11 citations · 20 across the 8 of their papers we have counts for
9 papers
KiranaBench: Can a Language Model Run a Neighbourhood Shop?
Bhuvan Arora, Ankita Karmakar, Chaitanya Jain +10
Large language model (LLM) agents are increasingly evaluated on long-horizon tasks, including autonomously running a business. Existing benchmarks for this setting are almost exclu…
The Rise of Verbal Reinforcement Learning
Kshitij Tayal, Arun Sharma, Genta Indra Winata +2
Natural language is emerging as a primary feedback channel for improving language agents, capable of conveying intent, preferences, and causal structure in forms interpretable by b…
T1-Bench: Benchmarking Multi-Scenario Agents in Real-World Domains
Genta Indra Winata, Amartya Chakraborty, Yuzhen Lin +12
Recent advances in reasoning and tool-calling capabilities of large language models (LLMs) have enabled increasingly capable agentic systems. However, existing benchmarks remain li…
Hierarchical Conditional Multi-Task Learning for Streamflow Modeling
Shaoming Xu, Arvind Renganathan, Ankush Khandelwal +9
Streamflow, vital for water resource management, is governed by complex hydrological systems involving intermediate processes driven by meteorological forces. While deep learning m…
ExoTST: Exogenous-Aware Temporal Sequence Transformer for Time Series Prediction
Kshitij Tayal, Arvind Renganathan, Xiaowei Jia +2
Accurate long-term predictions are the foundations for many machine learning applications and decision-making processes. Traditional time series approaches for prediction often foc…
Koopman Invertible Autoencoder: Leveraging Forward and Backward Dynamics for Temporal Modeling
Kshitij Tayal, Arvind Renganathan, Rahul Ghosh +2
Accurate long-term predictions are the foundations for many machine learning applications and decision-making processes. However, building accurate long-term prediction models rema…
Robust Inverse Framework using Knowledge-guided Self-Supervised Learning: An application to Hydrology
Rahul Ghosh, Arvind Renganathan, Kshitij Tayal +6
Machine Learning is beginning to provide state-of-the-art performance in a range of environmental applications such as streamflow prediction in a hydrologic basin. However, buildin…
Phase Retrieval using Single-Instance Deep Generative Prior
Kshitij Tayal, Raunak Manekar, Zhong Zhuang +4
Several deep learning methods for phase retrieval exist, but most of them fail on realistic data without precise support information. We propose a novel method based on single-inst…
Inverse Problems, Deep Learning, and Symmetry Breaking
Kshitij Tayal, Chieh-Hsin Lai, Vipin Kumar +1
In many physical systems, inputs related by intrinsic system symmetries are mapped to the same output. When inverting such systems, i.e., solving the associated inverse problems, t…