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- EEG Cortical Connectivity Analysis of Working Memory Reveals Topological Reorganization in Theta and Alpha Bands113
Higher functional integration in the theta band and lower functional segregation in the alpha band in the WM task compared with the control task is found, which demonstrates the potential for using the brain network metrics as biomarkers for predicting the task performance during WM tasks.
- Bayesian Optimization Meets Bayesian Optimal Stopping71
This paper proposes to unify BO (specifically, Gaussian process-upper confi-dence bound (GP-UCB) with Bayesian optimal stopping (BO-BOS) with Bayesian optimal stopping (BO-BOS) to boost the epoch efficiency of BO.
- Modular‐level alterations of structure–function coupling in schizophrenia connectome67
It is demonstrated that altered module‐dependent SC–FC coupling may underlie abnormal brain function and clinical symptoms observed in schizophrenia and highlighted the potential for using new multimodal neuroimaging biomarkers for diagnosis and severity evaluation of schizophrenia.
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- Prompt Optimization with Human Feedback47
The problem of prompt optimization with human feedback (POHF), in which the APOHF aims to optimize the prompt for a black-box LLM using only human preference feedback, is studied.
- The effects of a mid-task break on the brain connectome in healthy participants: A resting-state functional MRI study46
Understanding of how the resting brain reorganizes following the accumulation of prolonged task is augmented, dissociable processes between the neural mechanisms of fatigue and recovery are suggested, and some of the first quantitative insights into the cognitive neuroscience of work and rest are provided.
- Sample-Then-Optimize Batch Neural Thompson Sampling34
Two algorithms based on the Thompson sampling (TS) policy named Sample-Then-Optimize Batch Neural TS (STO-BNTS) and STO -BNTS-Linear are introduced and derive regret upper bounds for their algorithms with batch evaluations, and use insights from batch BO and NTK to show that they are asymptotically no-regret under certain conditions.
- Use Your INSTINCT: INSTruction optimization for LLMs usIng Neural bandits Coupled with Transformers28
A neural bandit algorithm is adopted which replaces the GP in BO by an NN surrogate to optimize instructions for black-box LLMs and is used to propose the INSTINCT algorithm, which consistently outperforms the existing methods in different tasks.
- Stress-induced changes in modular organizations of human brain functional networks28
It is demonstrated that acute stress prompts large-scale brain-wide reconfiguration involving multiple functional modules, corresponding to several well-known functional systems underpinning cognitive control, self-referential mental processing, visual, salience processing, sensory and motor functions.
- R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games28
It is proved that by reasoning at level 2 or more and at one level higher than the other agents, the R2-B2 agent can achieve faster asymptotic convergence to no regret than that without utilizing recursive reasoning.
- Temporal efficiency evaluation and small-worldness characterization in temporal networks28
A novel generalized analytical framework for temporal networks is introduced, which enables robust evaluation of the efficiency of temporal information exchange using two new network metrics and quantitative inspection of the temporal small-worldness, and can provide further insight into dynamic changes in the network topology of various real-world systems.
- Quantum Bayesian Optimization22
This work introduces the quantum-Gaussian process-upper confidence bound (Q-GP-UCB) algorithm, which is the first BO algorithm able to achieve a regret upper bound of O(polylog T), which is significantly smaller than its regret lower bound of Omega(sqrt(T)) in the classical setting.
- Source Attribution for Large Language Model-Generated Data21
This paper shows that both problems can be solved by watermarking, i.e., by enabling an LLM to generate synthetic texts with embedded watermarks that contain information about their source(s) and achieves effective source attribution and data provenance.
- Altered intra- and inter-hemispheric functional dysconnectivity in schizophrenia21
The source of the functional dysconnection in schizophrenia is examined, providing new insights into the nature of dysconnectivity in schizophrenia, and the dissociable processes between the preserved intra-hemispheric network topology and altered inter-hemisphere functional connectivity are highlighted.
- Bayesian Optimization under Stochastic Delayed Feedback20
This paper proposes algorithms with sub-linear regret guarantees that efficiently address the dilemma of selecting new function queries while waiting for randomly delayed feedback.
- Real-Time Aligned Reward Model beyond Semantics19
R2M (Real-Time Aligned Reward Model), a novel lightweight RLHF framework that goes beyond vanilla reward models that solely depend on the semantic representations of a pretrained LLM and leverages the evolving hidden states of the policy to align with the real-time distribution shift of the policy during the RL process.
- Adaptive Batch-Wise Sample Scheduling for Direct Preference Optimization15
SamS is proposed, an efficient and effective algorithm that adaptively selects samples in each training batch based on the LLM's learning feedback to maximize the potential generalization performance, without modifying the core DPO algorithm.
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- Refining Adaptive Zeroth-Order Optimization at Ease14
This paper first shows the untapped variance reduction effect of first moment estimate on ZO gradient estimation, which improves the accuracy and stability of ZO updates, and refined the second moment estimate based on these variance-reduced gradient estimates to better capture the geometry of the optimization landscape, enabling a more effective scaling of ZO updates.
- Federated Neural Bandits14
The federated neural-upper confidence bound (FN-UCB) algorithm is introduced, which adopts a weighted combination of two UCBs: UCB a allows every agent to additionally use the observations from the other agents to accelerate exploration (without sharing raw observations); UCB b uses an NN with aggregated parameters for reward prediction in a similar way as federated averaging for supervised learning.
- On Provably Robust Meta-Bayesian Optimization13
It is proved that both meta-BO algorithms are asymptotically no-regret even when some or all previous tasks are dissimilar to the current task, and it is shown that RM-GP-UCB enjoys a better theoretical robustness than RM- GP-TS.
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- ActiveDPO: Active Direct Preference Optimization for Sample-Efficient Alignment12
This work proposes an algorithm, ActiveDPO, that uses a theoretically grounded data selection criterion for non-linear reward functions while directly leveraging the LLM itself to parameterize the reward model used for active data selection, thereby leading to more effective and efficient data collection.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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