26 citations · 69 across the 34 of their papers we have counts for
34 papers
Converting Expert Deliberation into Financial Signals Through A Context-Aware NLP Pipeline
Vivek Batra, Kristin Chen, Sanjiv Das +6
We introduce the CDSP (context-conditional deliberation signal pipeline), converting an investment committee's meeting transcripts into structured predictive features. CDSP segment…
Using Common Random Numbers for Simulation-based Planning with Rollouts
Sandarbh Yadav, Frederic J Maliakkal, Harshad Khadilkar +1
Simulation-based planning with rollouts is a widely-deployed technique for decision making in stochastic environments. The primary instrument of simulation-based planning is a samp…
Efficiency Boost in Decentralized Optimization: Reimagining Neighborhood Aggregation with Minimal Overhead
Durgesh Kalwar, Mayank Baranwal, Harshad Khadilkar
In today's data-sensitive landscape, distributed learning emerges as a vital tool, not only fortifying privacy measures but also streamlining computational operations. This becomes…
AEGIS: An Agent for Extraction and Geographic Identification in Scholarly Proceedings
Om Vishesh, Harshad Khadilkar, Deepak Akkil
Keeping pace with the rapid growth of academia literature presents a significant challenge for researchers, funding bodies, and academic societies. To address the time-consuming ma…
Causal-Counterfactual RAG: The Integration of Causal-Counterfactual Reasoning into RAG
Harshad Khadilkar, Abhay Gupta
Large language models (LLMs) have transformed natural language processing (NLP), enabling diverse applications by integrating large-scale pre-trained knowledge. However, their stat…
Redistributing Rewards Across Time and Agents for Multi-Agent Reinforcement Learning
Aditya Kapoor, Kale-ab Tessera, Mayank Baranwal +4
Credit assignmen, disentangling each agent's contribution to a shared reward, is a critical challenge in cooperative multi-agent reinforcement learning (MARL). To be effective, cre…
Agent-Temporal Credit Assignment for Optimal Policy Preservation in Sparse Multi-Agent Reinforcement Learning
Aditya Kapoor, Sushant Swamy, Kale-ab Tessera +4
In multi-agent environments, agents often struggle to learn optimal policies due to sparse or delayed global rewards, particularly in long-horizon tasks where it is challenging to…
DeepClean: Integrated Distortion Identification and Algorithm Selection for Rectifying Image Corruptions
Aditya Kapoor, Harshad Khadilkar, Jayvardhana Gubbi
Distortion identification and rectification in images and videos is vital for achieving good performance in downstream vision applications. Instead of relying on fixed trial-and-er…
Transformers are Expressive, But Are They Expressive Enough for Regression?
Swaroop Nath, Harshad Khadilkar, Pushpak Bhattacharyya
Transformers have become pivotal in Natural Language Processing, demonstrating remarkable success in applications like Machine Translation and Summarization. Given their widespread…
Leveraging Domain Knowledge for Efficient Reward Modelling in RLHF: A Case-Study in E-Commerce Opinion Summarization
Swaroop Nath, Tejpalsingh Siledar, Sankara Sri Raghava Ravindra Muddu +8
Reinforcement Learning from Human Feedback (RLHF) has become a dominating strategy in aligning Language Models (LMs) with human values/goals. The key to the strategy is learning a…
Heuristic for Optimisation of Dark Store Facility Locations for Quick Commerce Businesses
Prithvi Kewalramani, Harshad Khadilkar
We present a fast, flexible heuristic for setting up warehouse locations for quick commerce businesses, with the goal of serving the largest number of customers under the constrain…
Reinforcement Replaces Supervision: Query focused Summarization using Deep Reinforcement Learning
Swaroop Nath, Harshad Khadilkar, Pushpak Bhattacharyya
Query-focused Summarization (QfS) deals with systems that generate summaries from document(s) based on a query. Motivated by the insight that Reinforcement Learning (RL) provides a…
Multi-Agent Learning of Efficient Fulfilment and Routing Strategies in E-Commerce
Omkar Shelke, Pranavi Pathakota, Anandsingh Chauhan +3
This paper presents an integrated algorithmic framework for minimising product delivery costs in e-commerce (known as the cost-to-serve or C2S). One of the major challenges in e-co…
Using General Value Functions to Learn Domain-Backed Inventory Management Policies
Durgesh Kalwar, Omkar Shelke, Harshad Khadilkar
We consider the inventory management problem, where the goal is to balance conflicting objectives such as availability and wastage of a large range of products in a store. We propo…
Using Linear Regression for Iteratively Training Neural Networks
Harshad Khadilkar
We present a simple linear regression based approach for learning the weights and biases of a neural network, as an alternative to standard gradient based backpropagation. The pres…
DCT: Dual Channel Training of Action Embeddings for Reinforcement Learning with Large Discrete Action Spaces
Pranavi Pathakota, Hardik Meisheri, Harshad Khadilkar
The ability to learn robust policies while generalizing over large discrete action spaces is an open challenge for intelligent systems, especially in noisy environments that face t…
Supplementing Gradient-Based Reinforcement Learning with Simple Evolutionary Ideas
Harshad Khadilkar
We present a simple, sample-efficient algorithm for introducing large but directed learning steps in reinforcement learning (RL), through the use of evolutionary operators. The met…
Using Contrastive Samples for Identifying and Leveraging Possible Causal Relationships in Reinforcement Learning
Harshad Khadilkar, Hardik Meisheri
A significant challenge in reinforcement learning is quantifying the complex relationship between actions and long-term rewards. The effects may manifest themselves over a long seq…
A Novel Data Augmentation Technique for Out-of-Distribution Sample Detection using Compounded Corruptions
Ramya S. Hebbalaguppe, Soumya Suvra Goshal, Jatin Prakash +2
Modern deep neural network models are known to erroneously classify out-of-distribution (OOD) test data into one of the in-distribution (ID) training classes with high confidence.…
Solving the capacitated vehicle routing problem with timing windows using rollouts and MAX-SAT
Harshad Khadilkar
The vehicle routing problem is a well known class of NP-hard combinatorial optimisation problems in literature. Traditional solution methods involve either carefully designed heuri…
A Learning Based Framework for Handling Uncertain Lead Times in Multi-Product Inventory Management
Hardik Meisheri, Somjit Nath, Mayank Baranwal +1
Most existing literature on supply chain and inventory management consider stochastic demand processes with zero or constant lead times. While it is true that in certain niche scen…
Follow your Nose: Using General Value Functions for Directed Exploration in Reinforcement Learning
Durgesh Kalwar, Omkar Shelke, Somjit Nath +2
Improving sample efficiency is a key challenge in reinforcement learning, especially in environments with large state spaces and sparse rewards. In literature, this is resolved eit…
Learning to Minimize Cost-to-Serve for Multi-Node Multi-Product Order Fulfilment in Electronic Commerce
Pranavi Pathakota, Kunwar Zaid, Anulekha Dhara +4
We describe a novel decision-making problem developed in response to the demands of retail electronic commerce (e-commerce). While working with logistics and retail industry busine…
A simulation driven optimization algorithm for scheduling sorting center operations
Supratim Ghosh, Aritra Pal, Prashant Kumar +4
Parcel sorting operations in logistics enterprises aim to achieve a high throughput of parcels through sorting centers. These sorting centers are composed of large circular conveyo…