5 citations · 7 across the 7 of their papers we have counts for
9 papers
High-Throughput LLM-Based Compression and What It Takes: A Systems Study of Inference Engines and Entropy Coding Optimizations
David Adams, Luisa Neubauer, Ping-Lin Kuo +5
Large language models are attractive text compressors because next-token probabilities can be converted into code lengths. In practice, however, the main bottleneck is often not mo…
Towards Anticipatory Databases Through Shared Data and Workload Semantics
Farzaneh Zirak, Kasper Overgaard Mortensen, Farhana Choudhury +1
Database management systems increasingly serve dynamic and exploratory workloads, yet many of their decisions still rely on low-level signals such as recency, frequency, and addres…
RUBRIC: Realism--Utility Balanced Ranking for Imbalanced Classification
Yanxuan Yu, Dong Liu, Shu Wang +9
Class imbalance poses a fundamental challenge in risk-sensitive applications such as fraud detection and medical diagnosis, where minority-class samples are scarce yet critical for…
HSMLA: Hierarchical Softmax Multi-scale Linear Attention for Efficient Vision Transformers
Dong Liu, Yanxuan Yu, Renata Borovica-Gajic +2
Vision transformers face significant computational overheads in high-resolution dense prediction due to the quadratic complexity of self-attention. Linear attention offers efficien…
IDSTune: A Multi-Agent Collaborative Framework for Integrated Database System Tuning
Yiyan Li, Guanli Liu, Renata Borovica-Gajic +6
Database tuning is critical for achieving high performance in modern database management systems (DBMSs). Existing methods typically optimize a single component---knobs, indexes, o…
SemCEB: A Cardinality Estimation Benchmark for Semantic Operators
Andreas Zimmerer, Claudius Kühn, Yang Li +3
Modern data systems increasingly expose multi-modal large language models as semantic operators: SQL operators, including filters and joins, whose predicates are defined by a natur…
Testing the Robustness of Learned Index Structures
Matthias Bachfischer, Renata Borovica-Gajic, Benjamin I. P. Rubinstein
While early empirical evidence has supported the case for learned index structures as having favourable average-case performance, little is known about their worst-case performance…
No DBA? No regret! Multi-armed bandits for index tuning of analytical and HTAP workloads with provable guarantees
R. Malinga Perera, Bastian Oetomo, Benjamin I. P. Rubinstein +1
Automating physical database design has remained a long-term interest in database research due to substantial performance gains afforded by optimised structures. Despite significan…
DBA bandits: Self-driving index tuning under ad-hoc, analytical workloads with safety guarantees
R. Malinga Perera, Bastian Oetomo, Benjamin I. P. Rubinstein +1
Automating physical database design has remained a long-term interest in database research due to substantial performance gains afforded by optimised structures. Despite significan…