27 citations · 27 across the 11 of their papers we have counts for
11 papers
Notes to Self: Can LLMs Benefit from Experiential Abstractions?
Chang Liu, Xinyu Li, Artur Dubrawski
Humans distill experience into reusable abstractions, e.g., strategies and cautionary reminders, and apply them to gradually solve problems more effectively. We study whether Large…
TimeSeriesExamAgent: Creating Time Series Reasoning Benchmarks at Scale
Malgorzata Gwiazda, Yifu Cai, Mononito Goswami +2
Large Language Models (LLMs) have shown promising performance in time series modeling tasks, but do they truly understand time series data? While multiple benchmarks have been prop…
Mitigating Persistent Client Dropout in Asynchronous Decentralized Federated Learning
Ignacy Stępka, Nicholas Gisolfi, Kacper Trębacz +1
We consider the problem of persistent client dropout in asynchronous Decentralized Federated Learning (DFL). Asynchronicity and decentralization obfuscate information about model u…
TimeSeriesGym: A Scalable Benchmark for (Time Series) Machine Learning Engineering Agents
Yifu Cai, Xinyu Li, Mononito Goswami +3
We introduce TimeSeriesGym, a scalable benchmarking framework for evaluating Artificial Intelligence (AI) agents on time series machine learning engineering challenges. Existing be…
Investigating Compositional Reasoning in Time Series Foundation Models
Willa Potosnak, Cristian Challu, Mononito Goswami +4
Large pre-trained time series foundation models (TSFMs) have demonstrated promising zero-shot performance across a wide range of domains. However, a question remains: Do TSFMs succ…
TimeSeriesExam: A time series understanding exam
Yifu Cai, Arjun Choudhry, Mononito Goswami +1
Large Language Models (LLMs) have recently demonstrated a remarkable ability to model time series data. These capabilities can be partly explained if LLMs understand basic time ser…
Towards Long-Context Time Series Foundation Models
Nina Żukowska, Mononito Goswami, Michał Wiliński +2
Time series foundation models have shown impressive performance on a variety of tasks, across a wide range of domains, even in zero-shot settings. However, most of these models are…
Exploring Representations and Interventions in Time Series Foundation Models
Michał Wiliński, Mononito Goswami, Willa Potosnak +2
Time series foundation models (TSFMs) promise to be powerful tools for a wide range of applications. However, their internal representations and learned concepts are still not well…
A SAT-based approach to rigorous verification of Bayesian networks
Ignacy Stępka, Nicholas Gisolfi, Artur Dubrawski
Recent advancements in machine learning have accelerated its widespread adoption across various real-world applications. However, in safety-critical domains, the deployment of mach…
MOMENT: A Family of Open Time-series Foundation Models
Mononito Goswami, Konrad Szafer, Arjun Choudhry +3
We introduce MOMENT, a family of open-source foundation models for general-purpose time series analysis. Pre-training large models on time series data is challenging due to (1) the…
Signal Quality Auditing for Time-series Data
Chufan Gao, Nicholas Gisolfi, Artur Dubrawski
Signal quality assessment (SQA) is required for monitoring the reliability of data acquisition systems, especially in AI-driven Predictive Maintenance (PMx) application contexts. S…