Tootfinder

This https://arxiv.org/abs/2505.11862 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Q-Policy: Quantum-Enhanced Policy Evaluation for Scalable Reinforcement Learning
We propose Q-Policy, a hybrid quantum-classical reinforcement learning (RL) framework that mathematically accelerates policy evaluation and optimization by exploiting quantum computing primitives. Q-Policy encodes value functions in quantum superposition, enabling simultaneous evaluation of multiple state-action pairs via amplitude encoding and quantum parallelism. We introduce a quantum-enhanced policy iteration algorithm with provable polynomial reductions in sample complexity for the evaluat…

This https://arxiv.org/abs/2204.04198 has been replaced.
link: https://scholar.google.com/scholar?q=a

Modern applications of machine learning in quantum sciences
In this book, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback control, and quantum circuits optimization. Moreover, we introduce and discuss more specialized topics such as differentiable programming, gen…

Q-learning-based Hierarchical Cooperative Local Search for Steelmaking-continuous Casting Scheduling Problem
Yang Lv, Rong Hu, Bin Qian, Jian-Bo Yang
https://arxiv.org/abs/2506.08608

Q-learning-based Hierarchical Cooperative Local Search for Steelmaking-continuous Casting Scheduling Problem
The steelmaking continuous casting scheduling problem (SCCSP) is a critical and complex challenge in modern steel production, requiring the coordinated assignment and sequencing of steel charges across multiple production stages. Efficient scheduling not only enhances productivity but also significantly reduces energy consumption. However, both traditional heuristics (e.g., two-stage local search) and recent metaheuristics often struggle to adapt to the dynamic characteristics of practical SCCS…

This https://arxiv.org/abs/2506.03780 has been replaced.
link: https://scholar.google.com/scholar?q=a

High-Dimensional Learning in Finance
Recent advances in machine learning have shown promising results for financial prediction using large, over-parameterized models. This paper provides theoretical foundations and empirical validation for understanding when and how these methods achieve predictive success. I examine three key aspects of high-dimensional learning in finance. First, I prove that within-sample standardization in Random Fourier Features implementations fundamentally alters the underlying Gaussian kernel approximation…

This https://arxiv.org/abs/2207.04285 has been replaced.
link: https://scholar.google.com/scholar?q=a

A Closer Look into Transformer-Based Code Intelligence Through Code Transformation: Challenges and Opportunities
Transformer-based models have demonstrated state-of-the-art performance in many intelligent coding tasks such as code comment generation and code completion. Previous studies show that deep learning models are sensitive to the input variations, but few studies have systematically studied the robustness of Transformer under perturbed input code. In this work, we empirically study the effect of semantic-preserving code transformation on the performance of Transformer. Specifically, 24 and 27 code…

Reinforcement Learning with Action Chunking
Qiyang Li, Zhiyuan Zhou, Sergey Levine
https://arxiv.org/abs/2507.07969 https://arxiv.org/pdf/2507.07969 https://arxiv.org/html/2507.07969
arXiv:2507.07969v1 Announce Type: new
Abstract: We present Q-chunking, a simple yet effective recipe for improving reinforcement learning (RL) algorithms for long-horizon, sparse-reward tasks. Our recipe is designed for the offline-to-online RL setting, where the goal is to leverage an offline prior dataset to maximize the sample-efficiency of online learning. Effective exploration and sample-efficient learning remain central challenges in this setting, as it is not obvious how the offline data should be utilized to acquire a good exploratory policy. Our key insight is that action chunking, a technique popularized in imitation learning where sequences of future actions are predicted rather than a single action at each timestep, can be applied to temporal difference (TD)-based RL methods to mitigate the exploration challenge. Q-chunking adopts action chunking by directly running RL in a 'chunked' action space, enabling the agent to (1) leverage temporally consistent behaviors from offline data for more effective online exploration and (2) use unbiased $n$-step backups for more stable and efficient TD learning. Our experimental results demonstrate that Q-chunking exhibits strong offline performance and online sample efficiency, outperforming prior best offline-to-online methods on a range of long-horizon, sparse-reward manipulation tasks.
toXiv_bot_toot

Replaced article(s) found for q-bio.GN. https://arxiv.org/list/q-bio.GN/new
[1/1]:
- Terrier: A Deep Learning Repeat Classifier
Robert Turnbull, Neil D. Young, Edoardo Tescari, Lee F. Skerratt, Tiffany A. Kosch

Q&A with Maggie Haberman about covering Trump's second term, how the media is separating "the signal from the noise", Trump's threats, Biden's decline, and more (Natalie Korach/Vanity Fair)
https://www.vanityfair.com/news/story/magg

Donald Trump “Wants to Demonstrate Absolute Power”: Maggie Haberman on Covering a Tumultuous Second Term
The New York Times’ stalwart Trump chronicler, who’s out this week with a new afterword to Confidence Man, sees the media learning lessons from the first go-round. “I think that people have managed to figure out and separate the signal from the noise,” she tells VF.

Q-Detection: A Quantum-Classical Hybrid Poisoning Attack Detection Method
Haoqi He, Xiaokai Lin, Jiancai Chen, Yan Xiao
https://arxiv.org/abs/2507.06262 ht…

Q-Detection: A Quantum-Classical Hybrid Poisoning Attack Detection Method
Data poisoning attacks pose significant threats to machine learning models by introducing malicious data into the training process, thereby degrading model performance or manipulating predictions. Detecting and sifting out poisoned data is an important method to prevent data poisoning attacks. Limited by classical computation frameworks, upcoming larger-scale and more complex datasets may pose difficulties for detection. We introduce the unique speedup of quantum computing for the first time in…

Regret-Optimal Q-Learning with Low Cost for Single-Agent and Federated Reinforcement Learning
Haochen Zhang, Zhong Zheng, Lingzhou Xue
https://arxiv.org/abs/2506.04626

Regret-Optimal Q-Learning with Low Cost for Single-Agent and Federated Reinforcement Learning
Motivated by real-world settings where data collection and policy deployment -- whether for a single agent or across multiple agents -- are costly, we study the problem of on-policy single-agent reinforcement learning (RL) and federated RL (FRL) with a focus on minimizing burn-in costs (the sample sizes needed to reach near-optimal regret) and policy switching or communication costs. In parallel finite-horizon episodic Markov Decision Processes (MDPs) with $S$ states and $A$ actions, existing m…

Dilution, Diffusion and Symbiosis in Spatial Prisoner's Dilemma with Reinforcement Learning
Gustavo C. Mangold, Heitor C. M. Fernandes, Mendeli H. Vainstein
https://arxiv.org/abs/2507.02211

Dilution, Diffusion and Symbiosis in Spatial Prisoner's Dilemma with Reinforcement Learning
Recent studies in the spatial prisoner's dilemma games with reinforcement learning have shown that static agents can learn to cooperate through a diverse sort of mechanisms, including noise injection, different types of learning algorithms and neighbours' payoff knowledge.In this work, using an independent multi-agent Q-learning algorithm, we study the effects of dilution and mobility in the spatial version of the prisoner's dilemma. Within this setting, different possible actions for the algor…

Deep Learning Based Antenna Selection Technique for RIS-Empowered RQSM System
Burak Ahmet Ozden, Fatih Cogen, Erdogan Aydin
https://arxiv.org/abs/2507.05071

Deep Learning Based Antenna Selection Technique for RIS-Empowered RQSM System
Reconfigurable intelligent surface (RIS) technology has attracted considerable interest due to its ability to control wireless propagation with minimal power usage. Receive quadrature spatial modulation (RQSM) scheme transmits data bits in both in-phase ($I$) and quadrature ($Q$) channels, doubling the number of active receive antenna indices and improving spectral efficiency compared to the traditional receive spatial modulation (RSM) technique. Also, capacity-optimized antenna selection (COAS…

EXPO: Stable Reinforcement Learning with Expressive Policies
Perry Dong, Qiyang Li, Dorsa Sadigh, Chelsea Finn
https://arxiv.org/abs/2507.07986 https://arxiv.org/pdf/2507.07986 https://arxiv.org/html/2507.07986
arXiv:2507.07986v1 Announce Type: new
Abstract: We study the problem of training and fine-tuning expressive policies with online reinforcement learning (RL) given an offline dataset. Training expressive policy classes with online RL present a unique challenge of stable value maximization. Unlike simpler Gaussian policies commonly used in online RL, expressive policies like diffusion and flow-matching policies are parameterized by a long denoising chain, which hinders stable gradient propagation from actions to policy parameters when optimizing against some value function. Our key insight is that we can address stable value maximization by avoiding direct optimization over value with the expressive policy and instead construct an on-the-fly RL policy to maximize Q-value. We propose Expressive Policy Optimization (EXPO), a sample-efficient online RL algorithm that utilizes an on-the-fly policy to maximize value with two parameterized policies -- a larger expressive base policy trained with a stable imitation learning objective and a light-weight Gaussian edit policy that edits the actions sampled from the base policy toward a higher value distribution. The on-the-fly policy optimizes the actions from the base policy with the learned edit policy and chooses the value maximizing action from the base and edited actions for both sampling and temporal-difference (TD) backup. Our approach yields up to 2-3x improvement in sample efficiency on average over prior methods both in the setting of fine-tuning a pretrained policy given offline data and in leveraging offline data to train online.
toXiv_bot_toot

Replaced article(s) found for q-bio.BM. https://arxiv.org/list/q-bio.BM/new
[1/1]:
- Topological Learning Prediction of Virus-like Particle Stoichiometry and Stability
Xiang Liu, Xuefei Huang, Guo-Wei Wei

Replaced article(s) found for q-fin.PM. https://arxiv.org/list/q-fin.PM/new
[1/1]:
- Evaluation of Deep Reinforcement Learning Algorithms for Portfolio Optimisation
Chung I Lu

Learning-based primal-dual optimal control of discrete-time stochastic systems with multiplicative noise
Xiushan Jiang, Weihai Zhang
https://arxiv.org/abs/2506.02613

Learning-based primal-dual optimal control of discrete-time stochastic systems with multiplicative noise
Reinforcement learning (RL) is an effective approach for solving optimal control problems without knowing the exact information of the system model. However, the classical Q-learning method, a model-free RL algorithm, has its limitations, such as lack of strict theoretical analysis and the need for artificial disturbances during implementation. This paper explores the partially model-free stochastic linear quadratic regulator (SLQR) problem for a system with multiplicative noise from the primal…

Improving Q-Learning for Real-World Control: A Case Study in Series Hybrid Agricultural Tractors
Hend Abououf, Sidra Ghayour Bhatti, Qadeer Ahmed
https://arxiv.org/abs/2508.03647

Improving Q-Learning for Real-World Control: A Case Study in Series Hybrid Agricultural Tractors
The variable and unpredictable load demands in hybrid agricultural tractors make it difficult to design optimal rule-based energy management strategies, motivating the use of adaptive, learning-based control. However, existing approaches often rely on basic fuel-based rewards and do not leverage expert demonstrations to accelerate training. In this paper, first, the performance of Q-value-based reinforcement learning algorithms is evaluated for powertrain control in a hybrid agricultural tracto…

Integrated user scheduling and beam steering in over-the-air federated learning for mobile IoT
Shengheng Liu, Ningning Fu, Zhonghao Zhang, Yongming Huang, Tony Q. S. Quek
https://arxiv.org/abs/2508.00341

Integrated user scheduling and beam steering in over-the-air federated learning for mobile IoT
The rising popularity of Internet of things (IoT) has spurred technological advancements in mobile internet and interconnected systems. While offering flexible connectivity and intelligent applications across various domains, IoT service providers must gather vast amounts of sensitive data from users, which nonetheless concomitantly raises concerns about privacy breaches. Federated learning (FL) has emerged as a promising decentralized training paradigm to tackle this challenge. This work focus…

Replaced article(s) found for q-fin.CP. https://arxiv.org/list/q-fin.CP/new
[1/1]:
- Decision by Supervised Learning with Deep Ensembles: A Practical Framework for Robust Portfolio O...
Juhyeong Kim, Sungyoon Choi, Youngbin Lee, Yejin Kim, Yongmin Choi, Yongjae Lee

A Deep Reinforcement Learning-Based TCP Congestion Control Algorithm: Design, Simulation, and Evaluation
Efe A\u{g}lamazlar, Emirhan Eken, Harun Batur Ge\c{c}ici
https://arxiv.org/abs/2508.01047

A Deep Reinforcement Learning-Based TCP Congestion Control Algorithm: Design, Simulation, and Evaluation
This paper presents a novel TCP congestion control algorithm based on Deep Reinforcement Learning. The proposed approach utilizes Deep Q-Networks to optimize the congestion window (cWnd) by observing key network parameters and taking real-time actions. The algorithm is trained and evaluated within the NS-3 network simulator using the OpenGym interface. The results demonstrate significant improvements over traditional TCP New Reno in terms of latency and throughput, with better adaptability to c…

Learning to Charge More: A Theoretical Study of Collusion by Q-Learning Agents
Cristian Chica, Yinglong Guo, Gilad Lerman
https://arxiv.org/abs/2505.22909 …

Learning to Charge More: A Theoretical Study of Collusion by Q-Learning Agents
There is growing experimental evidence that $Q$-learning agents may learn to charge supracompetitive prices. We provide the first theoretical explanation for this behavior in infinite repeated games. Firms update their pricing policies based solely on observed profits, without computing equilibrium strategies. We show that when the game admits both a one-stage Nash equilibrium price and a collusive-enabling price, and when the $Q$-function satisfies certain inequalities at the end of experiment…

This https://arxiv.org/abs/2506.00605 has been replaced.
link: https://scholar.google.com/scholar?q=a

ABCDEFGH: An Adaptation-Based Convolutional Neural Network-CycleGAN Disease-Courses Evolution Framework Using Generative Models in Health Education
With the advancement of modern medicine and the development of technologies such as MRI, CT, and cellular analysis, it has become increasingly critical for clinicians to accurately interpret various diagnostic images. However, modern medical education often faces challenges due to limited access to high-quality teaching materials, stemming from privacy concerns and a shortage of educational resources (Balogh et al., 2015). In this context, image data generated by machine learning models, partic…

Replaced article(s) found for q-bio.QM. https://arxiv.org/list/q-bio.QM/new
[1/1]:
- Machine learning-based multimodal prognostic models integrating pathology images and high-through...
Charlotte Jennings, Andrew Broad, Lucy Godson, Emily Clarke, David Westhead, Darren Treanor

This https://arxiv.org/abs/2308.03734 has been replaced.
link: https://scholar.google.com/scholar?q=a

Labeling without Seeing? Blind Annotation for Privacy-Preserving Entity Resolution
The entity resolution problem requires finding pairs across datasets that belong to different owners but refer to the same entity in the real world. To train and evaluate solutions (either rule-based or machine-learning-based) to the entity resolution problem, generating a ground truth dataset with entity pairs or clusters is needed. However, such a data annotation process involves humans as domain oracles to review the plaintext data for all candidate record pairs from different parties, which…

This https://arxiv.org/abs/2412.20925 has been replaced.
link: https://scholar.google.com/scholar?q=a

Active Learning with Variational Quantum Circuits for Quantum Process Tomography
Quantum process tomography (QPT) is a fundamental tool for fully characterizing quantum systems. It relies on querying a set of quantum states as input to the quantum process. Previous QPT methods typically employ a straightforward strategy for randomly selecting quantum states, overlooking differences in informativeness among them. In this work, we propose a general active learning (AL) framework that adaptively selects the most informative subset of quantum states for reconstruction. We desig…

Replaced article(s) found for q-bio.BM. https://arxiv.org/list/q-bio.BM/new
[1/1]:
- Hierarchical Multi-Label Contrastive Learning for Protein-Protein Interaction Prediction Across O...
Shiyi Liu, Buwen Liang, Yuetong Fang, Zixuan Jiang, Renjing Xu

Replaced article(s) found for q-bio.NC. https://arxiv.org/list/q-bio.NC/new
[1/1]:
- Advantageous and disadvantageous inequality aversion can be taught through vicarious learning of ...
Shen Zhang, Oriel FeldmanHall, S\'ebastien H\'etu, A. Ross Otto

This https://arxiv.org/abs/2502.17327 has been replaced.
link: https://scholar.google.com/scholar?q=a

AnyTop: Character Animation Diffusion with Any Topology
Generating motion for arbitrary skeletons is a longstanding challenge in computer graphics, remaining largely unexplored due to the scarcity of diverse datasets and the irregular nature of the data. In this work, we introduce AnyTop, a diffusion model that generates motions for diverse characters with distinct motion dynamics, using only their skeletal structure as input. Our work features a transformer-based denoising network, tailored for arbitrary skeleton learning, integrating topology info…

This https://arxiv.org/abs/2112.13738 has been replaced.
link: https://scholar.google.com/scholar?q=a

Evaluation of binary classifiers for asymptotically dependent and independent extremes
Machine learning classification methods usually assume that all possible classes are sufficiently present within the training set. Due to their inherent rarities, extreme events are always under-represented and classifiers tailored for predicting extremes need to be carefully designed to handle this under-representation. In this paper, we address the question of how to assess and compare classifiers with respect to their capacity to capture extreme occurrences. This is also related to the topic…

This https://arxiv.org/abs/2208.09407 has been replaced.
link: https://scholar.google.com/scholar?q=a

Learning in Stackelberg Games with Non-myopic Agents
We study Stackelberg games where a principal repeatedly interacts with a non-myopic long-lived agent, without knowing the agent's payoff function. Although learning in Stackelberg games is well-understood when the agent is myopic, dealing with non-myopic agents poses additional complications. In particular, non-myopic agents may strategize and select actions that are inferior in the present in order to mislead the principal's learning algorithm and obtain better outcomes in the future. We pro…

This https://arxiv.org/abs/2202.12453 has been replaced.
link: https://scholar.google.com/scholar?q=a

Social Learning under Platform Influence: Consensus and Persistent Disagreement
Individuals increasingly rely on social networking platforms to form opinions. However, these platforms typically aim to maximize engagement, which may not align with social good. In this paper, we introduce an opinion dynamics model where agents are connected in a social network, and update their opinions based on their neighbors' opinions and on the content shown to them by the platform. We focus on a stochastic block model with two blocks, where the initial opinions of the individuals in dif…

Can Artificial Intelligence Trade the Stock Market?
J\k{e}drzej Maskiewicz, Pawe{\l} Sakowski
https://arxiv.org/abs/2506.04658 https://

Can Artificial Intelligence Trade the Stock Market?
The paper explores the use of Deep Reinforcement Learning (DRL) in stock market trading, focusing on two algorithms: Double Deep Q-Network (DDQN) and Proximal Policy Optimization (PPO) and compares them with Buy and Hold benchmark. It evaluates these algorithms across three currency pairs, the S&P 500 index and Bitcoin, on the daily data in the period of 2019-2023. The results demonstrate DRL's effectiveness in trading and its ability to manage risk by strategically avoiding trades in unfavorab…

This https://arxiv.org/abs/2402.02160 has been replaced.
link: https://scholar.google.com/scholar?q=a

Data Poisoning for In-context Learning
In the domain of large language models (LLMs), in-context learning (ICL) has been recognized for its innovative ability to adapt to new tasks, relying on examples rather than retraining or fine-tuning. This paper delves into the critical issue of ICL's susceptibility to data poisoning attacks, an area not yet fully explored. We wonder whether ICL is vulnerable, with adversaries capable of manipulating example data to degrade model performance. To address this, we introduce ICLPoison, a speciali…

This https://arxiv.org/abs/2111.06931 has been replaced.
link: https://scholar.google.com/scholar?q=a

Kaczmarz and Gauss-Seidel Algorithms with Volume Sampling
The method of Alternating Projections (AP) is a fundamental iterative technique with applications to problems in machine learning, optimization and signal processing. Examples include the Gauss-Seidel algorithm which is used to solve large-scale regression problems and the Kaczmarz and projections onto convex sets (POCS) algorithms that are fundamental to iterative reconstruction. Progress has been made with regards to the questions of efficiency and rate of convergence in the randomized settin…

This https://arxiv.org/abs/2209.09443 has been replaced.
link: https://scholar.google.com/scholar?q=a

Cryogenic in-memory computing using magnetic topological insulators
Machine learning algorithms have been proven effective for essential quantum computation tasks such as quantum error correction and quantum control. Efficient hardware implementation of these algorithms at cryogenic temperatures is essential. Here, we utilize magnetic topological insulators as memristors (termed magnetic topological memristors) and introduce a cryogenic in-memory computing scheme based on the coexistence of the chiral edge state and the topological surface state. The memristive…

This https://arxiv.org/abs/2506.02139 has been replaced.
link: https://scholar.google.com/scholar?q=a

The Unified Cognitive Consciousness Theory for Language Models: Anchoring Semantics, Thresholds of Activation, and Emergent Reasoning
Few-shot learning in large language models (LLMs) reveals a core paradox: certain tasks generalize from just a few examples, while others demand extensive supervision. To explain this, we introduce the Unified Cognitive Consciousness Theory (UCCT), which reconceptualizes LLMs not as deficient agents, but as unconscious substrates: dense, distributed repositories of linguistic and conceptual patterns that operate without explicit semantics, intention, or goal-directed reasoning. Under this view,…

Distributed Neural Policy Gradient Algorithm for Global Convergence of Networked Multi-Agent Reinforcement Learning
Pengcheng Dai, Yuanqiu Mo, Wenwu Yu, Wei Ren
https://arxiv.org/abs/2505.24113

Distributed Neural Policy Gradient Algorithm for Global Convergence of Networked Multi-Agent Reinforcement Learning
This paper studies the networked multi-agent reinforcement learning (NMARL) problem, where the objective of agents is to collaboratively maximize the discounted average cumulative rewards. Different from the existing methods that suffer from poor expression due to linear function approximation, we propose a distributed neural policy gradient algorithm that features two innovatively designed neural networks, specifically for the approximate Q-functions and policy functions of agents. This distri…

This https://arxiv.org/abs/2505.19946 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Inverse Q-Learning Done Right: Offline Imitation Learning in $Q^π$-Realizable MDPs
We study the problem of offline imitation learning in Markov decision processes (MDPs), where the goal is to learn a well-performing policy given a dataset of state-action pairs generated by an expert policy. Complementing a recent line of work on this topic that assumes the expert belongs to a tractable class of known policies, we approach this problem from a new angle and leverage a different type of structural assumption about the environment. Specifically, for the class of linear $Q^π$-rea…

When Vision-Language Model (VLM) Meets Beam Prediction: A Multimodal Contrastive Learning Framework
Ji Wang, Bin Tang, Jian Xiao, Qimei Cui, Xingwang Li, Tony Q. S. Quek
https://arxiv.org/abs/2508.00456

When Vision-Language Model (VLM) Meets Beam Prediction: A Multimodal Contrastive Learning Framework
As the real propagation environment becomes in creasingly complex and dynamic, millimeter wave beam prediction faces huge challenges. However, the powerful cross modal representation capability of vision-language model (VLM) provides a promising approach. The traditional methods that rely on real-time channel state information (CSI) are computationally expensive and often fail to maintain accuracy in such environments. In this paper, we present a VLM-driven contrastive learning based multimodal…

Replaced article(s) found for q-fin.PM. https://arxiv.org/list/q-fin.PM/new
[1/1]:
- Decision by Supervised Learning with Deep Ensembles: A Practical Framework for Robust Portfolio O...
Juhyeong Kim, Sungyoon Choi, Youngbin Lee, Yejin Kim, Yongmin Choi, Yongjae Lee

Offline Reinforcement Learning for Mobility Robustness Optimization
Pegah Alizadeh, Anastasios Giovanidis, Pradeepa Ramachandra, Vasileios Koutsoukis, Osama Arouk
https://arxiv.org/abs/2506.22793

Offline Reinforcement Learning for Mobility Robustness Optimization
In this work we revisit the Mobility Robustness Optimisation (MRO) algorithm and study the possibility of learning the optimal Cell Individual Offset tuning using offline Reinforcement Learning. Such methods make use of collected offline datasets to learn the optimal policy, without further exploration. We adapt and apply a sequence-based method called Decision Transformers as well as a value-based method called Conservative Q-Learning to learn the optimal policy for the same target reward as t…

Replaced article(s) found for q-bio.BM. https://arxiv.org/list/q-bio.BM/new
[1/1]:
- Hierarchical Multi-Label Contrastive Learning for Protein-Protein Interaction Prediction Across O...
Shiyi Liu, Buwen Liang, Yuetong Fang, Zixuan Jiang, Renjing Xu

This https://arxiv.org/abs/2205.09337 has been replaced.
link: https://scholar.google.com/scholar?q=a

Deep Learning in Business Analytics: A Clash of Expectations and Reality
Our fast-paced digital economy shaped by global competition requires increased data-driven decision-making based on artificial intelligence (AI) and machine learning (ML). The benefits of deep learning (DL) are manifold, but it comes with limitations that have, so far, interfered with widespread industry adoption. This paper explains why DL, despite its popularity, has difficulties speeding up its adoption within business analytics. It is shown that the adoption of deep learning is not only aff…

This https://arxiv.org/abs/2406.03674 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDS_…

Learning Safe Strategies for Value Maximizing Buyers in Uniform Price Auctions
We study the bidding problem in repeated uniform price multi-unit auctions from the perspective of a single value-maximizing buyer who aims to maximize their cumulative value over $T$ rounds while adhering to return-on-investment (RoI) constraints in each round. Buyers adopt $m$-uniform bidding format, where they submit $m$ bid-quantity pairs $(b_i, q_i)$ to demand $q_i$ units at bid $b_i$. We introduce safe bidding strategies as those that satisfy RoI constraints in every auction, regardless o…

This https://arxiv.org/abs/2207.06418 has been replaced.
link: https://scholar.google.com/scholar?q=a

Open High-Resolution Satellite Imagery: The WorldStrat Dataset -- With Application to Super-Resolution
Analyzing the planet at scale with satellite imagery and machine learning is a dream that has been constantly hindered by the cost of difficult-to-access highly-representative high-resolution imagery. To remediate this, we introduce here the WorldStrat dataset. The largest and most varied such publicly available dataset, at Airbus SPOT 6/7 satellites' high resolution of up to 1.5 m/pixel, empowered by European Space Agency's Phi-Lab as part of the ESA-funded QueryPlanet project, we curate nearl…

Replaced article(s) found for q-fin.GN. https://arxiv.org/list/q-fin.GN/new
[1/1]:
- Machine Learning Classification and Portfolio Allocation: with Implications from Machine Uncertainty
Yang Bai, Kuntara Pukthuanthong

Replaced article(s) found for q-fin.CP. https://arxiv.org/list/q-fin.CP/new
[1/1]:
- Decision by Supervised Learning with Deep Ensembles: A Practical Framework for Robust Portfolio O...
Juhyeong Kim, Sungyoon Choi, Youngbin Lee, Yejin Kim, Yongmin Choi, Yongjae Lee

Replaced article(s) found for q-fin.ST. https://arxiv.org/list/q-fin.ST/new
[1/1]:
- Year-over-Year Developments in Financial Fraud Detection via Deep Learning: A Systematic Literatu...
Yisong Chen, Chuqing Zhao, Yixin Xu, Chuanhao Nie, Yixin Zhang

Replaced article(s) found for q-bio.NC. https://arxiv.org/list/q-bio.NC/new
[1/1]:
- Inverse Reinforcement Learning via Convex Optimization
Hao Zhu, Yuan Zhang, Joschka Boedecker

Replaced article(s) found for q-fin.PR. https://arxiv.org/list/q-fin.PR/new
[1/1]:
- Machine-learning regression methods for American-style path-dependent contracts
Matteo Gambara, Giulia Livieri, Andrea Pallavicini

This https://arxiv.org/abs/2409.19434 has been replaced.
link: https://scholar.google.com/scholar?q=a

Energy-Efficient Computation with DVFS using Deep Reinforcement Learning for Multi-Task Systems in Edge Computing
Finding an optimal energy-efficient policy that is adaptable to underlying edge devices while meeting deadlines for tasks has always been challenging. This research studies generalized systems with multi-task, multi-deadline scenarios with reinforcement learning-based DVFS for energy saving for periodic soft real-time applications on edge devices. This work addresses the limitation of previous work that models a periodic system as a single task and single-deadline scenario, which is too simplif…

This https://arxiv.org/abs/2401.17909 has been replaced.
link: https://scholar.google.com/scholar?q=a

Regularizing Fairness in Optimal Policy Learning with Distributional Targets
A decision maker typically (i) incorporates training data to learn about the relative effectiveness of treatments, and (ii) chooses an implementation mechanism that implies an ``optimal'' predicted outcome distribution according to some target functional. Nevertheless, a fairness-aware decision maker may not be satisfied achieving said optimality at the cost of being ``unfair" against a subgroup of the population, in the sense that the outcome distribution in that subgroup deviates too strongly…

Robust estimation of optimal dynamic treatment regimes with nonignorable missing covariates
Jian Sun, Bo Fu, Li Su
https://arxiv.org/abs/2506.22892 https:/…

Robust estimation of optimal dynamic treatment regimes with nonignorable missing covariates
Estimating optimal dynamic treatment regimes (DTRs) using observational data is often challenged by nonignorable missing covariates arsing from informative monitoring of patients in clinical practice. To address nonignorable missingness of pseudo-outcomes induced by nonignorable missing covariates, a weighted Q-learning approach using parametric Q-function models and a semiparametric missingness propensity model has recently been proposed. However, misspecification of parametric Q-functions at …

This https://arxiv.org/abs/2503.00140 has been replaced.
link: https://scholar.google.com/scholar?q=a

Approaching the Harm of Gradient Attacks While Only Flipping Labels
Machine learning systems deployed in distributed or federated environments are highly susceptible to adversarial manipulations, particularly availability attacks -adding imperceptible perturbations to training data, thereby rendering the trained model unavailable. Prior research in distributed machine learning has demonstrated such adversarial effects through the injection of gradients or data poisoning. In this study, we aim to enhance comprehension of the potential of weaker (and more probabl…

This https://arxiv.org/abs/2205.10538 has been replaced.
link: https://scholar.google.com/scholar?q=a

Automated machine learning: AI-driven decision making in business analytics
The realization that AI-driven decision-making is indispensable in today's fast-paced and ultra-competitive marketplace has raised interest in industrial machine learning (ML) applications significantly. The current demand for analytics experts vastly exceeds the supply. One solution to this problem is to increase the user-friendliness of ML frameworks to make them more accessible for the non-expert. Automated machine learning (AutoML) is an attempt to solve the problem of expertise by providin…

This https://arxiv.org/abs/2210.07383 has been replaced.
link: https://scholar.google.com/scholar?q=a

Notes on CSPs and Polymorphisms
These are notes from a multi-year learning seminar on the algebraic approach to Constraint Satisfaction Problems (CSPs). The main topics covered are the theory of algebraic structures with few subpowers, the theory of absorbing subalgebras and its applications to studying CSP templates which can be solved by local consistency methods, and the dichotomy theorem for conservative CSP templates. Subsections and appendices cover supplementary material.

This https://arxiv.org/abs/2211.00694 has been replaced.
link: https://scholar.google.com/scholar?q=a

What we talk about when we talk about physics problem solving
I am a second-year cognitive science major, and as a student who has completed my physical science distributive requirements, I will likely never again come across Gauss's law. So why do I feel that the time and effort I devoted to solving Gauss's law problems was worth it? Partly, I inherently enjoy the learning process and the new perspective on the physical world I have acquired by understanding electromagnetism. But I was also inspired by the ways in which physics problems train the mind in…

This https://arxiv.org/abs/2304.12647 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Learned Collusion
Q-learning can be described as an all-purpose automaton that provides estimates (Q-values) of the continuation values associated with each available action and follows the naive policy of almost always choosing the action with highest Q-value. We consider a family of automata based on Q-values, whose policy may systematically favor some actions over others, for example through a bias that favors cooperation. We look for stable equilibrium biases, easily learned under converging logit/best-respo…

Deep Learning for Gradient and BCG Artifacts Removal in EEG During Simultaneous fMRI
K. A. Shahriar, E. H. Bhuiyan, Q. Luo, M. E. H. Chowdhury, X. J. Zhou
https://arxiv.org/abs/2507.22263

Deep Learning for Gradient and BCG Artifacts Removal in EEG During Simultaneous fMRI
Simultaneous EEG-fMRI recording combines high temporal and spatial resolution for tracking neural activity. However, its usefulness is greatly limited by artifacts from magnetic resonance (MR), especially gradient artifacts (GA) and ballistocardiogram (BCG) artifacts, which interfere with the EEG signal. To address this issue, we used a denoising autoencoder (DAR), a deep learning framework designed to reduce MR-related artifacts in EEG recordings. Using paired data that includes both artifact-…

Replaced article(s) found for q-fin.PM. https://arxiv.org/list/q-fin.PM/new
[1/1]:
- Decision by Supervised Learning with Deep Ensembles: A Practical Framework for Robust Portfolio O...
Juhyeong Kim, Sungyoon Choi, Youngbin Lee, Yejin Kim, Yongmin Choi, Yongjae Lee

A Principled Path to Fitted Distributional Evaluation
Sungee Hong, Jiayi Wang, Zhengling Qi, Raymond Ka Wai Wong
https://arxiv.org/abs/2506.20048 https://

A Principled Path to Fitted Distributional Evaluation
In reinforcement learning, distributional off-policy evaluation (OPE) focuses on estimating the return distribution of a target policy using offline data collected under a different policy. This work focuses on extending the widely used fitted-Q evaluation -- developed for expectation-based reinforcement learning -- to the distributional OPE setting. We refer to this extension as fitted distributional evaluation (FDE). While only a few related approaches exist, there remains no unified framewor…

Hybrid Reward-Driven Reinforcement Learning for Efficient Quantum Circuit Synthesis
Sara Giordano, Kornikar Sen, Miguel A. Martin-Delgado
https://arxiv.org/abs/2507.16641

Hybrid Reward-Driven Reinforcement Learning for Efficient Quantum Circuit Synthesis
A reinforcement learning (RL) framework is introduced for the efficient synthesis of quantum circuits that generate specified target quantum states from a fixed initial state, addressing a central challenge in both the NISQ era and future fault-tolerant quantum computing. The approach utilizes tabular Q-learning, based on action sequences, within a discretized quantum state space, to effectively manage the exponential growth of the space dimension. The framework introduces a hybrid reward mecha…

Replaced article(s) found for q-bio.QM. https://arxiv.org/list/q-bio.QM/new
[1/1]:
DeepGDel: Deep Learning-based Gene Deletion Prediction Framework for Growth-Coupled Production in...

Replaced article(s) found for q-fin.CP. https://arxiv.org/list/q-fin.CP/new
[1/1]:
- Decision by Supervised Learning with Deep Ensembles: A Practical Framework for Robust Portfolio O...
Juhyeong Kim, Sungyoon Choi, Youngbin Lee, Yejin Kim, Yongmin Choi, Yongjae Lee

Theory-informed neural networks for particle physics
Barry M. Dillon, Michael Spannowsky
https://arxiv.org/abs/2507.13447 https://arx…

Theory-informed neural networks for particle physics
We present a theory-informed reinforcement-learning framework that recasts the combinatorial assignment of final-state particles in hadron collider events as a Markov decision process. A transformer-based Deep Q-Network, rewarded at each step by the logarithmic change in the tree-level matrix element, learns to map final-state particles to partons. Because the reward derives solely from first-principles theory, the resulting policy is label-free and fully interpretable, allowing every reconstru…

This https://arxiv.org/abs/2203.01664 has been replaced.
link: https://scholar.google.com/scholar?q=a

Tail-GAN: Learning to Simulate Tail Risk Scenarios
The estimation of loss distributions for dynamic portfolios requires the simulation of scenarios representing realistic joint dynamics of their components. We propose a novel data-driven approach for simulating realistic, high-dimensional multi-asset scenarios, focusing on accurately representing tail risk for a class of static and dynamic trading strategies. We exploit the joint elicitability property of Value-at-Risk (VaR) and Expected Shortfall (ES) to design a Generative Adversarial Network…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[1/5]:
- A finite time analysis of distributed Q-learning
Han-Dong Lim, Donghwan Lee
https:/…

Multi-Year Maintenance Planning for Large-Scale Infrastructure Systems: A Novel Network Deep Q-Learning Approach
Amir Fard, Arnold X. -X. Yuan
https://arxiv.org/abs/2507.18732 h…

Replaced article(s) found for q-bio.NC. https://arxiv.org/list/q-bio.NC/new
[1/1]:
Spatiotemporal Learning of Brain Dynamics from fMRI Using Frequency-Specific Multi-Band Attention...

Deep Reinforcement Learning in Applied Control: Challenges, Analysis, and Insights
Klinsmann Agyei, Pouria Sarhadi, Daniel Polani
https://arxiv.org/abs/2507.08196

Deep Reinforcement Learning in Applied Control: Challenges, Analysis, and Insights
Over the past decade, remarkable progress has been made in adopting deep neural networks to enhance the performance of conventional reinforcement learning. A notable milestone was the development of Deep Q-Networks (DQN), which achieved human-level performance across a range of Atari games, demonstrating the potential of deep learning to stabilise and scale reinforcement learning. Subsequently, extensions to continuous control algorithms paved the way for a new paradigm in control, one that has…

Replaced article(s) found for q-bio.GN. https://arxiv.org/list/q-bio.GN/new
[1/1]:
MLOmics: Cancer Multi-Omics Database for Machine Learning
https…

Replaced article(s) found for q-fin.PM. https://arxiv.org/list/q-fin.PM/new
[1/1]:
- Decision by Supervised Learning with Deep Ensembles: A Practical Framework for Robust Portfolio O...
Juhyeong Kim, Sungyoon Choi, Youngbin Lee, Yejin Kim, Yongmin Choi, Yongjae Lee

Replaced article(s) found for cs.MA. https://arxiv.org/list/cs.MA/new
[1/1]:
- A finite time analysis of distributed Q-learning
Han-Dong Lim, Donghwan Lee
https:/…

This https://arxiv.org/abs/2501.13881 has been replaced.
link: https://scholar.google.com/scholar?q=a

The machine learning platform for developers of large systems
The machine learning system in the form of Retrieval Augmented Generation (RAG) has developed steadily since about 2021. RAG could be observed as a version of the knowledge transfer. In the studied case, the large computing systems are observed as the application point of RAG, which includes large language model (LLM), as a partner for the developing team. Such an approach has advantages during the development process and further in exploitation time.

Replaced article(s) found for q-bio.BM. https://arxiv.org/list/q-bio.BM/new
[1/1]:
- JAMUN: Bridging Smoothed Molecular Dynamics and Score-Based Learning for Conformational Ensembles
Ameya Daigavane, Bodhi P. Vani, Darcy Davidson, Saeed Saremi, Joshua Rackers, Joseph Kleinhenz

Replaced article(s) found for q-fin.CP. https://arxiv.org/list/q-fin.CP/new
[1/1]:
- Machine Learning Classification and Portfolio Allocation: with Implications from Machine Uncertainty
Yang Bai, Kuntara Pukthuanthong

GCN-Driven Reinforcement Learning for Probabilistic Real-Time Guarantees in Industrial URLLC
Eman Alqudah, Ashfaq Khokhar
https://arxiv.org/abs/2506.15011 …

GCN-Driven Reinforcement Learning for Probabilistic Real-Time Guarantees in Industrial URLLC
Ensuring packet-level communication quality is vital for ultra-reliable, low-latency communications (URLLC) in large-scale industrial wireless networks. We enhance the Local Deadline Partition (LDP) algorithm by introducing a Graph Convolutional Network (GCN) integrated with a Deep Q-Network (DQN) reinforcement learning framework for improved interference coordination in multi-cell, multi-channel networks. Unlike LDP's static priorities, our approach dynamically learns link priorities based on …

Replaced article(s) found for q-bio.NC. https://arxiv.org/list/q-bio.NC/new
[1/1]:
- Summary statistics of learning link changing neural representations to behavior
Jacob A. Zavatone-Veth, Blake Bordelon, Cengiz Pehlevan

To Trust or Not to Trust: On Calibration in ML-based Resource Allocation for Wireless Networks
Rashika Raina, Nidhi Simmons, David E. Simmons, Michel Daoud Yacoub, Trung Q. Duong
https://arxiv.org/abs/2507.17494

To Trust or Not to Trust: On Calibration in ML-based Resource Allocation for Wireless Networks
In next-generation communications and networks, machine learning (ML) models are expected to deliver not only accurate predictions but also well-calibrated confidence scores that reflect the true likelihood of correct decisions. This paper studies the calibration performance of an ML-based outage predictor within a single-user, multi-resource allocation framework. We first establish key theoretical properties of this system's outage probability (OP) under perfect calibration. Importantly, we sh…

Replaced article(s) found for q-bio.QM. https://arxiv.org/list/q-bio.QM/new/
[1/1]:
DeepGDel: Deep Learning-based Gene Deletion Prediction Framework for Growth-Coupled Production in...

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/9]:
- Eau De $Q$-Network: Adaptive Distillation of Neural Networks in Deep Reinforcement Learning
Th\'eo Vincent, Tim Faust, Yogesh Tripathi, Jan Peters, Carlo D'Eramo

This https://arxiv.org/abs/2502.15084 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Algorithmic Collusion under Observed Demand Shocks
The growing reliance on AI-driven pricing algorithms has raised antitrust concerns, particularly that third-party use of nonpublic competitor data may facilitate information sharing and undermine market competition. This paper examines a setting in which firms receive demand information from a common third party and incorporate it into their pricing algorithms. Specifically, I analyze the pricing behavior of Q-learning agents when demand shocks are observable. The simulation results show that a…

Replaced article(s) found for q-fin.PM. https://arxiv.org/list/q-fin.PM/new
[1/1]:
- Machine Learning Classification and Portfolio Allocation: with Implications from Machine Uncertainty
Yang Bai, Kuntara Pukthuanthong

Replaced article(s) found for q-bio.BM. https://arxiv.org/list/q-bio.BM/new
[1/1]:
Interpretable Multimodal Learning for Tumor Protein-Metal Binding: Progress, Challenges, and Pers...

Replaced article(s) found for q-bio.GN. https://arxiv.org/list/q-bio.GN/new
[1/1]:
- On learning functions over biological sequence space: relating Gaussian process priors, regulariz...
Samantha Petti, Carlos Mart\'i-G\'omez, Justin B. Kinney, Juannan Zhou, David M. Mc…

Replaced article(s) found for math.OC. https://arxiv.org/list/math.OC/new
[1/1]:
- Stochastic Primal-Dual Q-Learning
Narim Jeong, Donghwan Lee, Niao He

Replaced article(s) found for math.OC. https://arxiv.org/list/math.OC/new
[1/1]:
- Stochastic Primal-Dual Q-Learning
Donghwan Lee, Niao He