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[post_date] => 2025-10-23 16:34:25
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We are delighted to announce exSPLORe 2026, a five-day workshop from January 13–17, 2026, traversing the exciting areas of quantitative research in Data Science, Machine Learning, and Artificial Intelligence.
- Jan 13–14: Tutorials on Generative AI, LLMs, and Sequential Learning Theory.
- Jan 15: Industry interactions and panel discussions with Data Science Practitioners.
- Jan 16–17: Research workshop, featuring talks by luminaries, Young Researcher Best Paper Award, and Hackathon Winners.
Venue
Takshila Hall ( AC-02 | LR-007), Ashoka University
To Participate
Graduates, advanced undergraduates, post-docs, and faculty interested in related fields are encouraged to apply.
Limited accommodation is available for outstation participants.
Click here to Register
Last date for registration: November 30, 2025
Confirmed Speakers
| Peter W. Glynn, Stanford University
Ashish Goel, Stanford University
Aditya Gopalan, IISc
Vineet Goyal, Columbia University
Prateek Jain, Google DeepMind
Karthyek Murthy, USC Los Angeles
Dheeraj Nagaraj, Google DeepMind |
Venkat Padmanabhan, Microsoft Research
Alexandre Proutiere, KTH Royal Institute of Technology
Arun Sai Suggala, Google DeepMind
Dootika Vats, IIT Kanpur
Nishith Vishnoi, Yale University
Assaf Zeevi, Columbia University |
Steering committee: V Borkar (IIT Bombay), P Glynn (Stanford), G Iyengar (Columbia), S Juneja (Ashoka University), P Netrapalli (Google DeepMind), A Ramdas (CMU)
Organising committee: V Borkar (IIT Bombay), S Juneja (Ashoka), P Netrapalli (Google DeepMind), Sumegh P (Ashoka), KS Mallikarjuna Rao (IIT Bombay), D Shah (MIT), A Thakkar (Ashoka)
[post_title] => Explorations in Statistics, Probability, Learning and Optimization Research 2026
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[post_content] => Abstract- People routinely reveal their preferences online, e.g., when choosing search results, videos, or products. Such data is used by algorithms to learn human tastes. Recently, curated datasets of human preferences have been used to fine-tune language models, substantially improving their alignment with human intent. These successes raise a natural question: can recommender systems learn more effectively from comparisons rather than ratings? The talk will trace a path from basic models of choice behaviour to new frameworks for recommender systems. The main focus will be on our theoretical result showing that personalised recommendations can be learned efficiently from comparison data, despite the underlying optimisation problem being nonconvex. I will then describe a bandit formulation that addresses the classical exploration-exploitation trade-off in a novel way. Finally, I’ll share empirical insights motivating richer models of human choice. I will conclude by arguing that learning from human preferences is key to building interactive AI systems that reliably serve human needs.
Bio- Suryanarayana Sankagiri is a postdoctoral researcher at EPFL, Switzerland, in the Information and Network Dynamics Laboratory. He obtained his B.Tech. in Electrical Engineering from IIT Bombay in 2016, and his M.S. and Ph.D. in Electrical and Computer Engineering from the University of Illinois Urbana-Champaign in 2018 and 2022. During his Ph.D., he worked on the design and analysis of network protocols—ranging from message-passing algorithms for graph clustering, to consensus mechanisms in blockchains, to pricing protocols for payment channel networks. At EPFL, his research focuses on recommender systems that learn from comparisons and choices, designing new algorithms as well as models that better capture human behaviour. More broadly, his interests lie in understanding modern human-in-the-loop intelligent systems.
Here is a zoom link for online aattendees: https://zoom.us/j/92550826231?pwd=w0KQ9NmqB3DAHxOnlWkYNXUchUbvzA.1
[post_title] => Learning Human Preferences: From Clicks to Conversations
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We are delighted to announce exSPLORe 2026, a five-day workshop from January 13–17, 2026, traversing the exciting areas of quantitative research in Data Science, Machine Learning, and Artificial Intelligence.
- Jan 13–14: Tutorials on Generative AI, LLMs, and Sequential Learning Theory.
- Jan 15: Industry interactions and panel discussions with Data Science Practitioners.
- Jan 16–17: Research workshop, featuring talks by luminaries, Young Researcher Best Paper Award, and Hackathon Winners.
Venue
Takshila Hall ( AC-02 | LR-007), Ashoka University
To Participate
Graduates, advanced undergraduates, post-docs, and faculty interested in related fields are encouraged to apply.
Limited accommodation is available for outstation participants.
Click here to Register
Last date for registration: November 30, 2025
Confirmed Speakers
| Peter W. Glynn, Stanford University
Ashish Goel, Stanford University
Aditya Gopalan, IISc
Vineet Goyal, Columbia University
Prateek Jain, Google DeepMind
Karthyek Murthy, USC Los Angeles
Dheeraj Nagaraj, Google DeepMind |
Venkat Padmanabhan, Microsoft Research
Alexandre Proutiere, KTH Royal Institute of Technology
Arun Sai Suggala, Google DeepMind
Dootika Vats, IIT Kanpur
Nishith Vishnoi, Yale University
Assaf Zeevi, Columbia University |
Steering committee: V Borkar (IIT Bombay), P Glynn (Stanford), G Iyengar (Columbia), S Juneja (Ashoka University), P Netrapalli (Google DeepMind), A Ramdas (CMU)
Organising committee: V Borkar (IIT Bombay), S Juneja (Ashoka), P Netrapalli (Google DeepMind), Sumegh P (Ashoka), KS Mallikarjuna Rao (IIT Bombay), D Shah (MIT), A Thakkar (Ashoka)
[post_title] => Explorations in Statistics, Probability, Learning and Optimization Research 2026
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