Subrata Mitra — accepted AI-conference papers

2026

FlowCast: Trajectory Forecasting for Scalable Zero-Cost Speculative Flow Matching

Divya Jyoti Bajpai, Shubham Agarwal, Apoorv Saxena, Kuldeep Kulkarni, Subrata Mitra, Manjesh Kumar Hanawal

ICLR 2026poster

Flow Matching (FM) has recently emerged as a powerful approach for high-quality visual generation. However, their prohibitively slow inference due to a large number of denoising steps limits their potential use in real-time or interactive applications. Existing acceleration methods, like distillatio…

Cited by 0 Source ↗Scholar ↗

2024

Personalized Federated Learning for Text Classification with Gradient-Free Prompt Tuning

Rui Wang, Tong Yu, Ruiyi Zhang, Sungchul Kim, Ryan Rossi, Handong Zhao +4

NAACL 2024findings

In this paper, we study personalized federated learning for text classification with Pretrained Language Models (PLMs). We identify two challenges in efficiently leveraging PLMs for personalized federated learning: 1) Communication. PLMs are usually large in size, e.g., with hundreds of millions of…

Cited by 2 Source ↗PDF ↗Scholar ↗

2024

ReCON: Training-Free Acceleration for Text-to-Image Synthesis with Retrieval of Concept Prompt Trajectories

Chen-Yi Lu*, Shubham Agarwal, Md Mehrab Tanjim, Kanak Mahadik, Anup Rao, Subrata Mitra +3

ECCV 2024poster

"Text-to-image diffusion models excel in generating photo-realistic images but are hampered by slow processing times. Training-free retrieval-based acceleration methods, which leverage pre-generated “trajectories,” have been introduced to address this. Yet, these methods often lack diversity and fid…

Cited by 3 Source ↗PDF ↗code ↗Scholar ↗

2023

Federated Domain Adaptation for Named Entity Recognition via Distilling with Heterogeneous Tag Sets

Rui Wang, Tong Yu, Junda Wu, Handong Zhao, Sungchul Kim, Ruiyi Zhang +2

ACL 2023findings

Federated learning involves collaborative training with private data from multiple platforms, while not violating data privacy. We study the problem of federated domain adaptation for Named Entity Recognition (NER), where we seek to transfer knowledge across different platforms with data of multiple…

Cited by 5 Source ↗PDF ↗code ↗Scholar ↗

2023

Flash: Concept Drift Adaptation in Federated Learning

Kunjal Panchal, Sunav Choudhary, Subrata Mitra, Koyel Mukherjee, Somdeb Sarkhel, Saayan Mitra +1

ICML 2023poster

In Federated Learning (FL), adaptive optimization is an effective approach to addressing the statistical heterogeneity issue but cannot adapt quickly to concept drifts. In this work, we propose a novel adaptive optimizer called Flash that simultaneously addresses both statistical heterogeneity and t…

Cited by 14 Source ↗PDF ↗Scholar ↗

2023

Reinforced Approximate Exploratory Data Analysis

Shaddy Garg, Subrata Mitra, Tong Yu, Yash Gadhia, Arjun Kashettiwar

AAAI 2023technical

Exploratory data analytics (EDA) is a sequential decision making process where analysts choose subsequent queries that might lead to some interesting insights based on the previous queries and corresponding results. Data processing systems often execute the queries on samples to produce results with…

Cited by 11 Source ↗PDF ↗Scholar ↗

2022

Conditional Generative Model Based Predicate-Aware Query Approximation

Nikhil Sheoran, Subrata Mitra, Vibhor Porwal, Siddharth Ghetia, Jatin Varshney, Tung Mai +2

AAAI 2022technical

The goal of Approximate Query Processing (AQP) is to provide very fast but "accurate enough" results for costly aggregate queries thereby improving user experience in interactive exploration of large datasets. Recently proposed Machine-Learning-based AQP techniques can provide very low latency as qu…

Cited by 17 Source ↗PDF ↗code ↗Scholar ↗

2022

Few-Shot Class-Incremental Learning for Named Entity Recognition

Rui Wang, Tong Yu, Handong Zhao, Sungchul Kim, Subrata Mitra, Ruiyi Zhang +1

ACL 2022long

Previous work of class-incremental learning for Named Entity Recognition (NER) relies on the assumption that there exists abundance of labeled data for the training of new classes. In this work, we study a more challenging but practical problem, i.e., few-shot class-incremental learning for NER, whe…

Cited by 43 Source ↗PDF ↗code ↗Scholar ↗

2022

Root Cause Analysis of Failures in Microservices through Causal Discovery

Muhammad Azam Ikram, Sarthak Chakraborty, Subrata Mitra, Shiv Saini, Saurabh Bagchi, Murat Kocaoglu

NeurIPS 2022accept

Most cloud applications use a large number of smaller sub-components (called microservices) that interact with each other in the form of a complex graph to provide the overall functionality to the user. While the modularity of the microservice architecture is beneficial for rapid software developmen…

Cited by 86 Source ↗PDF ↗Scholar ↗

2021

Scheduling of Time-Varying Workloads Using Reinforcement Learning

Shanka Subhra Mondal, Nikhil Sheoran, Subrata Mitra

AAAI 2021technical

Resource usage of production workloads running on shared compute clusters often fluctuate significantly across time. While simultaneous spike in the resource usage between two workloads running on the same machine can create performance degradation, unused resources in a machine results in wastage a…

Cited by 42 Source ↗PDF ↗Scholar ↗