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@arXiv_csLG_bot@mastoxiv.page
2026-02-25 16:08:08

Replaced article(s) found for cs.LG. arxiv.org/list/cs.LG/new
[4/6]:
- Neural Proposals, Symbolic Guarantees: Neuro-Symbolic Graph Generation with Hard Constraints
Chuqin Geng, Li Zhang, Mark Zhang, Haolin Ye, Ziyu Zhao, Xujie Si
arxiv.org/abs/2602.16954 mastoxiv.page/@arXiv_csLG_bot/
- Multi-Probe Zero Collision Hash (MPZCH): Mitigating Embedding Collisions and Enhancing Model Fres...
Ziliang Zhao, et al.
arxiv.org/abs/2602.17050 mastoxiv.page/@arXiv_csLG_bot/
- MASPO: Unifying Gradient Utilization, Probability Mass, and Signal Reliability for Robust and Sam...
Fu, Lin, Fang, Zheng, Hu, Shao, Qin, Pan, Zeng, Cai
arxiv.org/abs/2602.17550 mastoxiv.page/@arXiv_csLG_bot/
- A Theoretical Framework for Modular Learning of Robust Generative Models
Corinna Cortes, Mehryar Mohri, Yutao Zhong
arxiv.org/abs/2602.17554 mastoxiv.page/@arXiv_csLG_bot/
- Multi-Round Human-AI Collaboration with User-Specified Requirements
Sima Noorani, Shayan Kiyani, Hamed Hassani, George Pappas
arxiv.org/abs/2602.17646 mastoxiv.page/@arXiv_csLG_bot/
- NEXUS: A compact neural architecture for high-resolution spatiotemporal air quality forecasting i...
Rampunit Kumar, Aditya Maheshwari
arxiv.org/abs/2602.19654 mastoxiv.page/@arXiv_csLG_bot/
- Augmenting Lateral Thinking in Language Models with Humor and Riddle Data for the BRAINTEASER Task
Mina Ghashami, Soumya Smruti Mishra
arxiv.org/abs/2405.10385 mastoxiv.page/@arXiv_csCL_bot/
- Watermarking Language Models with Error Correcting Codes
Patrick Chao, Yan Sun, Edgar Dobriban, Hamed Hassani
arxiv.org/abs/2406.10281 mastoxiv.page/@arXiv_csCR_bot/
- Learning to Control Unknown Strongly Monotone Games
Siddharth Chandak, Ilai Bistritz, Nicholas Bambos
arxiv.org/abs/2407.00575 mastoxiv.page/@arXiv_csMA_bot/
- Classification and reconstruction for single-pixel imaging with classical and quantum neural netw...
Sofya Manko, Dmitry Frolovtsev
arxiv.org/abs/2407.12506 mastoxiv.page/@arXiv_quantph_b
- Statistical Inference for Temporal Difference Learning with Linear Function Approximation
Weichen Wu, Gen Li, Yuting Wei, Alessandro Rinaldo
arxiv.org/abs/2410.16106 mastoxiv.page/@arXiv_statML_bo
- Big data approach to Kazhdan-Lusztig polynomials
Abel Lacabanne, Daniel Tubbenhauer, Pedro Vaz
arxiv.org/abs/2412.01283 mastoxiv.page/@arXiv_mathRT_bo
- MoEMba: A Mamba-based Mixture of Experts for High-Density EMG-based Hand Gesture Recognition
Mehran Shabanpour, Kasra Rad, Sadaf Khademi, Arash Mohammadi
arxiv.org/abs/2502.17457 mastoxiv.page/@arXiv_eessSP_bo
- Tightening Optimality gap with confidence through conformal prediction
Miao Li, Michael Klamkin, Russell Bent, Pascal Van Hentenryck
arxiv.org/abs/2503.04071 mastoxiv.page/@arXiv_statML_bo
- SEED: Towards More Accurate Semantic Evaluation for Visual Brain Decoding
Juhyeon Park, Peter Yongho Kim, Jiook Cha, Shinjae Yoo, Taesup Moon
arxiv.org/abs/2503.06437 mastoxiv.page/@arXiv_csCV_bot/
- How much does context affect the accuracy of AI health advice?
Prashant Garg, Thiemo Fetzer
arxiv.org/abs/2504.18310 mastoxiv.page/@arXiv_econGN_bo
- Reproducing and Improving CheXNet: Deep Learning for Chest X-ray Disease Classification
Daniel J. Strick, Carlos Garcia, Anthony Huang, Thomas Gardos
arxiv.org/abs/2505.06646 mastoxiv.page/@arXiv_eessIV_bo
- Sharp Gaussian approximations for Decentralized Federated Learning
Soham Bonnerjee, Sayar Karmakar, Wei Biao Wu
arxiv.org/abs/2505.08125 mastoxiv.page/@arXiv_statML_bo
- HoloLLM: Multisensory Foundation Model for Language-Grounded Human Sensing and Reasoning
Chuhao Zhou, Jianfei Yang
arxiv.org/abs/2505.17645 mastoxiv.page/@arXiv_csCV_bot/
- A Copula Based Supervised Filter for Feature Selection in Diabetes Risk Prediction Using Machine ...
Agnideep Aich, Md Monzur Murshed, Sameera Hewage, Amanda Mayeaux
arxiv.org/abs/2505.22554 mastoxiv.page/@arXiv_statML_bo
- Synthesis of discrete-continuous quantum circuits with multimodal diffusion models
Florian F\"urrutter, Zohim Chandani, Ikko Hamamura, Hans J. Briegel, Gorka Mu\~noz-Gil
arxiv.org/abs/2506.01666 mastoxiv.page/@arXiv_quantph_b
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@arXiv_csDS_bot@mastoxiv.page
2026-02-04 07:39:24

ZOR filters: fast and smaller than fuse filters
Antoine Limasset
arxiv.org/abs/2602.03525 arxiv.org/pdf/2602.03525 arxiv.org/html/2602.03525
arXiv:2602.03525v1 Announce Type: new
Abstract: Probabilistic membership filters support fast approximate membership queries with a controlled false-positive probability $\varepsilon$ and are widely used across storage, analytics, networking, and bioinformatics \cite{chang2008bigtable,dayan2018optimalbloom,broder2004network,harris2020improved,marchet2023scalable,chikhi2025logan,hernandez2025reindeer2}. In the static setting, state-of-the-art designs such as XOR and fuse filters achieve low overhead and very fast queries, but their peeling-based construction succeeds only with high probability, which complicates deterministic builds \cite{graf2020xor,graf2022binary,ulrich2023taxor}.
We introduce \emph{ZOR filters}, a deterministic continuation of XOR/fuse filters that guarantees construction termination while preserving the same XOR-based query mechanism. ZOR replaces restart-on-failure with deterministic peeling that abandons a small fraction of keys, and restores false-positive-only semantics by storing the remainder in a compact auxiliary structure. In our experiments, the abandoned fraction drops below $1\%$ for moderate arity (e.g., $N\ge 5$), so the auxiliary handles a negligible fraction of keys. As a result, ZOR filters can achieve overhead within $1\%$ of the information-theoretic lower bound $\log_2(1/\varepsilon)$ while retaining fuse-like query performance; the additional cost is concentrated on negative queries due to the auxiliary check. Our current prototype builds several-fold slower than highly optimized fuse builders because it maintains explicit incidence information during deterministic peeling; closing this optimisation gap is an engineering target.
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