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Benjamin Coleman

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9 papers

2 author rows

NeurIPS Conference 2023 Conference Paper

DESSERT: An Efficient Algorithm for Vector Set Search with Vector Set Queries

Joshua Engels
Benjamin Coleman
Vihan Lakshman
Anshumali Shrivastava

We study the problem of $\text{\emph{vector set search}}$ with $\text{\emph{vector set queries}}$. This task is analogous to traditional near-neighbor search, with the exception that both the query and each element in the collection are $\text{\textit{sets}}$ of vectors. We identify this problem as a core subroutine for semantic search applications and find that existing solutions are unacceptably slow. Towards this end, we present a new approximate search algorithm, DESSERT ($\text{\bf D}$ESSERT $\text{\bf E}$ffeciently $\text{\bf S}$earches $\text{\bf S}$ets of $\text{\bf E}$mbeddings via $\text{\bf R}$etrieval $\text{\bf T}$ables). DESSERT is a general tool with strong theoretical guarantees and excellent empirical performance. When we integrate DESSERT into ColBERT, a state-of-the-art semantic search model, we find a 2-5x speedup on the MS MARCO and LoTTE retrieval benchmarks with minimal loss in recall, underscoring the effectiveness and practical applicability of our proposal.

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NeurIPS Conference 2023 Conference Paper

One-Pass Distribution Sketch for Measuring Data Heterogeneity in Federated Learning

Zichang Liu
Zhaozhuo Xu
Benjamin Coleman
Anshumali Shrivastava

Federated learning (FL) is a machine learning paradigm where multiple client devices train models collaboratively without data exchange. Data heterogeneity problem is naturally inherited in FL since data in different clients follow diverse distributions. To mitigate the negative influence of data heterogeneity, we need to start by measuring it across clients. However, the efficient measurement between distributions is a challenging problem, especially in high dimensionality. In this paper, we propose a one-pass distribution sketch to represent the client data distribution. Our sketching algorithm only requires a single pass of the client data, which is efficient in terms of time and memory. Moreover, we show in both theory and practice that the distance between two distribution sketches represents the divergence between their corresponding distributions. Furthermore, we demonstrate with extensive experiments that our distribution sketch improves the client selection in the FL training. We also showcase that our distribution sketch is an efficient solution to the cold start problem in FL for new clients with unlabeled data.

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NeurIPS Conference 2023 Conference Paper

Unified Embedding: Battle-Tested Feature Representations for Web-Scale ML Systems

Benjamin Coleman
Wang-Cheng Kang
Matthew Fahrbach
Ruoxi Wang
Lichan Hong
Ed Chi
Derek Cheng

Learning high-quality feature embeddings efficiently and effectively is critical for the performance of web-scale machine learning systems. A typical model ingests hundreds of features with vocabularies on the order of millions to billions of tokens. The standard approach is to represent each feature value as a $d$-dimensional embedding, which introduces hundreds of billions of parameters for extremely high-cardinality features. This bottleneck has led to substantial progress in alternative embedding algorithms. Many of these methods, however, make the assumption that each feature uses an independent embedding table. This work introduces a simple yet highly effective framework, Feature Multiplexing, where one single representation space is used for many different categorical features. Our theoretical and empirical analysis reveals that multiplexed embeddings can be decomposed into components from each constituent feature, allowing models to distinguish between features. We show that multiplexed representations give Pareto-optimal space-accuracy tradeoffs for three public benchmark datasets. Further, we propose a highly practical approach called Unified Embedding with three major benefits: simplified feature configuration, strong adaptation to dynamic data distributions, and compatibility with modern hardware. Unified embedding gives significant improvements in offline and online metrics compared to highly competitive baselines across five web-scale search, ads, and recommender systems, where it serves billions of users across the world in industry-leading products.

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NeurIPS Conference 2022 Conference Paper

Graph Reordering for Cache-Efficient Near Neighbor Search

Benjamin Coleman
Santiago Segarra
Alexander J. Smola
Anshumali Shrivastava

Graph search is one of the most successful algorithmic trends in near neighbor search. Several of the most popular and empirically successful algorithms are, at their core, a greedy walk along a pruned near neighbor graph. However, graph traversal applications often suffer from poor memory access patterns, and near neighbor search is no exception to this rule. Our measurements show that popular search indices such as the hierarchical navigable small-world graph (HNSW) can have poor cache miss performance. To address this issue, we formulate the graph traversal problem as a cache hit maximization task and propose multiple graph reordering as a solution. Graph reordering is a memory layout optimization that groups commonly-accessed nodes together in memory. We mathematically formalize the connection between the graph layout and the cache complexity of search. We present exhaustive experiments applying several reordering algorithms to a leading graph-based near neighbor method based on the HNSW index. We find that reordering improves the query time by up to 40%, we present analysis and improvements for existing graph layout methods, and we demonstrate that the time needed to reorder the graph is negligible compared to the time required to construct the index.

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ICML Conference 2022 Conference Paper

One-Pass Diversified Sampling with Application to Terabyte-Scale Genomic Sequence Streams

Benjamin Coleman
Benito Geordie
Li Chou
Ryan A. Leo Elworth
Todd J. Treangen
Anshumali Shrivastava

A popular approach to reduce the size of a massive dataset is to apply efficient online sampling to the stream of data as it is read or generated. Online sampling routines are currently restricted to variations of reservoir sampling, where each sample is selected uniformly and independently of other samples. This renders them unsuitable for large-scale applications in computational biology, such as metagenomic community profiling and protein function annotation, which suffer from severe class imbalance. To maintain a representative and diverse sample, we must identify and preferentially select data that are likely to belong to rare classes. We argue that existing schemes for diversity sampling have prohibitive overhead for large-scale problems and high-throughput streams. We propose an efficient sampling routine that uses an online representation of the data distribution as a prefilter to retain elements from rare groups. We apply this method to several genomic data analysis tasks and demonstrate significant speedup in downstream analysis without sacrificing the quality of the results. Because our algorithm is 2x faster and uses 1000x less memory than coreset, reservoir and sketch-based alternatives, we anticipate that it will become a useful preprocessing step for applications with large-scale streaming data.

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NeurIPS Conference 2022 Conference Paper

Retaining Knowledge for Learning with Dynamic Definition

Zichang Liu
Benjamin Coleman
Tianyi Zhang
Anshumali Shrivastava

Machine learning models are often deployed in settings where they must be constantly updated in response to the changes in class definitions while retaining high accuracy on previously learned definitions. A classical use case is fraud detection, where new fraud schemes come one after another. While such an update can be accomplished by re-training on the complete data, the process is inefficient and prevents real-time and on-device learning. On the other hand, efficient methods that incrementally learn from new data often result in the forgetting of previously-learned knowledge. We define this problem as Learning with Dynamic Definition (LDD) and demonstrate that popular models, such as the Vision Transformer and Roberta, exhibit substantial forgetting of past definitions. We present the first practical and provable solution to LDD. Our proposal is a hash-based sparsity model \textit{RIDDLE} that solves evolving definitions by associating samples only to relevant parameters. We prove that our model is a universal function approximator and theoretically bounds the knowledge lost during the update process. On practical tasks with evolving class definition in vision and natural language processing, \textit{RIDDLE} outperforms baselines by up to 30\% on the original dataset while providing competitive accuracy on the update dataset.

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NeurIPS Conference 2021 Conference Paper

Practical Near Neighbor Search via Group Testing

Joshua Engels
Benjamin Coleman
Anshumali Shrivastava

We present a new algorithm for the approximate near neighbor problem that combines classical ideas from group testing with locality-sensitive hashing (LSH). We reduce the near neighbor search problem to a group testing problem by designating neighbors as "positives, " non-neighbors as "negatives, " and approximate membership queries as group tests. We instantiate this framework using distance-sensitive Bloom Filters to Identify Near-Neighbor Groups (FLINNG). We prove that FLINNG has sub-linear query time and show that our algorithm comes with a variety of practical advantages. For example, FLINNG can be constructed in a single pass through the data, consists entirely of efficient integer operations, and does not require any distance computations. We conduct large-scale experiments on high-dimensional search tasks such as genome search, URL similarity search, and embedding search over the massive YFCC100M dataset. In our comparison with leading algorithms such as HNSW and FAISS, we find that FLINNG can provide up to a 10x query speedup with substantially smaller indexing time and memory.

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AAAI Conference 2021 Conference Paper

Revisiting Consistent Hashing with Bounded Loads

John Chen
Benjamin Coleman
Anshumali Shrivastava

Dynamic load balancing lies at the heart of distributed caching. Here, the goal is to assign objects (load) to servers (computing nodes) in a way that provides load balancing while at the same time dynamically adjusts to the addition or removal of servers. Load balancing is critical to many areas including cloud systems, distributed databases, and distributed and data-parallel machine learning. A popular and widely adopted solution to dynamic load balancing is the two-decade-old Consistent Hashing (CH). Recently, an elegant extension was provided to account for server bounds. In this paper, we identify that existing methodologies for CH and its variants suffer from cascaded overflow, leading to poor load balancing. This cascading effect leads to decreasing performance of the hashing procedure with increasing load. To overcome the cascading effect, we propose a simple solution to CH based on recent advances in fast minwise hashing. We show, both theoretically and empirically, that our proposed solution is significantly superior for load balancing and is optimal in many senses. On the AOL search dataset and Indiana University Clicks dataset with real user activity, our proposed solution reduces cache misses by several magnitudes.

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ICML Conference 2020 Conference Paper

Sub-linear Memory Sketches for Near Neighbor Search on Streaming Data

Benjamin Coleman
Richard G. Baraniuk
Anshumali Shrivastava

We present the first sublinear memory sketch that can be queried to find the nearest neighbors in a dataset. Our online sketching algorithm compresses an N element dataset to a sketch of size $O(N^b \log^3 N)$ in $O(N^{(b+1)} \log^3 N)$ time, where $b < 1$. This sketch can correctly report the nearest neighbors of any query that satisfies a stability condition parameterized by $b$. We achieve sublinear memory performance on stable queries by combining recent advances in locality sensitive hash (LSH)-based estimators, online kernel density estimation, and compressed sensing. Our theoretical results shed new light on the memory-accuracy tradeoff for nearest neighbor search, and our sketch, which consists entirely of short integer arrays, has a variety of attractive features in practice. We evaluate the memory-recall tradeoff of our method on a friend recommendation task in the Google plus social media network. We obtain orders of magnitude better compression than the random projection based alternative while retaining the ability to report the nearest neighbors of practical queries.

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