b'Matei Zaharia'

research

∙ 08/17/2023

Accelerating Aggregation Queries on Unstructured Streams of Data

Analysts and scientists are interested in querying streams of video, aud...

0 Matthew Russo, et al. ∙

research

∙ 07/18/2023

How is ChatGPT's behavior changing over time?

GPT-3.5 and GPT-4 are the two most widely used large language model (LLM...

0 Lingjiao Chen, et al. ∙

research

∙ 05/09/2023

FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance

There is a rapidly growing number of large language models (LLMs) that u...

0 Lingjiao Chen, et al. ∙

research

∙ 05/05/2023

Zelda: Video Analytics using Vision-Language Models

Advances in ML have motivated the design of video analytics systems that...

0 Francisco Romero, et al. ∙

research

∙ 02/11/2023

Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks

Recent advances in instruction-following large language models (LLMs) ha...

0 Daniel Kang, et al. ∙

research

∙ 12/29/2022

Transactions Make Debugging Easy

We propose TROD, a novel transaction-oriented framework for debugging mo...

0 Qian Li, et al. ∙

research

∙ 12/28/2022

Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Retrieval-augmented in-context learning has emerged as a powerful approa...

0 Omar Khattab, et al. ∙

research

∙ 12/02/2022

Moving Beyond Downstream Task Accuracy for Information Retrieval Benchmarking

Neural information retrieval (IR) systems have progressed rapidly in rec...

0 Keshav Santhanam, et al. ∙

research

∙ 11/29/2022

MegaBlocks: Efficient Sparse Training with Mixture-of-Experts

We present MegaBlocks, a system for efficient Mixture-of-Experts (MoE) t...

0 Trevor Gale, et al. ∙

research

∙ 09/18/2022

HAPI: A Large-scale Longitudinal Dataset of Commercial ML API Predictions

Commercial ML APIs offered by providers such as Google, Amazon and Micro...

8 Lingjiao Chen, et al. ∙

research

∙ 09/18/2022

Estimating and Explaining Model Performance When Both Covariates and Labels Shift

Deployed machine learning (ML) models often encounter new user data that...

3 Lingjiao Chen, et al. ∙

research

∙ 08/27/2022

Apiary: A DBMS-Backed Transactional Function-as-a-Service Framework

Developers are increasingly using function-as-a-service (FaaS) platforms...

0 Peter Kraft, et al. ∙

research

∙ 05/19/2022

PLAID: An Efficient Engine for Late Interaction Retrieval

Pre-trained language models are increasingly important components across...

5 Keshav Santhanam, et al. ∙

research

∙ 04/28/2022

Extricating IoT Devices from Vendor Infrastructure with Karl

Most consumer IoT devices are vertically integrated with cloud-side infr...

0 Gina Yuan, et al. ∙

research

∙ 01/15/2022

Finding Label and Model Errors in Perception Data With Learned Observation Assertions

ML is being deployed in complex, real-world scenarios where errors have ...

0 Daniel Kang, et al. ∙

research

∙ 12/13/2021

What can Data-Centric AI Learn from Data and ML Engineering?

Data-centric AI is a new and exciting research topic in the AI community...

0 Neoklis Polyzotis, et al. ∙

research

∙ 12/02/2021

ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction

Neural information retrieval (IR) has greatly advanced search and other ...

4 Keshav Santhanam, et al. ∙

research

∙ 11/19/2021

Toward Compact Parameter Representations for Architecture-Agnostic Neural Network Compression

This paper investigates deep neural network (DNN) compression from the p...

11 Yuezhou Sun, et al. ∙

research

∙ 11/09/2021

DistIR: An Intermediate Representation and Simulator for Efficient Neural Network Distribution

The rapidly growing size of deep neural network (DNN) models and dataset...

7 Keshav Santhanam, et al. ∙

research

∙ 10/22/2021

Solving Large-Scale Granular Resource Allocation Problems Efficiently with POP

Resource allocation problems in many computer systems can be formulated ...

0 Deepak Narayanan, et al. ∙

research

∙ 10/14/2021

Hindsight: Posterior-guided training of retrievers for improved open-ended generation

Many text generation systems benefit from using a retriever to retrieve ...

1 Ashwin Paranjape, et al. ∙

research

∙ 07/29/2021

Did the Model Change? Efficiently Assessing Machine Learning API Shifts

Machine learning (ML) prediction APIs are increasingly widely used. An M...

24 Lingjiao Chen, et al. ∙

research

∙ 04/13/2021

Don't Give Up on Large Optimization Problems; POP Them!

Resource allocation problems in many computer systems can be formulated ...

0 Deepak Narayanan, et al. ∙

research

∙ 04/09/2021

Efficient Large-Scale Language Model Training on GPU Clusters

Large language models have led to state-of-the-art accuracies across a r...

8 Deepak Narayanan, et al. ∙

research

∙ 04/01/2021

Allocation of Fungible Resources via a Fast, Scalable Price Discovery Method

We consider the problem of assigning or allocating resources to a set of...

0 Akshay Agrawal, et al. ∙

research

∙ 02/18/2021

FrugalMCT: Efficient Online ML API Selection for Multi-Label Classification Tasks

Multi-label classification tasks such as OCR and multi-object recognitio...

13 Lingjiao Chen, et al. ∙

research

∙ 01/02/2021

Baleen: Robust Multi-Hop Reasoning at Scale via Condensed Retrieval

Multi-hop reasoning (i.e., reasoning across two or more documents) at sc...

0 Omar Khattab, et al. ∙

research

∙ 09/09/2020

Task-agnostic Indexes for Deep Learning-based Queries over Unstructured Data

Unstructured data is now commonly queried by using target deep neural ne...

0 Daniel Kang, et al. ∙

research

∙ 08/20/2020

Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads

Specialized accelerators such as GPUs, TPUs, FPGAs, and custom ASICs hav...

0 Deepak Narayanan, et al. ∙

research

∙ 07/25/2020

Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics

While deep neural networks (DNNs) are an increasingly popular way to que...

1 Daniel Kang, et al. ∙

research

∙ 07/21/2020

DBOS: A Proposal for a Data-Centric Operating System

Current operating systems are complex systems that were designed before ...

0 Michael Cafarella, et al. ∙

research

∙ 07/01/2020

Relevance-guided Supervision for OpenQA with ColBERT

Systems for Open-Domain Question Answering (OpenQA) generally depend on ...

0 Omar Khattab, et al. ∙

research

∙ 06/30/2020

Similarity Search for Efficient Active Learning and Search of Rare Concepts

Many active learning and search approaches are intractable for industria...

7 Cody Coleman, et al. ∙

research

∙ 06/22/2020

Overlook: Differentially Private Exploratory Visualization for Big Data

Data exploration systems that provide differential privacy must manage a...

0 Pratiksha Thaker, et al. ∙

research

∙ 06/18/2020

Sparse GPU Kernels for Deep Learning

Scientific workloads have traditionally exploited high levels of sparsit...

11 Trevor Gale, et al. ∙

research

∙ 06/16/2020

Memory-Efficient Pipeline-Parallel DNN Training

Many state-of-the-art results in domains such as NLP and computer vision...

25 Deepak Narayanan, et al. ∙

research

∙ 06/12/2020

FrugalML: How to Use ML Prediction APIs More Accurately and Cheaply

Prediction APIs offered for a fee are a fast-growing industry and an imp...

20 Lingjiao Chen, et al. ∙

research

∙ 04/27/2020

ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT

Recent progress in Natural Language Understanding (NLU) is driving fast-...

0 Omar Khattab, et al. ∙

research

∙ 04/02/2020

Approximate Selection with Guarantees using Proxies

Due to the falling costs of data acquisition and storage, researchers an...

0 Daniel Kang, et al. ∙

research

∙ 03/03/2020

Model Assertions for Monitoring and Improving ML Model

ML models are increasingly deployed in settings with real world interact...

16 Daniel Kang, et al. ∙

research

∙ 03/03/2020

Model Assertions for Monitoring and Improving ML Models

ML models are increasingly deployed in settings with real world interact...

9 Daniel Kang, et al. ∙

research

∙ 11/20/2019

Express: Lowering the Cost of Metadata-hiding Communication with Cryptographic Privacy

Existing systems for metadata-hiding messaging that provide cryptographi...

0 Saba Eskandarian, et al. ∙

research

∙ 10/02/2019

MLPerf Training Benchmark

Machine learning is experiencing an explosion of software and hardware s...

6 Peter Mattson, et al. ∙

research

∙ 09/21/2019

Automated Lower Bounds on the I/O Complexity of Computation Graphs

We consider the problem of finding lower bounds on the I/O complexity of...

0 Saachi Jain, et al. ∙

research

∙ 06/26/2019

Selection Via Proxy: Efficient Data Selection For Deep Learning

Data selection methods such as active learning and core-set selection ar...

12 Cody Coleman, et al. ∙

research

∙ 06/03/2019

Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference

Machine learning (ML) has become increasingly important and performance-...

0 Peter Kraft, et al. ∙

research

∙ 03/29/2019

SysML: The New Frontier of Machine Learning Systems

Machine learning (ML) techniques are enjoying rapidly increasing adoptio...

0 Alexander Ratner, et al. ∙

research

∙ 10/29/2018

Splitability Annotations: Optimizing Black-Box Function Composition in Existing Libraries

Data movement is a major bottleneck in parallel data-intensive applicati...

0 Shoumik Palkar, et al. ∙

research

∙ 10/02/2018

LIT: Block-wise Intermediate Representation Training for Model Compression

Knowledge distillation (KD) is a popular method for reducing the computa...

4 Animesh Koratana, et al. ∙

research

∙ 07/14/2018

Beyond Data and Model Parallelism for Deep Neural Networks

The computational requirements for training deep neural networks (DNNs) ...

0 Zhihao Jia, et al. ∙

Matei Zaharia

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