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08/16/2023
AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework
This technical report presents AutoGen, a new framework that enables dev...
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06/02/2023
An Empirical Study on Challenging Math Problem Solving with GPT-4
Employing Large Language Models (LLMs) to address mathematical problems ...
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05/28/2023
HyperTime: Hyperparameter Optimization for Combating Temporal Distribution Shifts
In this work, we propose a hyperparameter optimization method named Hype...
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03/08/2023
Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference
Large Language Models (LLMs) like GPT-3 have sparked significant interes...
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08/04/2022
ACE: Adaptive Constraint-aware Early Stopping in Hyperparameter Optimization
Deploying machine learning models requires high model quality and needs ...
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06/03/2022
Multivariate Sparse Group Lasso Joint Model for Radiogenomics Data
Radiogenomics is an emerging field in cancer research that combines medi...
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02/20/2022
Mining Robust Default Configurations for Resource-constrained AutoML
Automatic machine learning (AutoML) is a key enabler of the mass deploym...
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11/29/2021
Bounding the Last Mile: Efficient Learned String Indexing
We introduce the RadixStringSpline (RSS) learned index structure for eff...
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11/11/2021
Fair AutoML
We present an end-to-end automated machine learning system to find machi...
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10/29/2021
Estimating the Distribution of Ratio of Paired Event Times in Phase II Oncology Trials
With the rapid development of new anti-cancer agents which are cytostati...
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10/25/2021
Weighted Mean Difference Statistics for Paired Data in Presence of Missing Values
Missing data is a common issue in many biomedical studies. Under a paire...
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10/01/2021
Geometry Attention Transformer with Position-aware LSTMs for Image Captioning
In recent years, transformer structures have been widely applied in imag...
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08/05/2021
Q-error Bounds of Random Uniform Sampling for Cardinality Estimation
Random uniform sampling has been studied in various statistical tasks bu...
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06/17/2021
An Empirical Study on Hyperparameter Optimization for Fine-Tuning Pre-trained Language Models
The performance of fine-tuning pre-trained language models largely depen...
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06/09/2021
ChaCha for Online AutoML
We propose the ChaCha (Champion-Challengers) algorithm for making an onl...
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02/04/2021
Active Boundary Loss for Semantic Segmentation
This paper proposes a novel active boundary loss for semantic segmentati...
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10/01/2020
Understanding the hardness of approximate query processing with joins
We study the hardness of Approximate Query Processing (AQP) of various t...
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08/11/2020
GEE-TGDR: A longitudinal feature selection algorithm and its application to lncRNA expression profiles for psoriasis patients treated with immune therapies
With the fast evolution of high-throughput technology, longitudinal gene...
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08/06/2020
Concentration Bounds for Co-occurrence Matrices of Markov Chains
Co-occurrence statistics for sequential data are common and important da...
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07/06/2020
Faster Graph Embeddings via Coarsening
Graph embeddings are a ubiquitous tool for machine learning tasks, such ...
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06/04/2020
Universal Graph Compression: Stochastic Block Models
Motivated by the prevalent data science applications of processing and m...
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05/04/2020
Cost Effective Optimization for Cost-related Hyperparameters
The increasing demand for democratizing machine learning algorithms for ...
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04/22/2020
Qd-tree: Learning Data Layouts for Big Data Analytics
Corporations today collect data at an unprecedented and accelerating sca...
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11/12/2019
FLO: Fast and Lightweight Hyperparameter Optimization for AutoML
Integrating ML models in software is of growing interest. Building accur...
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10/27/2019
Towards Extracting Highlights From Recorded Live Videos: An Implicit Crowdsourcing Approach
Live streaming platforms need to store a lot of recorded live videos on ...
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06/26/2019
NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization
We study the problem of large-scale network embedding, which aims to lea...
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05/21/2019
ALEX: An Updatable Adaptive Learned Index
Recent work on "learned indexes" has revolutionized the way we look at t...
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11/08/2018
Efficient Identification of Approximate Best Configuration of Training in Large Datasets
A configuration of training refers to the combinations of feature engine...
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03/13/2014