Machine Learning is a science that comprises algorithms to enable learning and decision making in machines. At MLL we focus on developing theory and further applications of such algorithms.

Research Areas

  • Machine Learning: Robustness, Privacy, Fairness and Scalability, Learning Theory
  • Reinforcement Learning, Online Learning, Self/Semi Supervised Learning
  • Multi-Agent Systems
  • ML or Graphs
  • Geometric Deep Learning, 3D Computer Vision
  • Game Theory and Mechanism Design
  • Blockchains and Distributing Trust

Lab Achievements

  • Multiple papers in prestigious conferences /Journals like AAAI, IJCAI, AAMAS, PAKDD, ICBC, EACL, IJCNN, PRICAI, JAMMAS, etc. in 2023. (10+ A* papers, 30+ reputed publication in a year)
  • PowerTAC Winner: MLL (IIITH-TCS Team) 2022,2021, (runner-up 2018)
  • Best Paper Awards HCII’23, PRICAI’22 (runner up), DAI’21, CODSCOMA’20 (runner-up)

Industry Collaborations

  • Xtraliving: Human Pose Estimation in Fitness
  • Fujitsu Research: Domain Adaptation for 3D Point Clouds
  • MEITY: Unified Blockchain Framework
  • CognitiveScale: Explainable and Robust Al
  • JPMC: Private Federated Learning
  • BEL: Game Theory and Multi Agent Systems
  • KoinEarth: Mechanism Design for Blockchains

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