11488 papers
This paper introduces a black-box meta-learning approach that optimizes intrinsic rewards to enhance data efficiency and generalization in sparse-reward continuous control environments, demonstrating its effectiveness compared to extrinsic rewards and meta-learned advantage functions.
The paper introduces AuToMATo, a novel persistence-based clustering algorithm that combines ToMATo with bootstrapping to provide robust, out-of-the-box performance without parameter tuning, making it particularly effective for topological data analysis applications like Mapper.
This paper establishes that low-degree polynomial tests can distinguish between correlated sparse stochastic block models and independent Erdős-Rényi graphs if and only if the subsampling probability exceeds the minimum of Otter's constant and the Kesten-Stigum threshold, thereby identifying a sharp computational transition for detection and partial recovery.
This paper introduces the Iterative Proportional Markovian Fitting (IPMF) procedure, a unified framework that combines Iterative Markovian Fitting and Iterative Proportional Fitting to solve the Schrödinger Bridge problem with proven convergence and improved stability for applications like unpaired domain translation.
The paper proposes ANOMIX, a graph anomaly detection framework that enhances reasoning capabilities for identifying subtle boundary anomalies by synthesizing informative hard negatives through a mixup strategy that interpolates normal and abnormal subgraph representations.
This paper proposes Curriculum-enhanced Group Distributionally Robust Optimization (CeGDRO), a novel approach that strategically prioritizes hard bias-confirming and easy bias-conflicting samples to initialize model weights in an unbiased vantage point, thereby overcoming the limitations of traditional curriculum learning in subpopulation shift scenarios and achieving state-of-the-art performance across benchmark datasets.
The paper introduces FlowCLAS, a hybrid framework that enhances normalizing flows for anomaly segmentation by integrating a contrastive loss with outlier exposure to bridge the performance gap between generative and discriminative methods, achieving state-of-the-art results on multiple robotics benchmarks.
This paper introduces FSMLP, a novel time series forecasting framework that employs a Simplex-MLP layer with weights constrained to a standard simplex to theoretically reduce overfitting in channel-wise dependencies via Rademacher complexity analysis, thereby achieving superior accuracy and scalability across multiple benchmarks.
This paper addresses the lack of unified memory evaluation in Reinforcement Learning by introducing cognitive science-inspired definitions to classify agent memory types and proposing a standardized experimental methodology to ensure accurate assessment and comparison of memory capabilities.
The paper proposes LoCo-RLHF, a framework that leverages low-rank contextual modeling and a pessimistic reduced-subspace policy to effectively align large language models with heterogeneous human feedback while ensuring computational efficiency and robustness to distributional shifts.
This paper theoretically demonstrates and empirically validates that removing difficult examples, along with techniques like margin tuning and temperature scaling, enhances the generalization and downstream performance of unsupervised contrastive learning by mitigating the negative impact these examples have on the model's learning mechanism.
This paper identifies and empirically validates "preference leakage," a pervasive contamination problem where LLM judges exhibit significant bias toward student models with which they share a relationship (such as being the same model, having an inheritance link, or belonging to the same family), thereby challenging the reliability of LLM-as-a-judge evaluation paradigms.
This paper introduces MIKASA, a comprehensive benchmark suite featuring a classification framework and two distinct datasets (MIKASA-Base and MIKASA-Robo) to systematically evaluate and advance memory-enhanced reinforcement learning agents across diverse scenarios, with a specific focus on tabletop robotic manipulation.
This paper introduces the UNB StepUP-P150 dataset, a large-scale, high-resolution collection of plantar pressure data from 150 individuals across varying walking speeds and footwear conditions, designed to advance research in biometric gait recognition, biomechanics, and deep learning.
This paper introduces Implicit U-KAN 2.0, a novel medical image segmentation model that combines second-order neural ordinary differential equations (SONO) with MultiKAN layers in a two-phase encoder-decoder architecture to achieve superior performance, enhanced interpretability, and dimension-independent approximation capabilities while reducing computational costs.
This paper proposes the TS4NAP approach, which leverages medical taxonomies (ICD-10-CM and ICD-10-PCS) and graph matching to enhance the accuracy and explainability of next-activity prediction in patient treatment planning using MIMIC-IV data.
This paper presents a large-scale analysis of 326 image classification models across nine quality dimensions beyond accuracy, revealing that vision-language models, self-supervised initialization, and dataset size significantly influence model behavior, and introduces the QUBA score to holistically rank and recommend models based on specific user needs.
This paper introduces \data, a dataset of fine-grained interaction prompts, and proposes \model, a novel framework leveraging Multimodal Large Language Models for prompt decomposition, image critique, and targeted refinement to significantly enhance the generation of complex object interactions in text-to-image synthesis.
This paper introduces PinRec, a novel outcome-conditioned, multi-token generative retrieval model developed for Pinterest that successfully balances performance, diversity, and efficiency to meet industrial-scale recommendation needs and multiple business metrics.
This paper introduces Noise-to-Meaning Recursive Self-Improvement (N2M-RSI), a minimal formal model demonstrating that AI agents feeding their own outputs back as inputs can trigger unbounded internal complexity growth once a specific information-integration threshold is crossed, while remaining implementation-agnostic and scalable to agent swarms.