(Last Updated: December 2024)
For the latest list of publications, visit: Google Scholar
2024
- LIGHTCODE: Light Analytical and Neural Codes for Channels with Feedback
S Ankireddy, K Narayanan, H Kim
[IEEE Journal on Selected Areas in Communications (JSAC)][code] - Neural Distributed Source Coding
J Whang*, A Nagle*, A Acharya, H Kim, AG Dimakis
[IEEE Journal on Selected Areas in Information Theory (JSAIT)][blog][code] - Fundamental Limits of Prompt Compression: A Rate-Distortion Framework for Black-Box Language Models
A Girish*, A Nagle*, A V Makkuva, M Bondaschi, M Gastpar, H Kim
[NeurIPS 2024, ICML TF2M Workshop Oral 2024][blog][code] - Neural Cover Selection for Image Steganography
K Chahine, H Kim
[NeurIPS 2024][blog][code] - Local to Global: Learning Dynamics and Effect of Initialization for Transformers
AV Makkuva, M Bondaschi, A Girish, A Nagle, H Kim, M Gastpar, C Ekbote
[NeurIPS 2024] - Clustered Federated Learning via Gradients-based Partitioning
Heasung Kim, H. Kim, G. de Veciana
[ICML 2024][blog][code] - DeepPolar: Inventing Nonlinear Large-Kernel Polar Codes via Deep Learning
A Hebbar*, SK Ankireddy*, H. Kim, S. Oh, P. Viswanath
[ICML 2024][blog][code] - LASER: Linear Compression in Wireless Distributed Optimization
A. Makkuva, M. Bondaschi, T. Vogels, M. Jaggi, H. Kim, M. Gastpar
[ICML 2024] - Estimation of Rate-Distortion Function for Computing with Decoder Side Information
Heasung Kim, H Kim, G de Veciana
[ISIT 2024][blog][code] - Deep Learning-Based mmWave Beam Alignment with Only Pilot Channel Measurements
T Lee, H Kim, J Andrews
[ICC 2024] - Deep Learning-based Autodetection of 5G NR mmWave Waveforms
T Lee, A Mahadevan, H Kim, J Andrews
[ICC 2024] - Generating High Dimensional User-Specific Wireless Channels using Diffusion Models
T Lee, J Park, H Kim, J Andrews
[arXiv][blog] - Enhancing K-user Interference Alignment for Discrete Constellations via Learning
Rajesh Mishra, Syed Jafar, Sriram Vishwanath and Hyeji Kim
[arXiv] - Attention with Markov: A Framework for Principled Analysis of Transformers via Markov Chains
A Makkuva, M Bondaschi, A Girish, A Nagle, M Jaggi, H Kim, M Gastpar
[arXiv] - Optimality of the Lossy Körner-Marton Scheme for Distributed Modulo-Two Sum Computation of Doubly Symmetric Binary Sources
S Li, H Kim
[Preprint]
2023
- Task-aware Distributed Source Coding under Dynamic Bandwidth
P Li*, SK Ankireddy*, R Zhao, H Mahjoub, E Pari, U Topcu, S Chinchali, H Kim
[NeurIPS 2023][blog] - Compressed Error HARQ: Feedback Communication on Noise-Asymmetric Channels
SK Ankireddy, SA Hebbar, Y Jiang, P Viswanath, H Kim
[ISIT 2023] - Interpreting Neural Min-Sum Decoders
SK Ankireddy, H Kim
[ICC 2023] - Linear Coding for AWGN channels with Noisy Output Feedback via Dynamic Programming
R. Mishra, D. Vasal, H. Kim
[IEEE Trans. on Information Theory] - DeepIC+: Learning Codes for Interference Channels
K Chahine, Y Jiang, J Cho, H Kim
[IEEE Trans on Wireless Communications][blog]
2022
- Channel Coding via Machine Learning (Book chapter)
H Kim
[Machine Learning and Wireless Communications, Y Eldar, A Goldsmith, D Gunduz, V Poor] - Inventing Codes for Channels with Active Feedback via Deep Learning
K Chahine, R Mishra, H Kim
[IEEE JSAIT 2022] - Learning Variable-Rate Codes for CSI Feedback
H Kim, H Kim, G de Veciana
[GLOBECOM 2022] - TinyTurbo: Efficient Turbo Decoders on Edge
S. Hebbar, R. Mishra, S. Ankireddy, A. Makkuva, H. Kim, P. Viswanath
[ISIT 2022] - Turbo Autoencoder with a Trainable Interleaver
K Chahine, Y Jiang, P Nuti, H Kim, J Cho[ICC 2022]
2021
- A Channel Coding Benchmark for Meta-Learning
R Li, O Bohdal, R Mishra, H Kim, D Li, N Lane, T Hospedales
- DeepIC: Coding for Interference Channels via Deep Learning
K Chahine, N Ye, H Kim
- Distributed Interference Alignment for K -user Interference Channels via Deep Learning
R Mishra, K Chahine, H Kim, S Jafar, S Vishwanath
[ISIT 2021] - Gaussian Channels with Feedback: A Dynamic Programming Approach
R Mishra, D Vasal, H Kim
[ISIT 2021]
2020
- BRP-NAS: Prediction-based NAS using GCNs
T Chau, Ł Dudziak, MS Abdelfattah, R Lee, H Kim, ND Lane
[NeurIPS 2020] - Journey Towards Tiny Perceptual Super-Resolution
R Lee, L Dudziak, M Abdelfattah, S Venieris, H Kim, H Wen, ND Lane
[ECCV 2020] - HAPI: Hardware-Aware Progressive Inference
S Laskaridis, S Venieris, H Kim, N Lane
[ICCAD 2020] - Best of Both Worlds: AutoML Codesign of a CNN and its Hardware Accelerator
M Abdelfattah, Ł Dudziak, T Chau, R Lee, H Kim, N Lane
[Design Automation Conference (DAC) 2020] - Joint Channel Coding and Modulation via Deep Learning
Y Jiang, H Kim, H Asnani, S Kannan, S Oh, P Viswanath
[SPAWC 2020] - Feedback Turbo Autoencoder
Y Jiang, H Kim, H Asnani, S Oh, S Kannan, P Viswanath
[ICASSP 2020] - Physical Layer Communication via Deep Learning
H Kim, S Oh, P Viswanath
[IEEE Journal on Selected Areas in Information Theory (JSAIT) 2020] - LEARN Codes: Inventing Low-Latency Codes via Recurrent Neural Networks
Y Jiang, H Kim, H Asnani, S Kannan, S Oh, P Viswanath
[JSAIT 2020] - Deepcode: Feedback Codes via Deep Learning
H Kim, Y Jiang, S Kannan, S Oh, P Viswanath
[JSAIT 2020]
2019
- Turbo Autoencoder: Deep learning based channel code for point-to-point communication channels
Y. Jiang, H. Kim, H. Asnani, S. Kannan, S. Oh, P. Viswanath
[NeurIPS 2019] [code]
- DeepTurbo: Deep Turbo Decoder
Y. Jiang, H. Kim, H. Asnani, S. Kannan, S. Oh, P. Viswanath
[SPAWC 2019] [code] - MIND: Model Independent Neural Decoder
Y. Jiang, H. Kim, H. Asnani, S. Kannan
[SPAWC 2019] - LEARN Codes: Inventing low-latency codes via recurrent neural networks
Y. Jiang, H. Kim, H. Asnani, S. Kannan, S. Oh, and P. Viswanath
[ICC 2019] [code]
2018
- Deepcode: Feedback Codes via Deep Learning
H. Kim, Y. Jiang, S. Kannan, S. Oh, P. Viswanath
[NeurIPS 2018] [code] - Communication Algorithms via Deep Learning
H. Kim, Y. Jiang, R. Rana, S. Kannan, S. Oh, P. Viswanath,
[ICLR 2018] [code] [blog]
2010-2017
- Discovering potential correlations via hypercontractivity
H. Kim, W. Gao, S. Kannan, S. Oh, and P. Viswanath
[NeurIPS 2017] [Entropy 2017] [code] - On the Optimality of Randomized Time Division and Superposition Coding Inner Bounds for the Broadcast Channel
C. Nair, H. Kim, and A. El Gamal
[ITW 2016] - Capacity Theorems for Broadcast Channels with Two Channel State Components Known at the Receivers
H. Kim and A. El Gamal
[IEEE Transactions on Information Theory 2016] - Superposition Coding is Almost Always Optimal for the Poisson Broadcast Channel
H. Kim, B. Nachman, and A. El Gamal
[IEEE Transactions on Information Theory 2016] - A Note on Broadcast Channels with Stale State Information at the Transmitter
H. Kim, Y. K. Chia, and A. El Gamal
[IEEE Transactions on Information Theory 2015] - Superposition Coding is Almost Always Optimal for the Poisson Broadcast Channel
H. Kim, B. Nachman, and A. El Gamal
[ISIT 2015] (semi-plenary session) - Capacity Region of the Broadcast Channel with Two Deterministic Channel State Components
H. Kim and A. El Gamal
[ISIT 2014] - Greedy Local Routing Strategy for Autonomous Global Load Balancing Based on Three-Dimensional Potential Field
S. Jung, J. Sung, Y. Bang, M.Kserawi, H. Kim, J.-K.K. Lee
[IEEE Communications Letters 2010]