On additive-combinatorial affine inequalities for Shannon entropy and differential entropy. - Ashok Vardhan Makkuva; Yihong Wu 001 (ISIT, 2016)

Globally Consistent Algorithms for Mixture of Experts. - Ashok Vardhan Makkuva; Sreeram Kannan; Pramod Viswanath (CoRR, 2018)

Equivalence of Additive-Combinatorial Linear Inequalities for Shannon Entropy and Differential Entropy. - Ashok Vardhan Makkuva; Yihong Wu 001 (IEEE Trans. Inf. Theory, 2018)

Learning One-hidden-layer Neural Networks under General Input Distributions. - Weihao Gao; Ashok Vardhan Makkuva; Sewoong Oh; Pramod Viswanath (AISTATS, 2019)

Breaking the gridlock in Mixture-of-Experts: Consistent and Efficient Algorithms. - Ashok Vardhan Makkuva; Pramod Viswanath; Sreeram Kannan; Sewoong Oh (ICML, 2019)

Barracuda: The Power of ℓ-polling in Proof-of-Stake Blockchains. - Giulia Fanti; Jiantao Jiao; Ashok Vardhan Makkuva; Sewoong Oh; Ranvir Rana; Pramod Viswanath (MobiHoc, 2019)

Learning in Gated Neural Networks. - Ashok Vardhan Makkuva; Sewoong Oh; Sreeram Kannan; Pramod Viswanath (AISTATS, 2020)

Optimal transport mapping via input convex neural networks. - Ashok Vardhan Makkuva; Amirhossein Taghvaei; Sewoong Oh; Jason D. Lee (ICML, 2020)

KO codes: inventing nonlinear encoding and decoding for reliable wireless communication via deep-learning. - Ashok Vardhan Makkuva; Xiyang Liu; Mohammad Vahid Jamali; Hessam Mahdavifar; Sewoong Oh; Pramod Viswanath (ICML, 2021)

Reed-Muller Subcodes: Machine Learning-Aided Design of Efficient Soft Recursive Decoding. - Mohammad Vahid Jamali; Xiyang Liu; Ashok Vardhan Makkuva; Hessam Mahdavifar; Sewoong Oh; Pramod Viswanath (ISIT, 2021)

TinyTurbo: Efficient Turbo Decoders on Edge. - S. Ashwin Hebbar; Rajesh K. Mishra; Sravan Kumar Ankireddy; Ashok Vardhan Makkuva; Hyeji Kim; Pramod Viswanath (ISIT, 2022)

CRISP: Curriculum based Sequential neural decoders for Polar code family. - S. Ashwin Hebbar; Viraj Vivek Nadkarni; Ashok Vardhan Makkuva; Suma Bhat; Sewoong Oh; Pramod Viswanath (ICML, 2023)

Machine Learning-Aided Efficient Decoding of Reed-Muller Subcodes. - Mohammad Vahid Jamali; Xiyang Liu; Ashok Vardhan Makkuva; Hessam Mahdavifar; Sewoong Oh; Pramod Viswanath (IEEE J. Sel. Areas Inf. Theory, 2023)

LASER: Linear Compression in Wireless Distributed Optimization. - Ashok Vardhan Makkuva; Marco Bondaschi; Thijs Vogels; Martin Jaggi; Hyeji Kim; Michael Gastpar (ICML, 2024)

Local to Global: Learning Dynamics and Effect of Initialization for Transformers. - Ashok Vardhan Makkuva; Marco Bondaschi; Adway Girish; Alliot Nagle; Hyeji Kim; Michael Gastpar; Chanakya Ekbote (NeurIPS, 2024)

Fundamental Limits of Prompt Compression: A Rate-Distortion Framework for Black-Box Language Models. - Alliot Nagle; Adway Girish; Marco Bondaschi; Michael Gastpar; Ashok Vardhan Makkuva; Hyeji Kim (NeurIPS, 2024)

Transformers on Markov data: Constant depth suffices. - Nived Rajaraman; Marco Bondaschi; Ashok Vardhan Makkuva; Kannan Ramchandran; Michael Gastpar (NeurIPS, 2024)

Attention with Markov: A Framework for Principled Analysis of Transformers via Markov Chains. - Ashok Vardhan Makkuva; Marco Bondaschi; Adway Girish; Alliot Nagle; Martin Jaggi; Hyeji Kim; Michael Gastpar (CoRR, 2024)

Attention with Markov: A Curious Case of Single-layer Transformers. - Ashok Vardhan Makkuva; Marco Bondaschi; Adway Girish; Alliot Nagle; Martin Jaggi; Hyeji Kim; Michael Gastpar (ICLR, 2025)

From Markov to Laplace: How Mamba In-Context Learns Markov Chains. - Marco Bondaschi; Nived Rajaraman; Xiuying Wei; Kannan Ramchandran; Razvan Pascanu; Caglar Gulcehre; Michael Gastpar; Ashok Vardhan Makkuva (CoRR, 2025)

What One Cannot, Two Can: Two-Layer Transformers Provably Represent Induction Heads on Any-Order Markov Chains. - Chanakya Ekbote; Marco Bondaschi; Nived Rajaraman; Jason D. Lee; Michael Gastpar; Ashok Vardhan Makkuva; Paul Pu Liang (CoRR, 2025)