastic models of active particles, enabling it to classify mot…

Pilot Contamination Attacks Detection with Machine Learning for Multi-User Massive MIMO
Pedro Ivo da Cruz, Dimitri Silva, Tito Spadini, Ricardo Suyama, Murilo Bellezoni Loiola
https://arxiv.org/abs/2510.03831

Pilot Contamination Attacks Detection with Machine Learning for Multi-User Massive MIMO
Massive multiple-input multiple-output (MMIMO) is essential to modern wireless communication systems, like 5G and 6G, but it is vulnerable to active eavesdropping attacks. One type of such attack is the pilot contamination attack (PCA), where a malicious user copies pilot signals from an authentic user during uplink, intentionally interfering with the base station's (BS) channel estimation accuracy. In this work, we propose to use a Decision Tree (DT) algorithm for PCA detection at the BS in a …

Replaced article(s) found for q-bio.NC. https://arxiv.org/list/q-bio.NC/new
[1/1]:
- State-space kinetic Ising model reveals task-dependent entropy flow in sparsely active nonequilib...
Ken Ishihara, Hideaki Shimazaki
https://arxiv.org/abs/2502.15440 https://mastoxiv.page/@arXiv_qbioNC_bot/114057779012161849
- Mechanisms for anesthesia, unawareness, respiratory depression, memory replay and sleep: MHb > IP...
Karin Vadovi\v{c}ov\'a
https://arxiv.org/abs/2509.04454 https://mastoxiv.page/@arXiv_qbioNC_bot/115167812677714466
- Meta-learning three-factor plasticity rules for structured credit assignment with sparse feedback
Dimitra Maoutsa
https://arxiv.org/abs/2512.09366 https://mastoxiv.page/@arXiv_qbioNC_bot/115699940165988688
- Prefrontal scaling of reward prediction error readout gates reinforcement-derived adaptive behavi...
Sang, Huang, Zhong, Wang, Yu, Li, Feng, Wang, Chai, Menon, Wang, Fang, Wang
https://arxiv.org/abs/2512.09761 https://mastoxiv.page/@arXiv_qbioNC_bot/115700046994546552
- Proof of a perfect platonic representation hypothesis
Liu Ziyin, Isaac Chuang
https://arxiv.org/abs/2507.01098 https://mastoxiv.page/@arXiv_csLG_bot/114788750477759162
toXiv_bot_toot

An Early Exploration of Deep-Learning-Driven Prefetching for Far Memory
Yutong Huang, Zhiyuan Guo, Yiying Zhang
https://arxiv.org/abs/2510.04360 https://ar…

An Early Exploration of Deep-Learning-Driven Prefetching for Far Memory
Far-memory systems, where applications store less-active data in more energy-efficient memory media, are increasingly adopted by data centers. However, applications are bottlenecked by on-demand data fetching from far- to local-memory. We present Memix, a far-memory system that embodies a deep-learning-system co-design for efficient and accurate prefetching, minimizing on-demand far-memory accesses. One key observation is that memory accesses are shaped by both application semantics and runtime…

Active Control of Turbulent Airfoil Flows Using Adjoint-based Deep Learning
Xuemin Liu, Tom Hickling, Jonathan F. MacArt
https://arxiv.org/abs/2510.07106 https://

Active Control of Turbulent Airfoil Flows Using Adjoint-based Deep Learning
We train active neural-network flow controllers using a deep learning PDE augmentation method to optimize lift-to-drag ratios in turbulent airfoil flows at Reynolds number $5\times10^4$ and Mach number 0.4. Direct numerical simulation and large eddy simulation are employed to model compressible, unconfined flow over two- and three-dimensional semi-infinite NACA 0012 airfoils at angles of attack $α= 5^\circ$, $10^\circ$, and $15^\circ$. Control actions, implemented through a blowing/suction jet…

Beyond named methods: A typology of active learning based on classroom observation networks
Meagan Sundstrom, Justin Gambrell, Colin Green, Adrienne L. Traxler, Eric Brewe
https://arxiv.org/abs/2510.01124

Beyond named methods: A typology of active learning based on classroom observation networks
A growing number of introductory physics instructors are implementing active learning methods in their classrooms, and they are modifying the methods to fit their local instructional contexts. However, we lack a detailed framework for describing the range of what these instructor adaptations of active learning methods look like in practice. Existing studies apply structured protocols to classroom observations and report descriptive statistics, but this approach overlooks the complex nature of i…

Active-Learning Inspired Ab Initio Theory-Experiment Loop Approach for Management of Material Defects: Application to Superconducting Qubits
Sarvesh Chaudhari, Cristobal Mendez, Rushil Choudhary, Tathagata Banerjee, Maciej Olszewski, Jadrien Paustian, Jaehong Choi, Zhaslan Baraissov, Raul Hernandez, David Muller, Britton Plourde, Gregory Fuchs, Valla Fatemi, Tomas Arias

Active-Learning Inspired Ab Initio Theory-Experiment Loop Approach for Management of Material Defects: Application to Superconducting Qubits
Surface oxides are associated with two-level systems (TLSs) that degrade the performance of niobium-based superconducting quantum computing devices. To address this, we introduce a predictive framework for selecting metal capping layers that inhibit niobium oxide formation. Using DFT-calculated oxygen interstitial and vacancy energies as thermodynamic descriptors, we train a logistic regression model on a limited set of experimental outcomes to successfully predict the likelihood of oxide forma…

Multilingual Hope Speech Detection: A Comparative Study of Logistic Regression, mBERT, and XLM-RoBERTa with Active Learning
T. O. Abiola, K. D. Abiodun, O. E. Olumide, O. O. Adebanji, O. Hiram Calvo, Grigori Sidorov
https://arxiv.org/abs/2509.20315

Multilingual Hope Speech Detection: A Comparative Study of Logistic Regression, mBERT, and XLM-RoBERTa with Active Learning
Hope speech language that fosters encouragement and optimism plays a vital role in promoting positive discourse online. However, its detection remains challenging, especially in multilingual and low-resource settings. This paper presents a multilingual framework for hope speech detection using an active learning approach and transformer-based models, including mBERT and XLM-RoBERTa. Experiments were conducted on datasets in English, Spanish, German, and Urdu, including benchmark test sets from …

Developing a Sequential Deep Learning Pipeline to Model Alaskan Permafrost Thaw Under Climate Change
Addina Rahaman
https://arxiv.org/abs/2510.06258 https://

Developing a Sequential Deep Learning Pipeline to Model Alaskan Permafrost Thaw Under Climate Change
Changing climate conditions threaten the natural permafrost thaw-freeze cycle, leading to year-round soil temperatures above 0°C. In Alaska, the warming of the topmost permafrost layer, known as the active layer, signals elevated greenhouse gas release due to high carbon storage. Accurate soil temperature prediction is therefore essential for risk mitigation and stability assessment; however, many existing approaches overlook the numerous factors driving soil thermal dynamics. This study prese…

Label-frugal satellite image change detection with generative virtual exemplar learning
Hichem Sahbi
https://arxiv.org/abs/2510.06926 https://arxiv.org/pdf…

Label-frugal satellite image change detection with generative virtual exemplar learning
Change detection is a major task in remote sensing which consists in finding all the occurrences of changes in multi-temporal satellite or aerial images. The success of existing methods, and particularly deep learning ones, is tributary to the availability of hand-labeled training data that capture the acquisition conditions and the subjectivity of the user (oracle). In this paper, we devise a novel change detection algorithm, based on active learning. The main contribution of our work resides …

Machine Learning Approaches for Classifying Star-Forming Galaxies and Active Galactic Nuclei from MIGHTEE-Detected Radio Sources in the COSMOS Field
Walter Silima, Fangxia An, Mattia Vaccari, Eslam A. Hussein, S. Randriamampandry
https://arxiv.org/abs/2510.00969

Machine Learning Approaches for Classifying Star-Forming Galaxies and Active Galactic Nuclei from MIGHTEE-Detected Radio Sources in the COSMOS Field
Radio synchrotron emission originates from both massive star formation and black hole accretion, two processes that drive galaxy evolution. Efficient classification of sources dominated by either process is therefore essential for fully exploiting deep, wide-field extragalactic radio continuum surveys. In this study, we implement, optimize, and compare five widely used supervised machine-learning (ML) algorithms to classify radio sources detected in the MeerKAT International GHz Tiered Extragal…

Causal-EPIG: A Prediction-Oriented Active Learning Framework for CATE Estimation
Erdun Gao, Jake Fawkes, Dino Sejdinovic
https://arxiv.org/abs/2509.21866 https://

Causal-EPIG: A Prediction-Oriented Active Learning Framework for CATE Estimation
Estimating the Conditional Average Treatment Effect (CATE) is often constrained by the high cost of obtaining outcome measurements, making active learning essential. However, conventional active learning strategies suffer from a fundamental objective mismatch. They are designed to reduce uncertainty in model parameters or in observable factual outcomes, failing to directly target the unobservable causal quantities that are the true objects of interest. To address this misalignment, we introduce…

Calibrated Uncertainty Sampling for Active Learning
Ha Manh Bui, Iliana Maifeld-Carucci, Anqi Liu
https://arxiv.org/abs/2510.03162 https://arxiv.org/pdf/25…

Calibrated Uncertainty Sampling for Active Learning
We study the problem of actively learning a classifier with a low calibration error. One of the most popular Acquisition Functions (AFs) in pool-based Active Learning (AL) is querying by the model's uncertainty. However, we recognize that an uncalibrated uncertainty model on the unlabeled pool may significantly affect the AF effectiveness, leading to sub-optimal generalization and high calibration error on unseen data. Deep Neural Networks (DNNs) make it even worse as the model uncertainty from…

Active Learning of Symbolic Mealy Automata
Kengo Irie, Masaki Waga, Kohei Suenaga
https://arxiv.org/abs/2509.14694 https://arxiv.org/pdf/2509.14694

Active Learning of Symbolic Mealy Automata
We propose $Λ^*_M$-an active learning algorithm that learns symbolic Mealy automata, which support infinite input alphabets and multiple output characters. Each of these two features has been addressed separately in prior work. Combining these two features poses a challenge in learning the outputs corresponding to potentially infinite sets of input characters at each state. To address this challenge, we introduce the notion of essential input characters, a finite set of input characters that i…

"On this National Day for Truth and Reconciliation, which the federal government created to honour 'the children who never returned home and Survivors of residential schools, as well as their families and communities,' members of the Active History editorial collective offer suggestions on scholarship and resources they have found helpful in their own work and learning journeys."

On the National Day of Truth and Reconciliation
Ten years ago, the Truth and Reconciliation Commission (TRC) issued its final report on the history of residential schools in Canada. Mandated to “inform all Canadians about what happened in reside…

Flow Matching-Based Active Learning for Radio Map Construction with Low-Altitude UAVs
Hao Sun, Shicong Liu, Xianghao Yu, Ying Sun
https://arxiv.org/abs/2509.13822 https://

Flow Matching-Based Active Learning for Radio Map Construction with Low-Altitude UAVs
The employment of unmanned aerial vehicles (UAVs) in the lowaltitude economy necessitates precise and real-time radio maps for reliable communication and safe navigation. However, constructing such maps is hindered by the infeasibility of exhaustive measurements due to UAVs' limited flight endurance. To address this, we propose a novel active learning framework for low-altitude radio map construction based on limited measurements. First, a Plug-and-Play (PnP)-refined flow matching algorithm is …

Online Hierarchical Policy Learning using Physics Priors for Robot Navigation in Unknown Environments
Wei Han Chen, Yuchen Liu, Alexiy Buynitsky, Ahmed H. Qureshi
https://arxiv.org/abs/2510.01519

Online Hierarchical Policy Learning using Physics Priors for Robot Navigation in Unknown Environments
Robot navigation in large, complex, and unknown indoor environments is a challenging problem. The existing approaches, such as traditional sampling-based methods, struggle with resolution control and scalability, while imitation learning-based methods require a large amount of demonstration data. Active Neural Time Fields (ANTFields) have recently emerged as a promising solution by using local observations to learn cost-to-go functions without relying on demonstrations. Despite their potential,…

Improving Active Learning for Melody Estimation by Disentangling Uncertainties
Aayush Jaiswal, Parampreet Singh, Vipul Arora
https://arxiv.org/abs/2509.17375 https://

Improving Active Learning for Melody Estimation by Disentangling Uncertainties
Estimating the fundamental frequency, or melody, is a core task in Music Information Retrieval (MIR). Various studies have explored signal processing, machine learning, and deep-learning-based approaches, with a very recent focus on utilizing uncertainty in active learning settings for melody estimation. However, these approaches do not investigate the relative effectiveness of different uncertainties. In this work, we follow a framework that disentangles aleatoric and epistemic uncertainties t…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[16/17]:
- Unsupervised Active Learning via Natural Feature Progressive Framework
Yuxi Liu, Catherine Lalman, Yimin Yang

A Goal-Oriented Approach for Active Object Detection with Exploration-Exploitation Balance
Yalei Yu, Matthew Coombes, Wen-Hua Chen, Cong Sun, Myles Flanagan, Jingjing Jiang, Pramod Pashupathy, Masoud Sotoodeh-Bahraini, Peter Kinnell, Niels Lohse
https://arxiv.org/abs/2509.11467

A Goal-Oriented Approach for Active Object Detection with Exploration-Exploitation Balance
Active object detection, which aims to identify objects of interest through controlled camera movements, plays a pivotal role in real-world visual perception for autonomous robotic applications, such as manufacturing tasks (e.g., assembly operations) performed in unknown environments. A dual control for exploration and exploitation (DCEE) algorithm is presented within goal-oriented control systems to achieve efficient active object detection, leveraging active learning by incorporating variance…

Parametric Neural Amp Modeling with Active Learning
Florian Gr\"otschla, Longxiang Jiao, Luca A. Lanzend\"orfer, Roger Wattenhofer
https://arxiv.org/abs/2509.26564 htt…

Parametric Neural Amp Modeling with Active Learning
We introduce Panama, an active learning framework to train parametric guitar amp models end-to-end using a combination of an LSTM model and a WaveNet-like architecture. With \model, one can create a virtual amp by recording samples that are determined through an ensemble-based active learning strategy to minimize the amount of datapoints needed (i.e., amp knob settings). Our strategy uses gradient-based optimization to maximize the disagreement among ensemble models, in order to identify the mo…

Synthetic Prefixes to Mitigate Bias in Real-Time Neural Query Autocomplete
Adithya Rajan, Xiaoyu Liu, Prateek Verma, Vibhu Arora
https://arxiv.org/abs/2510.01574 https://…

Synthetic Prefixes to Mitigate Bias in Real-Time Neural Query Autocomplete
We introduce a data-centric approach for mitigating presentation bias in real-time neural query autocomplete systems through the use of synthetic prefixes. These prefixes are generated from complete user queries collected during regular search sessions where autocomplete was not active. This allows us to enrich the training data for learning to rank models with more diverse and less biased examples. This method addresses the inherent bias in engagement signals collected from live query autocomp…

Adapting Medical Vision Foundation Models for Volumetric Medical Image Segmentation via Active Learning and Selective Semi-supervised Fine-tuning
Jin Yang, Daniel S. Marcus, Aristeidis Sotiras
https://arxiv.org/abs/2509.10784

Adapting Medical Vision Foundation Models for Volumetric Medical Image Segmentation via Active Learning and Selective Semi-supervised Fine-tuning
Medical Vision Foundation Models (Med-VFMs) have superior capabilities of interpreting medical images due to the knowledge learned from self-supervised pre-training with extensive unannotated images. To improve their performance on adaptive downstream evaluations, especially segmentation, a few samples from target domains are selected randomly for fine-tuning them. However, there lacks works to explore the way of adapting Med-VFMs to achieve the optimal performance on target domains efficiently…

Table Detection with Active Learning
Somraj Gautam, Nachiketa Purohit, Gaurav Harit
https://arxiv.org/abs/2509.20003 https://arxiv.org/pdf/2509.20003

Table Detection with Active Learning
Efficient data annotation remains a critical challenge in machine learning, particularly for object detection tasks requiring extensive labeled data. Active learning (AL) has emerged as a promising solution to minimize annotation costs by selecting the most informative samples. While traditional AL approaches primarily rely on uncertainty-based selection, recent advances suggest that incorporating diversity-based strategies can enhance sampling efficiency in object detection tasks. Our approach…

📚 Beyond simple explanations tackle dense topics and new concepts with active engagement help for better understanding and learning
🛍️ Research products and weigh choices by asking #Gemini to extract key information specifications pros and cons from pages for informed decision making
🎙️ Natural voice interaction with spoken answers and conversation capability integrated seamlessly …

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[8/8]:
- Unsupervised Active Learning via Natural Feature Progressive Framework
Yuxi Liu, Catherine Lalman, Yimin Yang

ActiveCQ: Active Estimation of Causal Quantities
Erdun Gao, Dino Sejdinovic
https://arxiv.org/abs/2509.24293 https://arxiv.org/pdf/2509.24293

ActiveCQ: Active Estimation of Causal Quantities
Estimating causal quantities (CQs) typically requires large datasets, which can be expensive to obtain, especially when measuring individual outcomes is costly. This challenge highlights the importance of sample-efficient active learning strategies. To address the narrow focus of prior work on the conditional average treatment effect, we formalize the broader task of Actively estimating Causal Quantities (ActiveCQ) and propose a unified framework for this general problem. Built upon the insight…

Bayesian model updating via streamlined Bayesian active learning cubature
Pei-Pei Li, Chao Dang, Crist\'obal H. Acevedo, Marcos A. Valdebenito, Matthias G. R. Faes
https://arxiv.org/abs/2509.11204 …

Bayesian model updating via streamlined Bayesian active learning cubature
This paper proposes a novel Bayesian active learning method for Bayesian model updating, which is termed as "Streamlined Bayesian Active Learning Cubature" (SBALC). The core idea is to approximate the log-likelihood function using Gaussian process (GP) regression in a streamlined Bayesian active learning way. Rather than generating many samples from the posterior GP, we only use its mean and variance function to form the model evidence estimator, stopping criterion, and learning function. Speci…

Op-Fed: Opinion, Stance, and Monetary Policy Annotations on FOMC Transcripts Using Active Learning
Alisa Kanganis, Katherine A. Keith
https://arxiv.org/abs/2509.13539 https://…

Op-Fed: Opinion, Stance, and Monetary Policy Annotations on FOMC Transcripts Using Active Learning
The U.S. Federal Open Market Committee (FOMC) regularly discusses and sets monetary policy, affecting the borrowing and spending decisions of millions of people. In this work, we release Op-Fed, a dataset of 1044 human-annotated sentences and their contexts from FOMC transcripts. We faced two major technical challenges in dataset creation: imbalanced classes -- we estimate fewer than 8% of sentences express a non-neutral stance towards monetary policy -- and inter-sentence dependence -- 65% of …

A Survey of Recent Advancements in Secure Peer-to-Peer Networks
Raj Patel, Umesh Biswas, Surya Kodipaka, Will Carroll, Preston Peranich, Maxwell Young
https://arxiv.org/abs/2509.19539

A Survey of Recent Advancements in Secure Peer-to-Peer Networks
Peer-to-peer (P2P) networks are a cornerstone of modern computing, and their security is an active area of research. Many defenses with strong security guarantees have been proposed; however, the most-recent survey is over a decade old. This paper delivers an updated review of recent theoretical advances that address classic threats, such as the Sybil and routing attacks, while highlighting how emerging trends -- such as machine learning, social networks, and dynamic systems -- pose new challen…

nnFilterMatch: A Unified Semi-Supervised Learning Framework with Uncertainty-Aware Pseudo-Label Filtering for Efficient Medical Segmentation
Yi Yang
https://arxiv.org/abs/2509.19746

nnFilterMatch: A Unified Semi-Supervised Learning Framework with Uncertainty-Aware Pseudo-Label Filtering for Efficient Medical Segmentation
Semi-supervised learning (SSL) has emerged as a promising paradigm in medical image segmentation, offering competitive performance while substantially reducing the need for extensive manual annotation. When combined with active learning (AL), these strategies further minimize annotation burden by selectively incorporating the most informative samples. However, conventional SSL_AL hybrid approaches often rely on iterative and loop-based retraining cycles after each annotation round, incurring si…

High Effort, Low Gain: Fundamental Limits of Active Learning for Linear Dynamical Systems
Nicolas Chatzikiriakos, Kevin Jamieson, Andrea Iannelli
https://arxiv.org/abs/2509.11907

High Effort, Low Gain: Fundamental Limits of Active Learning for Linear Dynamical Systems
In this work, we consider the problem of identifying an unknown linear dynamical system given a finite hypothesis class. In particular, we analyze the effect of the excitation input on the sample complexity of identifying the true system with high probability. To this end, we present sample complexity lower bounds that capture the choice of the selected excitation input. The sample complexity lower bound gives rise to a system theoretic condition to determine the potential benefit of experiment…

Bayesian E(3)-Equivariant Interatomic Potential with Iterative Restratification of Many-body Message Passing
Soohaeng Yoo Willow, Tae Hyeon Park, Gi Beom Sim, Sung Wook Moon, Seung Kyu Min, D. ChangMo Yang, Hyun Woo Kim, Juho Lee, Chang Woo Myung
https://arxiv.org/abs/2510.03046

Bayesian E(3)-Equivariant Interatomic Potential with Iterative Restratification of Many-body Message Passing
Machine learning potentials (MLPs) have become essential for large-scale atomistic simulations, enabling ab initio-level accuracy with computational efficiency. However, current MLPs struggle with uncertainty quantification, limiting their reliability for active learning, calibration, and out-of-distribution (OOD) detection. We address these challenges by developing Bayesian E(3) equivariant MLPs with iterative restratification of many-body message passing. Our approach introduces the joint ene…

Adaptive randomized pivoting and volume sampling
Ethan N. Epperly
https://arxiv.org/abs/2510.02513 https://arxiv.org/pdf/2510.02513

Adaptive randomized pivoting and volume sampling
Adaptive randomized pivoting (ARP) is a recently proposed and highly effective algorithm for column subset selection. This paper reinterprets the ARP algorithm by drawing connections to the volume sampling distribution and active learning algorithms for linear regression. As consequences, this paper presents new analysis for the ARP algorithm and faster implementations using rejection sampling.

Toward Ownership Understanding of Objects: Active Question Generation with Large Language Model and Probabilistic Generative Model
Saki Hashimoto, Shoichi Hasegawa, Tomochika Ishikawa, Akira Taniguchi, Yoshinobu Hagiwara, Lotfi El Hafi, Tadahiro Taniguchi
https://arxiv.org/abs/2509.12754

Toward Ownership Understanding of Objects: Active Question Generation with Large Language Model and Probabilistic Generative Model
Robots operating in domestic and office environments must understand object ownership to correctly execute instructions such as ``Bring me my cup.'' However, ownership cannot be reliably inferred from visual features alone. To address this gap, we propose Active Ownership Learning (ActOwL), a framework that enables robots to actively generate and ask ownership-related questions to users. ActOwL employs a probabilistic generative model to select questions that maximize information gain, thereby …

Vocabuild: An Accessible Augmented Tangible Interface for Gamified Vocabulary Learning of Constructing Meaning
Siying Hu, Zhenhao Zhang
https://arxiv.org/abs/2509.11027 https://…

Vocabuild: An Accessible Augmented Tangible Interface for Gamified Vocabulary Learning of Constructing Meaning
Vocabulary acquisition in early education often relies on rote memorization and passive screen-based tools, which can fail to engage students kinesthetically and collaboratively. This paper introduces Vocabuild, an augmented tangible interface designed to transform vocabulary learning into an active, embodied, and playful experience. The system combines physical letter blocks with a projection-augmented surface. As children physically construct words with the blocks, the system provides real-ti…

Active Learning Driven Materials Discovery for Low Thermal Conductivity Rare-Earth Pyrochlore for Thermal Barrier Coatings
Amiya Chowdhury, Acacio Rincon Romero, Grazziela Figueredo, Tanvir Hussain
https://arxiv.org/abs/2511.21297

Data-Driven Two-Stage IRS-Aided Sumrate Maximization with Inexact Precoding
Hassaan Hashmi, Spyridon Pougkakiotis, Dionysis Kalogerias
https://arxiv.org/abs/2509.16776 https://

Data-Driven Two-Stage IRS-Aided Sumrate Maximization with Inexact Precoding
We propose iZoSGA, a data-driven learning algorithm for joint passive long-term intelligent reflective surface (IRS)-aided beamforming and active short-term precoding in wireless networks. iZoSGA is based on a zeroth-order stochastic quasigradient ascent methodology designed for tackling two-stage nonconvex stochastic programs with continuous uncertainty and objective functions with "black-box" terms, and where second-stage optimization is inexact. As such, iZoSGA utilizes inexact precoding ora…

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[5/7]:
- BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Sp...
Rui-Yang Zhang, Henry B. Moss, Lachlan Astfalck, Edward Cripps, David S. Leslie

Crosslisted article(s) found for physics.atm-clus. https://arxiv.org/list/physics.atm-clus/new
[1/1]:
- Active Learning for Machine Learning Driven Molecular Dynamics
Kevin Bachelor, Sanya Murdeshwar, Daniel Sabo, Razvan Marinescu

Replaced article(s) found for eess.SY. https://arxiv.org/list/eess.SY/new
[1/1]:
- Gaussian-Process-based Adaptive Tracking Control with Dynamic Active Learning for Autonomous Grou...
Krist\'of Floch, Tam\'as P\'eni, Roland T\'oth

Replaced article(s) found for q-bio.PE. https://arxiv.org/list/q-bio.PE/new
[1/1]:
- Vendi Information Gain for Active Learning and its Application to Ecology
Quan Nguyen, Adji Bousso Dieng

Machine learning approaches to seismic event classification in the Ostrava region
Marek Pecha, Michael Skotnica, Jana Ru\v{s}ajov\'a, Bohdan Rieznikov, V\'it Wandrol, Mark\'eta R\"osnerov\'a, Jarom\'ir Knejzl\'ik
https://arxiv.org/abs/2509.22574

Machine learning approaches to seismic event classification in the Ostrava region
The northeastern region of the Czech Republic is among the most seismically active areas in the country. The most frequent seismic events are mining-induced since there used to be strong mining activity in the past. However, natural tectonic events may also occur. In addition, seismic stations often record explosions in quarries in the region. Despite the cessation of mining activities, mine-induced seismic events still occur. Therefore, a rapid differentiation between tectonic and anthropogeni…

End-to-end RL Improves Dexterous Grasping Policies
Ritvik Singh, Karl Van Wyk, Pieter Abbeel, Jitendra Malik, Nathan Ratliff, Ankur Handa
https://arxiv.org/abs/2509.16434 https:…

End-to-end RL Improves Dexterous Grasping Policies
This work explores techniques to scale up image-based end-to-end learning for dexterous grasping with an arm + hand system. Unlike state-based RL, vision-based RL is much more memory inefficient, resulting in relatively low batch sizes, which is not amenable for algorithms like PPO. Nevertheless, it is still an attractive method as unlike the more commonly used techniques which distill state-based policies into vision networks, end-to-end RL can allow for emergent active vision behaviors. We id…

Transport barriers for microswimmers in unsteady flow
L. Storm, J. Qiu, K. Gustavsson, B. Mehlig
https://arxiv.org/abs/2509.16430 https://arxiv.org/pdf/250…

Transport barriers for microswimmers in unsteady flow
Certain flow structures trap active microorganisms over extended periods, impacting mortality and growth by inhibiting foraging, exploration, and predator evasion. We show that transport barriers and trapping of a microswimmer in unsteady vortex flow can be understood in terms of Lagrangian coherent structures (LCS) in phase space, taking into account both flow and swimming dynamics. These phase-space LCS constrain when and how artificial microswimmers can be trained by reinforcement learning t…