9 citations · 42 across the 25 of their papers we have counts for
26 papers
Beyond State Capacity: Why Retrieval Breaks in Mamba
Nir Yarden, Amit Ben-Artzy, Roy Schwartz
Mamba models underperform Transformers on in-context retrieval. This gap is usually attributed to Mamba's fixed-size recurrent state, which loses information as the context grows.…
Seeking Universal Shot Language Understanding Solutions
Haoxin Liu, Harshavardhan Kamarthi, Zhiyuan Zhao +2
Shot language understanding (SLU) is crucial for cinematic analysis but remains challenging due to its diverse cinematographic dimensions and subjective expert judgment. While visi…
Hierarchical Industrial Demand Forecasting with Temporal and Uncertainty Explanations
Harshavardhan Kamarthi, Shangqing Xu, Xinjie Tong +4
Hierarchical time-series forecasting is essential for demand prediction across various industries. While machine learning models have obtained significant accuracy and scalability…
In-context Pre-trained Time-Series Foundation Models adapt to Unseen Tasks
Shangqing Xu, Harshavardhan Kamarthi, Haoxin Liu +1
Time-series foundation models (TSFMs) have demonstrated strong generalization capabilities across diverse datasets and tasks. However, existing foundation models are typically pre-…
AHA: Scalable Alternative History Analysis for Operational Timeseries Applications
Harshavardhan Kamarthi, Harshil Shah, Henry Milner +4
Many operational systems collect high-dimensional timeseries data about users/systems on key performance metrics. For instance, ISPs, content distribution networks, and video deliv…
How Can Time Series Analysis Benefit From Multiple Modalities? A Survey and Outlook
Haoxin Liu, Harshavardhan Kamarthi, Zhiyuan Zhao +6
Time series analysis (TSA) is a longstanding research topic in the data mining community and has wide real-world significance. Compared to "richer" modalities such as language and…
A Picture is Worth A Thousand Numbers: Enabling LLMs Reason about Time Series via Visualization
Haoxin Liu, Chenghao Liu, B. Aditya Prakash
Large language models (LLMs), with demonstrated reasoning abilities across multiple domains, are largely underexplored for time-series reasoning (TsR), which is ubiquitous in the r…
Large Scale Hierarchical Industrial Demand Time-Series Forecasting incorporating Sparsity
Harshavardhan Kamarthi, Aditya B. Sasanur, Xinjie Tong +4
Hierarchical time-series forecasting (HTSF) is an important problem for many real-world business applications where the goal is to simultaneously forecast multiple time-series that…
Learning Graph Structures and Uncertainty for Accurate and Calibrated Time-series Forecasting
Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodriguez +2
Multi-variate time series forecasting is an important problem with a wide range of applications. Recent works model the relations between time-series as graphs and have shown that…
Time-Series Forecasting for Out-of-Distribution Generalization Using Invariant Learning
Haoxin Liu, Harshavardhan Kamarthi, Lingkai Kong +3
Time-series forecasting (TSF) finds broad applications in real-world scenarios. Due to the dynamic nature of time-series data, it is crucial to equip TSF models with out-of-distrib…
Time-MMD: Multi-Domain Multimodal Dataset for Time Series Analysis
Haoxin Liu, Shangqing Xu, Zhiyuan Zhao +8
Time series data are ubiquitous across a wide range of real-world domains. While real-world time series analysis (TSA) requires human experts to integrate numerical series data wit…
LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting
Haoxin Liu, Zhiyuan Zhao, Jindong Wang +2
Time-series forecasting (TSF) finds broad applications in real-world scenarios. Prompting off-the-shelf Large Language Models (LLMs) demonstrates strong zero-shot TSF capabilities…
PEMS: Pre-trained Epidemic Time-series Models
Harshavardhan Kamarthi, B. Aditya Prakash
Providing accurate and reliable predictions about the future of an epidemic is an important problem for enabling informed public health decisions. Recent works have shown that leve…
Large Pre-trained time series models for cross-domain Time series analysis tasks
Harshavardhan Kamarthi, B. Aditya Prakash
Large pre-trained models have been vital in recent advancements in domains like language and vision, making model training for individual downstream tasks more efficient and provid…
When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting
Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez +2
Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting, where the goal is to model and forecast multivariate time-series that have un…
Data-Centric Epidemic Forecasting: A Survey
Alexander Rodríguez, Harshavardhan Kamarthi, Pulak Agarwal +4
The COVID-19 pandemic has brought forth the importance of epidemic forecasting for decision makers in multiple domains, ranging from public health to the economy as a whole. While…
When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting
Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez +2
Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting, where the goal is to model and forecast multivariate time-series that have un…
CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting
Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez +2
Probabilistic time-series forecasting enables reliable decision making across many domains. Most forecasting problems have diverse sources of data containing multiple modalities an…
Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future
Harshavardhan Kamarthi, Alexander Rodríguez, B. Aditya Prakash
In real-time forecasting in public health, data collection is a non-trivial and demanding task. Often after initially released, it undergoes several revisions later (maybe due to h…
When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting
Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez +2
Accurate and trustworthy epidemic forecasting is an important problem that has impact on public health planning and disease mitigation. Most existing epidemic forecasting models di…
Selective Intervention Planning using Restless Multi-Armed Bandits to Improve Maternal and Child Health Outcomes
Siddharth Nishtala, Lovish Madaan, Aditya Mate +11
India has a maternal mortality ratio of 113 and child mortality ratio of 2830 per 100,000 live births. Lack of access to preventive care information is a major contributing factor…
Reinforcement Learning for Unified Allocation and Patrolling in Signaling Games with Uncertainty
Aravind Venugopal, Elizabeth Bondi, Harshavardhan Kamarthi +3
Green Security Games (GSGs) have been successfully used in the protection of valuable resources such as fisheries, forests and wildlife. While real-world deployment involves both r…
Missed calls, Automated Calls and Health Support: Using AI to improve maternal health outcomes by increasing program engagement
Siddharth Nishtala, Harshavardhan Kamarthi, Divy Thakkar +8
India accounts for 11% of maternal deaths globally where a woman dies in childbirth every fifteen minutes. Lack of access to preventive care information is a significant problem co…
Integrating Lexical Knowledge in Word Embeddings using Sprinkling and Retrofitting
Aakash Srinivasan, Harshavardhan Kamarthi, Devi Ganesan +1
Neural network based word embeddings, such as Word2Vec and GloVe, are purely data driven in that they capture the distributional information about words from the training corpus. P…