Experimental comparison of 15 HPO and NAS algorithms for automated feature preprocessing on 45 tabular datasets finds evolution-based methods and random search as top performers.
Agrawal, Tomas Karnagel, Sam Idicula, Sanjay Jinturkar, and Nipun Agarwal
8 Pith papers cite this work, alongside 117 external citations. Polarity classification is still indexing.
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FlintKV delivers a durable linearizable skiplist key-value store with full production API support (atomic batches, snapshot iterators) and reports up to 75% higher end-to-end throughput than prior NVM stores.
PipeANN-Filter improves filtered vector search latency and throughput on SSD by exploring a superset of valid vectors identified via probabilistic filters and verifying attributes only after selecting top-k candidates.
FAVOR achieves 1.3-5x higher QPS at 95% Recall@10 for arbitrary filtered ANNS by combining exclusion-distance reshaping in HNSW graphs with a selectivity-driven router that switches between brute-force and optimized search.
Hermes enables constant-time global aggregations and in-place updates on homomorphically encrypted databases by embedding precomputed statistics in packed ciphertexts and using polynomial slot masking and shifting.
RACORN-1 adds adaptive search fallback to ACORN-1 to fix recall collapse at low selectivity in filtered vector search, achieving 9-26x speedups over HNSW with recovered recall on 1M-40M datasets.
Relational engines achieve faster SQL+vector-search queries on GPU than CPU when using compact vector indexes and fast interconnects, reversing the CPU-only design in current systems.
This survey discusses key components and challenges for Personal LLM Agents and reviews solutions for their capability, efficiency, and security.
citing papers explorer
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Auto-FP: An Experimental Study of Automated Feature Preprocessing for Tabular Data
Experimental comparison of 15 HPO and NAS algorithms for automated feature preprocessing on 45 tabular datasets finds evolution-based methods and random search as top performers.
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FlintKV: A Fast Durable Storage Engine for Modern Databases
FlintKV delivers a durable linearizable skiplist key-value store with full production API support (atomic batches, snapshot iterators) and reports up to 75% higher end-to-end throughput than prior NVM stores.
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PipeANN-Filter: An Efficient Filtered Vector Search System on SSD
PipeANN-Filter improves filtered vector search latency and throughput on SSD by exploring a superset of valid vectors identified via probabilistic filters and verifying attributes only after selecting top-k candidates.
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FAVOR: Efficient Filter-Agnostic Vector ANNS Based on Selectivity-Aware Exclusion Distances
FAVOR achieves 1.3-5x higher QPS at 95% Recall@10 for arbitrary filtered ANNS by combining exclusion-distance reshaping in HNSW graphs with a selectivity-driven router that switches between brute-force and optimized search.
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Hermes: Efficient Global Homomorphic Aggregation over Mutable Packed Ciphertexts
Hermes enables constant-time global aggregations and in-place updates on homomorphically encrypted databases by embedding precomputed statistics in packed ciphertexts and using polynomial slot masking and shifting.
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RACORN-1: Adaptive Recall-Preserving Speedup for Low-Selectivity Filtered Vector Search
RACORN-1 adds adaptive search fallback to ACORN-1 to fix recall collapse at low selectivity in filtered vector search, achieving 9-26x speedups over HNSW with recovered recall on 1M-40M datasets.
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To GPU or Not to GPU: Vector Search in Relational Engines
Relational engines achieve faster SQL+vector-search queries on GPU than CPU when using compact vector indexes and fast interconnects, reversing the CPU-only design in current systems.
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Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security
This survey discusses key components and challenges for Personal LLM Agents and reviews solutions for their capability, efficiency, and security.