4 citations · 8 across the 16 of their papers we have counts for
8 papers · 1 filter
Evaluating Joinable Column Discovery Approaches for Context-Aware Search
Harsha Kokel, Aamod Khatiwada, Tejaswini Pedapati +4
Joinable Column Discovery is a critical challenge in automating enterprise data analysis. While existing approaches focus on syntactic overlap and semantic similarity, there remain…
QueryGym: Step-by-Step Interaction with Relational Databases
Haritha Ananthakrishnan, Harsha Kokel, Kelsey Sikes +4
We introduce QueryGym, an interactive environment for building, testing, and evaluating LLM-based query planning agents. Existing frameworks often tie agents to specific query lang…
Seemingly Simple Planning Problems are Computationally Challenging: The Countdown Game
Michael Katz, Harsha Kokel, Sarath Sreedharan
There is a broad consensus that the inability to form long-term plans is one of the key limitations of current foundational models and agents. However, the existing planning benchm…
TOPJoin: A Context-Aware Multi-Criteria Approach for Joinable Column Search
Harsha Kokel, Aamod Khatiwada, Tejaswini Pedapati +4
One of the major challenges in enterprise data analysis is the task of finding joinable tables that are conceptually related and provide meaningful insights. Traditionally, joinabl…
Make Planning Research Rigorous Again!
Michael Katz, Harsha Kokel, Christian Muise +2
In over sixty years since its inception, the field of planning has made significant contributions to both the theory and practice of building planning software that can solve a nev…
ACPBench Hard: Unrestrained Reasoning about Action, Change, and Planning
Harsha Kokel, Michael Katz, Kavitha Srinivas +1
The ACPBench dataset provides atomic reasoning tasks required for efficient planning. The dataset is aimed at distilling the complex plan generation task into separate atomic reaso…
Combining Planning and Reinforcement Learning for Solving Relational Multiagent Domains
Nikhilesh Prabhakar, Ranveer Singh, Harsha Kokel +2
Multiagent Reinforcement Learning (MARL) poses significant challenges due to the exponential growth of state and action spaces and the non-stationary nature of multiagent environme…
Can Cross Encoders Produce Useful Sentence Embeddings?
Haritha Ananthakrishnan, Julian Dolby, Harsha Kokel +2
Cross encoders (CEs) are trained with sentence pairs to detect relatedness. As CEs require sentence pairs at inference, the prevailing view is that they can only be used as re-rank…