397 citations · 1.7k across the 82 of their papers we have counts for
41 papers · 1 filter
Dynamic Harness Search: Building Multi-Agent Systems Per-Query via Prediction
Som Sagar, Shasha Li, Hejie Cui +2
Agent harnesses specify the roles, instructions, tools, and communication structure used to solve a task, and the right harness depends on the query. Because the value of each desi…
MLE-STAR: Machine Learning Engineering Agent via Search and Targeted Refinement
Jaehyun Nam, Jinsung Yoon, Jiefeng Chen +3
Agents based on large language models (LLMs) for machine learning engineering (MLE) can automatically implement ML models via code generation. However, existing approaches to build…
Retrieval Augmented Time Series Forecasting
Sungwon Han, Seungeon Lee, Meeyoung Cha +2
Time series forecasting uses historical data to predict future trends, leveraging the relationships between past observations and available features. In this paper, we propose RAFT…
Reasoning-SQL: Reinforcement Learning with SQL Tailored Partial Rewards for Reasoning-Enhanced Text-to-SQL
Mohammadreza Pourreza, Shayan Talaei, Ruoxi Sun +5
Text-to-SQL is a challenging task involving multiple reasoning-intensive subtasks, including natural language understanding, database schema comprehension, and precise SQL query fo…
Multi-Agent Design: Optimizing Agents with Better Prompts and Topologies
Han Zhou, Xingchen Wan, Ruoxi Sun +5
Large language models, employed as multiple agents that interact and collaborate with each other, have excelled at solving complex tasks. The agents are programmed with prompts tha…
From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation
Xingchen Wan, Han Zhou, Ruoxi Sun +3
Recent advances in long-context large language models (LLMs) have led to the emerging paradigm of many-shot in-context learning (ICL), where it is observed that scaling many more d…
Learn-by-interact: A Data-Centric Framework for Self-Adaptive Agents in Realistic Environments
Hongjin Su, Ruoxi Sun, Jinsung Yoon +3
Autonomous agents powered by large language models (LLMs) have the potential to enhance human capabilities, assisting with digital tasks from sending emails to performing data anal…
CHASE-SQL: Multi-Path Reasoning and Preference Optimized Candidate Selection in Text-to-SQL
Mohammadreza Pourreza, Hailong Li, Ruoxi Sun +7
In tackling the challenges of large language model (LLM) performance for Text-to-SQL tasks, we introduce CHASE-SQL, a new framework that employs innovative strategies, using test-t…
Learned Feature Importance Scores for Automated Feature Engineering
Yihe Dong, Sercan Arik, Nathanael Yoder +1
Feature engineering has demonstrated substantial utility for many machine learning workflows, such as in the small data regime or when distribution shifts are severe. Thus automati…
Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning
Sungwon Han, Jinsung Yoon, Sercan O Arik +1
Large Language Models (LLMs), with their remarkable ability to tackle challenging and unseen reasoning problems, hold immense potential for tabular learning, that is vital for many…
COSTAR: Improved Temporal Counterfactual Estimation with Self-Supervised Learning
Chuizheng Meng, Yihe Dong, Sercan Ö. Arık +2
Estimation of temporal counterfactual outcomes from observed history is crucial for decision-making in many domains such as healthcare and e-commerce, particularly when randomized…
Search-Adaptor: Embedding Customization for Information Retrieval
Jinsung Yoon, Sercan O Arik, Yanfei Chen +1
Embeddings extracted by pre-trained Large Language Models (LLMs) have significant potential to improve information retrieval and search. Beyond the zero-shot setup in which they ar…
TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting
Defu Cao, Furong Jia, Sercan O Arik +4
The past decade has witnessed significant advances in time series modeling with deep learning. While achieving state-of-the-art results, the best-performing architectures vary high…
PAITS: Pretraining and Augmentation for Irregularly-Sampled Time Series
Nicasia Beebe-Wang, Sayna Ebrahimi, Jinsung Yoon +2
Real-world time series data that commonly reflect sequential human behavior are often uniquely irregularly sampled and sparse, with highly nonuniform sampling over time and entitie…
Business Metric-Aware Forecasting for Inventory Management
Helen Zhou, Sercan O. Arik, Jingtao Wang
Time-series forecasts play a critical role in business planning. However, forecasters typically optimize objectives that are agnostic to downstream business goals and thus can prod…
LANISTR: Multimodal Learning from Structured and Unstructured Data
Sayna Ebrahimi, Sercan O. Arik, Yihe Dong +1
Multimodal large-scale pretraining has shown impressive performance for unstructured data such as language and image. However, a prevalent real-world scenario involves structured d…
SLM: End-to-end Feature Selection via Sparse Learnable Masks
Yihe Dong, Sercan O. Arik
Feature selection has been widely used to alleviate compute requirements during training, elucidate model interpretability, and improve model generalizability. We propose SLM -- Sp…
ASPEST: Bridging the Gap Between Active Learning and Selective Prediction
Jiefeng Chen, Jinsung Yoon, Sayna Ebrahimi +3
Selective prediction aims to learn a reliable model that abstains from making predictions when uncertain. These predictions can then be deferred to humans for further evaluation. A…
TSMixer: An All-MLP Architecture for Time Series Forecasting
Si-An Chen, Chun-Liang Li, Nate Yoder +2
Real-world time-series datasets are often multivariate with complex dynamics. To capture this complexity, high capacity architectures like recurrent- or attention-based sequential…
SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch
Jinsung Yoon, Kihyuk Sohn, Chun-Liang Li +2
Semi-supervised anomaly detection is a common problem, as often the datasets containing anomalies are partially labeled. We propose a canonical framework: Semi-supervised Pseudo-la…
Provable Membership Inference Privacy
Zachary Izzo, Jinsung Yoon, Sercan O. Arik +1
In applications involving sensitive data, such as finance and healthcare, the necessity for preserving data privacy can be a significant barrier to machine learning model developme…
Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts
Rui Wang, Yihe Dong, Sercan Ö. Arik +1
Temporal distributional shifts, with underlying dynamics changing over time, frequently occur in real-world time series and pose a fundamental challenge for deep neural networks (D…
Self-Supervised Learning with an Information Maximization Criterion
Serdar Ozsoy, Shadi Hamdan, Sercan Ö. Arik +2
Self-supervised learning allows AI systems to learn effective representations from large amounts of data using tasks that do not require costly labeling. Mode collapse, i.e., the m…
Invariant Structure Learning for Better Generalization and Causal Explainability
Yunhao Ge, Sercan Ö. Arik, Jinsung Yoon +3
Learning the causal structure behind data is invaluable for improving generalization and obtaining high-quality explanations. We propose a novel framework, Invariant Structure Lear…