3 Search Results for "Kumar, Ananya"

Document
DOI: 10.4230/LIPIcs.FORC.2026.7
When to Ask a Question: Understanding Communication Strategies in Generative AI Tools

Authors: Charlotte Park, Kate Donahue, and Manish Raghavan

Published in: LIPIcs, Volume 368, 7th Symposium on Foundations of Responsible Computing (FORC 2026)

Abstract
Generative AI models differ from traditional machine learning tools in that they allow users to provide as much or as little information as they choose in their inputs. This flexibility often leads users to omit certain details, relying on the models to infer and fill in under-specified information based on distributional knowledge of user preferences. Such inferences may privilege majority viewpoints and disadvantage users with atypical preferences, raising concerns about fairness. Unlike more traditional recommender systems, LLMs can explicitly solicit more information from users through natural language. However, while directly eliciting user preferences could increase personalization and mitigate inequality, excessive querying places a burden on users who value efficiency. We develop a stylized model of user-LLM interaction and develop an objective that captures tradeoff between user burden and preference representation. Building on the observation that individual preferences are often correlated, we analyze how AI systems should balance inference and elicitation, characterizing the optimal amount of information to solicit before content generation. Ultimately, we show that information elicitation can mitigate the systematic biases of preference inference, enabling the design of generative tools that better incorporate diverse user perspectives while maintaining efficiency. We complement this theoretical analysis with an empirical evaluation illustrating the model’s predictions and exploring their practical implications.
Cite as

Charlotte Park, Kate Donahue, and Manish Raghavan. When to Ask a Question: Understanding Communication Strategies in Generative AI Tools. In 7th Symposium on Foundations of Responsible Computing (FORC 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 368, pp. 7:1-7:25, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

Copy BibTex To Clipboard

@InProceedings{park_et_al:LIPIcs.FORC.2026.7,
  author =	{Park, Charlotte and Donahue, Kate and Raghavan, Manish},
  title =	{{When to Ask a Question: Understanding Communication Strategies in Generative AI Tools}},
  booktitle =	{7th Symposium on Foundations of Responsible Computing (FORC 2026)},
  pages =	{7:1--7:25},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-419-2},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{368},
  editor =	{Lin, Huijia (Rachel)},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2026.7},
  URN =		{urn:nbn:de:0030-drops-259782},
  doi =		{10.4230/LIPIcs.FORC.2026.7},
  annote =	{Keywords: human-AI interaction, user modeling, personalization}
}
Document
DOI: 10.4230/LIPIcs.ICALP.2018.21
Approximate Convex Hull of Data Streams

Authors: Avrim Blum, Vladimir Braverman, Ananya Kumar, Harry Lang, and Lin F. Yang

Published in: LIPIcs, Volume 107, 45th International Colloquium on Automata, Languages, and Programming (ICALP 2018)

Abstract
Given a finite set of points P subseteq R^d, we would like to find a small subset S subseteq P such that the convex hull of S approximately contains P. More formally, every point in P is within distance epsilon from the convex hull of S. Such a subset S is called an epsilon-hull. Computing an epsilon-hull is an important problem in computational geometry, machine learning, and approximation algorithms. In many applications, the set P is too large to fit in memory. We consider the streaming model where the algorithm receives the points of P sequentially and strives to use a minimal amount of memory. Existing streaming algorithms for computing an epsilon-hull require O(epsilon^{(1-d)/2}) space, which is optimal for a worst-case input. However, this ignores the structure of the data. The minimal size of an epsilon-hull of P, which we denote by OPT, can be much smaller. A natural question is whether a streaming algorithm can compute an epsilon-hull using only O(OPT) space. We begin with lower bounds that show, under a reasonable streaming model, that it is not possible to have a single-pass streaming algorithm that computes an epsilon-hull with O(OPT) space. We instead propose three relaxations of the problem for which we can compute epsilon-hulls using space near-linear to the optimal size. Our first algorithm for points in R^2 that arrive in random-order uses O(log n * OPT) space. Our second algorithm for points in R^2 makes O(log(epsilon^{-1})) passes before outputting the epsilon-hull and requires O(OPT) space. Our third algorithm, for points in R^d for any fixed dimension d, outputs, with high probability, an epsilon-hull for all but delta-fraction of directions and requires O(OPT * log OPT) space.
Cite as

Avrim Blum, Vladimir Braverman, Ananya Kumar, Harry Lang, and Lin F. Yang. Approximate Convex Hull of Data Streams. In 45th International Colloquium on Automata, Languages, and Programming (ICALP 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 107, pp. 21:1-21:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

Copy BibTex To Clipboard

@InProceedings{blum_et_al:LIPIcs.ICALP.2018.21,
  author =	{Blum, Avrim and Braverman, Vladimir and Kumar, Ananya and Lang, Harry and Yang, Lin F.},
  title =	{{Approximate Convex Hull of Data Streams}},
  booktitle =	{45th International Colloquium on Automata, Languages, and Programming (ICALP 2018)},
  pages =	{21:1--21:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-076-7},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{107},
  editor =	{Chatzigiannakis, Ioannis and Kaklamanis, Christos and Marx, D\'{a}niel and Sannella, Donald},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2018.21},
  URN =		{urn:nbn:de:0030-drops-90254},
  doi =		{10.4230/LIPIcs.ICALP.2018.21},
  annote =	{Keywords: Convex Hulls, Streaming Algorithms, Epsilon Kernels, Sparse Coding}
}
Document
DOI: 10.4230/DagSemProc.04461.18
Optimizing Surface Profiles during Hot Rolling: A Genetic Algorithms based Multi-objective Analysis

Authors: Nirupam Chakraborti, Barrenkala Siva Kumar, Satish V. Babu, Sri Subhrangshu Moitra, and Ananya Mukhopadhyay

Published in: Dagstuhl Seminar Proceedings, Volume 4461, Practical Approaches to Multi-Objective Optimization (2005)

Abstract
A hot rolled strip produced by any integrated steel plant would require satisfying some stringent requirements of its surface profile. Crown and Flatness are two industrially accepted quantifiers that relate to the geometric tolerances in the rolled strips. This study attempts to regulate both crown and flatness within an acceptable limit, satisfying more than one objective at a time. Mathematically, this leads to a multi-objective optimization problem where the solution is no longer unique and a family of equally feasible solutions leads to the so called Pareto-Front, where each member is simply as good as the others. To implement this concept in the present context, one needs to realize that the surface deformation, which is ultimately imparted to the rolled sheets, comes from more than one source. The wear of the rolls, their thermal expansion, bending, and also deformation, contribute significantly towards the crown and flatness that is ultimately observed. During this study a detailed mathematical model has been worked out for this process incorporating all of these phenomena. Computation for the Pareto-optimality has been carried out using different forms of biologically inspired Genetic Algorithms, often integrated with an Ant Colony Optimization Scheme. Ultimately the model has been fine tuned for the hot rolling practice in a major integrated steel plant and tested against their actual operational data.
Cite as

Nirupam Chakraborti, Barrenkala Siva Kumar, Satish V. Babu, Sri Subhrangshu Moitra, and Ananya Mukhopadhyay. Optimizing Surface Profiles during Hot Rolling: A Genetic Algorithms based Multi-objective Analysis. In Practical Approaches to Multi-Objective Optimization. Dagstuhl Seminar Proceedings, Volume 4461, pp. 1-12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2005)

Copy BibTex To Clipboard

@InProceedings{chakraborti_et_al:DagSemProc.04461.18,
  author =	{Chakraborti, Nirupam and Kumar, Barrenkala Siva and Babu, Satish V. and Moitra, Sri Subhrangshu and Mukhopadhyay, Ananya},
  title =	{{Optimizing Surface Profiles during Hot Rolling: A Genetic Algorithms based Multi-objective Analysis}},
  booktitle =	{Practical Approaches to Multi-Objective Optimization},
  pages =	{1--12},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2005},
  volume =	{4461},
  editor =	{J\"{u}rgen Branke and Kalyanmoy Deb and Kaisa Miettinen and Ralph E. Steuer},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.04461.18},
  URN =		{urn:nbn:de:0030-drops-2454},
  doi =		{10.4230/DagSemProc.04461.18},
  annote =	{Keywords: Rolling, Hot Rolling, Crown, Flatness, Genetic Algorithms, Ant Colony Optimization, Multi-objective Optimization, Pareto Front, Multi-objective Evolut}
}
  • Refine by Type
  • 3 Document/PDF
  • 1 Document/HTML

  • Refine by Publication Year
  • 1 2026
  • 1 2018
  • 1 2005

  • Refine by Author
  • 1 Babu, Satish V.
  • 1 Blum, Avrim
  • 1 Braverman, Vladimir
  • 1 Chakraborti, Nirupam
  • 1 Donahue, Kate
  • Show More...

  • Refine by Series/Journal
  • 2 LIPIcs
  • 1 DagSemProc

  • Refine by Classification
  • 1 Computing methodologies → Artificial intelligence
  • 1 Human-centered computing → Interaction paradigms
  • 1 Human-centered computing → User models
  • 1 Theory of computation → Computational geometry
  • 1 Theory of computation → Sketching and sampling
  • Show More...

  • Refine by Keyword
  • 1 Ant Colony Optimization
  • 1 Convex Hulls
  • 1 Crown
  • 1 Epsilon Kernels
  • 1 Flatness
  • Show More...

Any Issues?
X

Feedback on the Current Page

CAPTCHA

Thanks for your feedback!

Feedback submitted to Dagstuhl Publishing

Could not send message

Please try again later or send an E-mail
© 2023-2026 Schloss Dagstuhl – LZI GmbH Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact