Kulin Shah

I am a Ph.D. student in Computer Science department at the University of Texas Austin advised by Prof. Adam Klivans. My research interest broadly lies in generative models. I like to understand (empirically and theoretically) building blocks of modern generative models with the hope of improving them.

I was a student researcher at Google with Rina Panigrahy working on understanding and improving reasoning capabilities of language models during June 2023-March 2024. Before joining Ph.D., I was a research fellow at Microsoft Research India, where I worked with Amit Deshpande and Navin Goyal. I graduated with my B.Tech. (Hons.) in Computer Science from IIIT-Hyderabad, where I worked with Naresh Manwani.


Papers

(α-β denotes alphabetical order and * denotes equal contribution)



Causal Language Modeling can Elicit Search and Reasoning Capabilities on Logic Puzzles PDF
Kulin Shah, Nishanth Dikkala, Xin Wang, Rina Panigrahy NeurIPS 2024 | Conference on Neural Information Processing Systems

Unrolled denoising networks provably learn optimal Bayesian inference PDF
Aayush Karan*, Kulin Shah*, Sitan Chen, Yonina C. Eldar NeurIPS 2024 | Conference on Neural Information Processing Systems

Learning general Gaussian mixtures with efficient score matching PDF
(α-β) Sitan Chen, Vasilis Kontonis, Kulin Shah
Preprint

Simple Mechanisms for Representing, Indexing and Manipulating Concepts PDF
(α-β)Yuanzhi Li, Raghu Meka, Rina Panigrahy, Kulin Shah
Preprint

Learning Mixtures of Gaussians Using the DDPM Objective PDF Poster
Kulin Shah, Sitan Chen, Adam Klivans
NeurIPS 2023 | Conference on Neural Information Processing Systems

Ambient Diffusion: Learning Clean Distributions From Corrupted Data PDF Code
Giannis Daras, Kulin Shah, Yuval Dagan, Aravind Gollakota, Alexandros G. Dimakis, Adam Klivans NeurIPS 2023 | Conference on Neural Information Processing Systems

Learning and Generalization in Overparameterized Normalizing Flows PDF Poster Code
Kulin Shah, Amit Deshpande, Navin Goyal AISTATS 2022 | International Conference on Artificial Intelligence and Statistics

RISAN: Robust Instance Specific Deep Abstention Network PDF
Bhavya Kalra, Kulin Shah, Naresh Manwani
UAI, 2021 | Conference on Uncertainty in Artificial Intelligence
Selected for Oral presentation

Rawlsian Fair Adaptation of Deep Learning Classifiers PDF
Kulin Shah, Pooja Gupta, Amit Deshpande, Chiranjib Bhattacharyya
AIES, 2021 | AAAI/ACM Conference on AI, Ethics, and Society

Online Active Learning of Reject Option Classifiers PDF
Kulin Shah, Naresh Manwani
AAAI, 2020 | AAAI Conference on Artificial Intelligence
Selected for Oral presentation

Sparse Reject Option Classifier using Successive Linear Programming PDF
Kulin Shah, Naresh Manwani
AAAI, 2019 | AAAI Conference on Artificial Intelligence
Selected for Oral presentation

Ingredients for happiness: Modeling constructs via semi-supervised content driven inductive transfer learning PDF
Kulin Shah, Naresh Manwani
AFFCON workshop @ AAAI, 2019 | AAAI-19 Workshop on affective content analysis

PLUME: Polyhedral Learning Using Mixture of Experts PDF
Kulin Shah, PS Sastry, Naresh Manwani
Preprint




Wepage inspired by minimal research theme.

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