11 citations · 21 across the 14 of their papers we have counts for
15 papers
FunL2O: LLM-Guided Feature Function Design for Learning to Optimize
Bingheng Li, Junyang Cai, Yupeng Zhang +3
Learning-to-optimize (L2O) methods accelerate repeated optimization by training models to predict solutions, warm starts, branching decisions, or other forms of solver guidance. A…
Hamiltonian-Guided Leverage Embedding: Robust Subspace Compression for Efficient QAOA Parameter Estimation
Sumanta Mukherjee, Kalyan Dasgupta, Surya Shravan Kumar Sajja +5
Every Ising energy is a known linear function of measured spin features, yet the standard QAOA estimator discards this structure. We introduce Hamiltonian-Guided Leverage Embedding…
Quantum Variational Approaches to the Maximum Independent Set Problem at Utility Scale
Kalyan Dasgupta, Sumanta Mukherjee, Dhriti Verma +4
Near-optimal solutions to Maximum Independent Set on dense graphs sit in local optima that greedy correction and maximality extension cannot escape. We encode near-optimal seeds as…
DiagnosticIQ: A Benchmark for LLM-Based Industrial Maintenance Action Recommendation from Symbolic Rules
Devin Yasith De Silva, Dhaval Patel, Christodoulos Constantinides +8
Monitoring complex industrial assets relies on engineer-authored symbolic rules that trigger on sensor conditions and prompt technicians to perform corrective actions. The bottlene…
Constrained Triple-Head Price Optimization (C3PO) Network for Discrete-Choice Models
Shivaram Subramanian, Zhengliang Xue, Markus Ettl +2
We propose a Constrained Triple-Head Price Optimization (C3PO) network for real-time bi-level decision making in discrete-choice environments. In these settings, a seller selects p…
Learning to Shuffle: Block Reshuffling and Reversal Schemes for Stochastic Optimization
Lam M. Nguyen, Dzung T. Phan, Jayant Kalagnanam
Shuffling strategies for stochastic gradient descent (SGD), including incremental gradient, shuffle-once, and random reshuffling, are supported by rigorous convergence analyses for…
Efficient Embedding-based Synthetic Data Generation for Complex Reasoning Tasks
Srideepika Jayaraman, Achille Fokoue, Dhaval Patel +1
Synthetic Data Generation (SDG), leveraging Large Language Models (LLMs), has recently been recognized and broadly adopted as an effective approach to improve the performance of sm…
Revisiting the Generic Transformer: Deconstructing a Strong Baseline for Time Series Foundation Models
Yunshi Wen, Wesley M. Gifford, Chandra Reddy +3
The recent surge in Time Series Foundation Models has rapidly advanced the field, yet the heterogeneous training setups across studies make it difficult to attribute improvements t…
Toward a Trustworthy Optimization Modeling Agent via Verifiable Synthetic Data Generation
Vinicius Lima, Dzung T. Phan, Jayant Kalagnanam +2
We present a framework for training trustworthy large language model (LLM) agents for optimization modeling via a verifiable synthetic data generation pipeline. Focusing on linear…
FailureSensorIQ: A Multi-Choice QA Dataset for Understanding Sensor Relationships and Failure Modes
Christodoulos Constantinides, Dhaval Patel, Shuxin Lin +3
We introduce FailureSensorIQ, a novel Multi-Choice Question-Answering (MCQA) benchmarking system designed to assess the ability of Large Language Models (LLMs) to reason and unders…
AssetOpsBench: Benchmarking AI Agents for Task Automation in Industrial Asset Operations and Maintenance
Dhaval Patel, Shuxin Lin, James Rayfield +7
AI for Industrial Asset Lifecycle Management aims to automate complex operational workflows, such as condition monitoring and maintenance scheduling, to minimize system downtime. W…
TSPulse: Tiny Pre-Trained Models with Disentangled Representations for Rapid Time-Series Analysis
Vijay Ekambaram, Subodh Kumar, Arindam Jati +5
Time-series tasks often benefit from signals expressed across multiple representation spaces (e.g., time vs. frequency) and at varying abstraction levels (e.g., local patterns vs.…
CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
Claudius Krause, Michele Faucci Giannelli, Gregor Kasieczka +66
We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of i…
Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series
Vijay Ekambaram, Arindam Jati, Pankaj Dayama +5
Large pre-trained models excel in zero/few-shot learning for language and vision tasks but face challenges in multivariate time series (TS) forecasting due to diverse data characte…
AutoMixer for Improved Multivariate Time-Series Forecasting on Business and IT Observability Data
Santosh Palaskar, Vijay Ekambaram, Arindam Jati +11
The efficiency of business processes relies on business key performance indicators (Biz-KPIs), that can be negatively impacted by IT failures. Business and IT Observability (BizITO…