Conference papers

• Insufficient Statistics Perturbation: Stable Estimators for Private Least Squares
  Gavin Brown, Jonathan Hayase, Samuel Hopkins, Weihao Kong, Xiyang Liu, Sewoong Oh, Juan C. Perdomo, Adam Smith
  working paper
  

• Uncertainty Quantification with User-level Differential Privacy
  Abhradeep Guha Thakurta, Dj Dvijotham, Georgie Evans, Peter Kairouz, Ryan McKenna, Sewoong Oh
  working paper
  

  • present at Theory and Practice of Differential Privacy 2023

• Profit: Benchmarking Personalization and Robustness Trade-off in Federated Prompt Tuning
  Collins, Liam, Shanshan Wu, Sewoong Oh, and Khe Chai Sim
  working paper
  

  • present at International Workshop on Federated Learning in the Age of Foundation Models in Conjunction with NeurIPS 2023

• MAUVE Scores for Generative Models: Theory and Practice
  Krishna Pillutla, Lang Liu, John Thickstun, Sean Welleck, Swabha Swayamdipta, Rowan Zellers, Sewoong Oh, Yejin Choi, Zaid Harchaoui
  working paper

• Stochastic optimization on matrices and a graphon McKean-Vlasov limit
  Zaid Harchaoui, Sewoong Oh, Soumik Pal, Raghav Somani, Raghavendra Tripathi
  working paper,

• DPZero: Private Fine-Tuning of Language Models without Backpropagation
  Liang Zhang, Bingcong Li, Kiran Koshy Thekumparampil, Sewoong Oh, Niao He
  ICML, 2024
  

  • slides from my talk at CSL student conference at UIUC are available here

  • slides from my talk at IMS International Conference on Statistics and Data Science (ICSDS) 2023 are available here

  • a workshop version presented at NeurIPS 2023 workshop on Federated Learning in the Age of Foundation Models

    • DPZero: Dimension-Independent and Differentially Private Zeroth-Order Optimization, Liang Zhang, Kiran Koshy Thekumparampil, Sewoong Oh, Niao He
      

• Privacy-Preserving Instructions for Aligning Large Language Models
  Da Yu, Peter Kairouz, Sewoong Oh, Zheng Xu
  ICML, 2024
  

• DeepPolar: Inventing Nonlinear Large-Kernel Polar Codes via Deep Learning
  S Ashwin Hebbar, Sravan Kumar Ankireddy, Hyeji Kim, Sewoong Oh, Pramod Viswanath
  ICML, 2024
  

• Can Public Large Language Models Help Private Cross-device Federated Learning?
  Boxin Wang, Yibo Jacky Zhang, Yuan Cao, Bo Li, H Brendan McMahan, Sewoong Oh, Zheng Xu, Manzil Zaheer
  NAACL, 2024
  

  • present at ICML 2023 workshop on Efficient Systems for Foundation Models

  • present at ICML 2023 workshop on Challenges of Deploying Generative AI

• One-shot Empirical Privacy Estimation for Federated Learning
  Andrew, Galen, Peter Kairouz, Sewoong Oh, Alina Oprea, H. Brendan McMahan, and Vinith Suriyakumar
  ICLR 2024, (Oral presentation)
  

  • International Workshop on Federated Learning in the Age of Foundation Models in Conjunction with NeurIPS 2023

• Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment
  Rachel Cummings, Damien Desfontaines, David Evans, Roxana Geambasu, Matthew Jagielski, Yangsibo Huang, Peter Kairouz, Gautam Kamath, Sewoong Oh, Olga Ohrimenko, Nicolas Papernot, Ryan Rogers, Milan Shen, Shuang Song, Weijie Su, Andreas Terzis, Abhradeep Thakurta, Sergei Vassilvitskii, Yu-Xiang Wang, Li Xiong, Sergey Yekhanin, Da Yu, Huanyu Zhang, Wanrong Zhang
  Harvard Data Science Review, 6(1), January 2024
  

  • In July 2022, we hosted a workshop titled “Differential Privacy (DP): Challenges towards the Next Frontier” with experts from industry, academia, and the public sector to discuss and find solutions to the challenges of differential privacy. This is a report from that workshop.

• Label Poisoning is All You Need
  Rishi D. Jha, Jonathan Hayase, Sewoong Oh
  NeurIPS, 2023
  

  • presentation at NeurIPS 2023 workshop on Federated Learning in the Age of Foundation Models

• Unleashing the power of randomization in auditing differentially private ML
  Krishna Pillutla, Galen Andrew, Peter Kairouz, Brendan McMahan, Alina Oprea, Sewoong Oh
  NeurIPS 2023
  

  • presented at Boston Privacy Day, Google, and MSR

  • presented at ICML 2023 Federated Learning workshop

  • Presented at CRYPTO 2023 Privacy Preserving Machine Learning (PPML) workshop

• Near Optimal Private and Robust Linear Regression
  Xiyang Liu, Prateek Jain, Weihao Kong, Sewoong Oh, Arun Sai Suggala
  NeurIPS 2023

• Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks
  Arun Ganesh, Daogao Liu, Sewoong Oh, Abhradeep Thakurta
  NeurIPS 2023 (Spotlight presentation)

• On the Connection between Pre-training Data Diversity and Fine-tuning Robustness
  Vivek Ramanujan, Thao Nguyen, Sewoong Oh, Ludwig Schmidt, Ali Farhadi
  NeurIPS 2023 (Spotlight presentation)

• Improving multimodal datasets with image captioning
  Thao Nguyen, Samir Yitzhak Gadre, Gabriel Ilharco, Sewoong Oh, Ludwig Schmidt
  NeurIPS 2023, Datasets and Benchmarks Track
  

  • presentation at ICML 2023 workshop on Data-centric Machine Learning Research (DMLR)

• DataComp: In search of the next generation of multimodal datasets
  Samir Yitzhak Gadre, Gabriel Ilharco, Alex Fang, Jonathan Hayase, Georgios Smyrnis, Thao Nguyen, Ryan Marten, Mitchell Wortsman, Dhruba Ghosh, Jieyu Zhang, Eyal Orgad, Rahim Entezari, Giannis Daras, Sarah Pratt, Vivek Ramanujan, Yonatan Bitton, Kalyani Marathe, Stephen Mussmann, Richard Vencu, Mehdi Cherti, Ranjay Krishna, Pang Wei Koh, Olga Saukh, Alexander Ratner, Shuran Song, Hannaneh Hajishirzi, Ali Farhadi, Romain Beaumont, Sewoong Oh, Alex Dimakis, Jenia Jitsev, Yair Carmon, Vaishaal Shankar, Ludwig Schmidt
  NeurIPS 2023, Datasets and Benchmarks Track (Oral presentation)
  

  • check out our benchmark competition at datacomp.ai

• Private federated learning with autotuned compression
  Enayat Ullah, Christopher Choquette, Peter Kairouz, Sewoong Oh
  ICML 2023,

• Why Is Public Pretraining Necessary for Private Model Training?
  Arun Ganesh, Mahdi Haghifam, Milad Nasr, Sewoong Oh, Thomas Steinke, Om Thakkar, Abhradeep Thakurta, Lun Wang
  ICML 2023,

• CRISP: Curriculum based Sequential Neural Decoders for Polar Code Family
  S Ashwin Hebbar, Viraj Nadkarni, Ashok Vardhan Makkuva, Suma Bhat, Sewoong Oh, Pramod Viswanath
  ICML 2023,

• Learning to Generate Image Embeddings with User-level Differential Privacy
  Zheng Xu, Maxwell Collins, Yuxiao Wang, Liviu Panait, Sewoong Oh, Sean Augenstein, Ting Liu, Florian Schroff, H. Brendan McMahan
  CVPR 2023,

• Few-shot Backdoor Attacks via Neural Tangent Kernels
  Jonathan Hayase, Sewoong Oh
  ICLR, 2023

  • poster presented at NeurIPS 2022 workshop on Trustworthy and Socially Responsible Machine Learning (TSRML)

• Zonotope Domains for Lagrangian Neural Network Verification
  Matt Jordan, Jonathan Hayase, Alexandros G Dimakis, Sewoong Oh
  NeurIPS 2022,

• Quality Not Quantity: On the Interaction between Dataset Design and Robustness of CLIP
  Thao Nguyen, Gabriel Ilharco, Mitchell Wortsman, Sewoong Oh, Ludwig Schmidt
  NeurIPS 2022, (Oral presentation)

  • presented at ICML 2022 workshop on Benchmarking Data for Data-Centric AI (DataPerf)

• Bring Your Own Algorithm for Optimal Differentially Private Stochastic Minimax Optimization
  Liang Zhang, Kiran Koshy Thekumparampil, Sewoong Oh, Niao He
  NeurIPS 2022,

• DP-PCA: Statistically Optimal and Differentially Private PCA
  Xiyang Liu, Weihao Kong, Prateek Jain, Sewoong Oh
  NeurIPS 2022,

• MAML and ANIL Provably Learn Representations
  Liam Collins, Aryan Mokhtari, Sewoong Oh, Sanjay Shakkottai
  ICML, 2022

  • video of a talk on Dec 2022 at C3 Digital Transformation Institute is available here

  • slides from my talk at C3 Digital Transformation Institute is available here

  • 5 minutes presentation by Liam Collins at ICML is available here

• De novo mass spectrometry peptide sequencing with a transformer model
  Melih Yilmaz, William E. Fondrie, Wout Bittremieux, Sewoong Oh, William Stafford Noble
  ICML, 2022

  • 5 minutes presentation by Melih Yilmaz at ICML is available here

• Differential privacy and robust statistics in high dimensions
  Xiyang Liu, Weihao Kong, Sewoong Oh
  COLT, 2022

  • Presented at the third AAAI Workshop on Privacy-Preserving Artificial Intelligence (PPAI-22), the recording of a 12 minute presentation is available here

  • video of a talk on Nov 2021 at SNAPP seminar series is available here

  • slides from my talk at SNAPP seminar is available here

  • slides from my talk at Google is available here

• Lifted Primal-Dual Method for Bilinearly Coupled Smooth Minimax Optimization
  Kiran Koshy Thekumparampil, Niao He, Sewoong Oh
  AISTATS, 2022 (Oral presentation)

  • video of a presention on Feb 2022 at Simons institute by Niao He is here

• Eliminating Sharp Minima from SGD with Truncated Heavy-tailed Noise
  Xingyu Wang, Sewoong Oh, Chang-Han Rhee
  ICLR, 2022

  • presented at DeepMath 2021

  • video of a talk Xingyu gave at DeepMath is available here

  • slides from Xingyu's talk is available here

  • video of a talk on Feb 2022 at SNAPP seminar series by Chang-han is here

• FedChain: Chained Algorithms for Near-Optimal Communication Cost in Federated Learning
  Charlie Hou, Kiran K. Thekumparampil, Giulia Fanti, Sewoong Oh
  ICLR, 2022

  • presented at the ICML 2021 Workshop on Federated Learning for User Privacy and Data Confidentiality (ICML-FL 2021)

• Robust and Differentially Private Mean Estimation
  Xiyang Liu, Weihao Kong, Sham Kakade, Sewoong Oh
  NeurIPS 2021,

  • presented at the ICML 2021 Workshop on Federated Learning for User Privacy and Data Confidentiality (ICML-FL 2021)

  • presented at the CCS 2021 workshop Privacy Preserving Machine Learning (PPML’21)

  • video of a talk on Oct 2021 at Simons Institute is available here

  • slides from my talk is available here

  • code is available here

• Sample Efficient Linear Meta-Learning by Alternating Minimization
  Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh
  NeurIPS, 2021

• Divergence Frontiers for Generative Models: Sample Complexity, Quantization Level, and Frontier Integral
  Lang Liu, Krishna Pillutla, Sean Welleck, Sewoong Oh, Yejin Choi, Zaid Harchaoui
  NeurIPS, 2021

• Gradient Inversion with Generative Image Prior
  Jaechang Kim, Jinwoo Jeon, Kangwook Lee, Sewoong Oh, and Jungseul Ok,
  NeurIPS, 2021

  • presented at the ICML 2021 Workshop on Federated Learning for User Privacy and Data Confidentiality (ICML-FL 2021)

• SPECTRE: Defending against backdoor attacks using robust covariance estimation
  Jonathan Hayase, Weihao Kong, Raghav Somani, Sewoong Oh
  ICML, 2021
  

  • you can find the code for SPECTRE here.

  • Backdoor-Suite: a user-friendly repository for backdoor attacks and defenses is available here

• KO codes: Inventing Nonlinear Encoding and Decoding for Reliable WirelessCommunication via Deep-Learning
  Ashok Vardhan Makkuva, Xiyang Liu, Mohammad Vahid Jamali, Hessam Mahdavifar, Sewoong Oh, Pramod Viswanath
  ICML, 2021
  

  • slides from a talk by Ashok is available here: 5 min and 1 hour

• 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

• Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method
  Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh
  NeurIPS, 2020 (Spotlight presentation)

• Robust Meta-learning for Mixed Linear Regression with Small Batches
  Weihao Kong, Raghav Somani, Sham Kakade, Sewoong Oh
  NeurIPS, 2020

  • you can find the code for Robust PCA here.

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

• Meta-learning for mixed linear regression
  Weihao Kong, Raghav Somani, Zhao Song, Sham Kakade, Sewoong Oh
  ICML, 2020

• InfoGAN-CR and ModelCentrality: Self-supervised Model Training and Selection for Disentangling GANs
  Zinan Lin, Kiran Koshy Thekumparampil, Giulia Fanti, Sewoong Oh
  ICML, 2020

• Learning in Gated Neural Networks
  Ashok Vardhan Makkuva, Sewoong Oh, Sreeram Kannan, and Pramod Viswanath
  AISTATS, 2020

• Turbo Autoencoder: Deep learning based channel code for point-to-point communication channels
  Yihan Jiang, Hyeji Kim, Himanshu Asnani, Sreeram Kannan, Sewoong Oh, Pramod Viswanath
  NeurIPS, 2019

• Efficient Algorithms for Smooth Minimax Optimization
  Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh
  NeurIPS, 2019
  

  • you can find the code for DIAG here

• Minimax Rates of Estimating Approximate Differential Privacy
  Xiyang Liu, Sewoong Oh
  NeurIPS, 2019

• Rate Distortion For Model Compression:From Theory To Practice
  Weihao Gao, Yu-Han Liu, Chong Wang, Sewoong Oh
  ICML, 2019

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

• DeepTurbo: Deep Turbo Decoder
  Yihan Jiang, Hyeji Kim, Himanshu Asnani, Sreeram Kannan, Sewoong Oh, and Pramod Viswanath
  2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2019

• Barracuda: The Power of l-polling in Proof-of-Stake Blockchains
  G Fanti, J Jiao, A Makkuva, S Oh, R Rana, and P Viswanath
  ACM MobiHoc, 2019, (Best paper award)

• LEARN Codes: Inventing Low-latency Codes via Recurrent Neural Networks
  Yihan Jiang, Hyeji Kim, Himanshu Asnani, Sreeram Kannan, Sewoong Oh, and Pramod Viswanath
  IEEE International Conference on Communications (ICC), 2019

• Learning One-hidden-layer Neural Networks under General Input Distributions
  Weihao Gao, Ashok Vardhan Makkuva, Sewoong Oh, and Pramod Viswanath
  AISTATS, 2019

• Iterative Bayesian Learning for Crowdsourced Regression
  Jungseul Ok, Yunhun Jang, Sewoong Oh, Jinwoo Shin, Yung Yi
  AISTATS, 2019, [ code ]

• Compounding of Wealth in Proof-of-Stake Cryptocurrencies
  Giulia Fanti, Leonid Kogan, Sewoong Oh, Kathleen Ruan, Pramod Viswanath, and Gerui Wang
  Financial Cryptography and Data Security, 2019

• Robustness of conditional GANs to noisy labels
  Kiran Thekumparampil, Ashish Khetan, Zinan Lin, Sewoong Oh
  NIPS, 2018 (Spotlight presentation), [ code ]

• Deepcode: Feedback Codes via Deep Learning
  Hyeji Kim, Yihan Jiang, Sreeram Kannan, Sewoong Oh, Pramod Viswanath
  NIPS, 2018, [ code by Hyeji Kim ], [ code by Yihan Jiang ]

• PacGAN: The power of two samples in generative adversarial networks
  Zinan Lin, Ashish Khetan, Giulia Fanti, Sewoong Oh
  NIPS, 2018, [ code ], [ project page]

• Communication Algorithms via Deep Learning
  H. Kim, Y. Jiang, R. B. Rana, S. Kannan, S. Oh, and P. Viswanath
  ICLR, 2018

• Estimating Mutual Information for Discrete-Continuous Mixtures
  Weihao Gao, Sreeram Kannan, Sewoong Oh, and Pramod Viswanath
  NIPS, 2017 (Spotlight presentation) [ code ]

• Discovering Potential Correlations via Hypercontractivity
  Hyeji Kim, Weihao Gao, Sreeram Kannan, Sewoong Oh, and Pramod Viswanath
  NIPS, 2017 [ code ]

• Matrix Norm Estimation from a Few Entries
  Ashish Khetan, Sewoong Oh
  NIPS, 2017, (Spotlight presentation) [ code ]

• Top-K Ranking from Pairwise Comparisons: When Spectral Ranking is Optimal
  Minje Jang, Sunghyun Kim, Changho Suh, Sewoong Oh
  NIPS, 2017

• Density Functional Estimators with k-Nearest Neighbor Bandwidths
  Weihao Gao, Sewoong Oh, Pramod Viswanath
  ISIT, 2017

• Demystifying Fixed k-Nearest Neighbor Information Estimators
  Weihao Gao, Sewoong Oh, Pramod Viswanath
  ISIT, 2017, [ code ]

• Achieving budget-optimality with adaptive schemes in crowdsourcing
  Ashish Khetan, Sewoong Oh
  NIPS, 2016

• Computational and Statistical Tradeoffs in Learning to Rank
  Ashish Khetan, Sewoong Oh
  NIPS, 2016

• Breaking the Bandwidth Barrier: Geometrical Adaptive Entropy Estimation
  Weihao Gao, Sewoong Oh, Pramod Viswanath
  NIPS, 2016, [ code ]

• Conditional Dependence via Shannon Capacity: Axioms, Estimators and Applications
  Weihao Gao, Sreeram Kannan, Sewoong Oh, Pramod Viswanath
  ICML, 2016, [ code ]

• Data-driven Rank Breaking for Efficient Rank Aggregation
  Ashish Khetan, Sewoong Oh
  ICML, 2016

• Optimality of Belief Propagation for Crowdsourced Classification
  Jungseul Ok, Sewoong Oh, Jinwoo Shin, Yung Yi
  ICML, 2016

• Metadata-conscious Anonymous Messaging
  Giulia Fanti, Peter Kairouz, Sewoong Oh, Kannan Ramchandran and Pramod Viswanath
  ICML 2016

• Rumor Source Obfuscation on Irregular Trees
  Giulia Fanti, Peter Kairouz, Sewoong Oh, Kannan Ramchandran and Pramod Viswanath
  SIGMETRICS 2016

• Collaboratively Learning Preferences from Ordinal Data
  Sewoong Oh, Kiran K. Thekumparampil, and Jiaming Xu
  NIPS 2015

• Detecting Sponsored Recommendations Subhashini Krishnasamy, Rajat Sen, Sewoong Oh, and Sanjay Shakkottai
  SIGMETRICS (short paper) 2015

• Spy vs. Spy: Rumor Source Obfuscation
  Giulia Fanti, Peter Kairouz, Sewoong Oh, and Pramod Viswanath
  SIGMETRICS 2015 (Best paper award)

• Secure Multi-party Differential Privacy
  Peter Kairouz, Sewoong Oh, and Pramod Viswanath
  NIPS 2015

• Extremal Mechanisms for Local Differential Privacy
  Peter Kairouz, Sewoong Oh, and Pramod Viswanath
  NIPS 2014

• Provable Tensor Factorization with Missing Data
  Prateek Jain and Sewoong Oh
  NIPS 2014, [code]

• Minimax-optimal Inference from Partial Rankings
  Bruce Hajek, Sewoong Oh, and Jiaming Xu
  NIPS 2014

• Learning Mixed Multinomial Logit Model from Ordinal Data
  Sewoong Oh and Devavrat Shah
  NIPS 2014

• Learning Mixtures of Discrete Product Distributions using Spectral Decompositions
  Prateek Jain and Sewoong Oh
  COLT 2014

• The Composition Theorem for Differential Privacy
  Peter Kairouz, Sewoong Oh and Pramod Viswanath
  ICML 2015

• What's your choice? Learning the mixed multi-nomial logit model
  Ammar Ammar, Sewoong Oh, Devavrat Shah, and Luis-Filipe Voloch
  SIGMETRICS (short paper) 2014

• Efficient Crowdsourcing for Multi-class Labeling
  David Karger, Sewoong Oh, and Devavrat Shah
  SIGMETRICS 2013

• Iterative Ranking from Pairwise Comparisons
  Sahand Negahban, Sewoong Oh, and Devavrat Shah
  NIPS 2012, (Spotlight presentation)

• Iterative Learning for Reliable Crowdsourcing Systems
  David R. Karger, Sewoong Oh, and Devavrat Shah
  NIPS 2011, (Oral presentation)

• Budget-optimal Crowdsourcing using Low-rank Matrix Approximations
  David R. Karger, Sewoong Oh, and Devavrat Shah
  Allerton 2011

• Gossip PCA
  Satish Babu Korada, Andrea Montanari, and Sewoong Oh
  ACM SIGMETRICS 2011

• On Positioning via Distributed Matrix Completion
  A. Montanari and S. Oh
  Sensor Array and Multichannel Signal Processing Workshop 2010

• Ultrasound Tomography Calibration using Structured Matrix Completion
  Reza Parhizkar, Amin Karbasi, Sewoong Oh, and Martin Vetterli
  The 20th International Congress on Acoustics, 2010

• Distributed Sensor Network Localization from Local Connectivity: Performance Analysis for the HOP-TERRAIN Algorithm
  Amin Karbasi and Sewoong Oh
  ACM SIGMETRICS 2010, (Kenneth C. Sevcik Outstanding Student Paper Award)

• Sensor Network Localization from Local Connectivity: Performance Analysis for the MDS-MAP Algorithm
  Sewoong Oh, Amin Karbasi, and Andrea Montanari
  Information Theory Workshop 2010

• OptSpace: A Gradient Descent Algorithm on Grassmann Manifold for Matrix Completion
  Raghunandan Keshavan and Sewoong Oh
  Technical report

• Low-rank Matrix Completion with Noisy Observations: a Quantitative Comparison
  Raghunandan Keshavan, Andrea Montanari, and Sewoong Oh
  Allerton 2009

• Matrix Completion from Noisy Entries
  Raghunandan Keshavan, Andrea Montanari, and Sewoong Oh
  NIPS 2009

• Matrix Completion from a Few Entries
  Raghunandan Keshavan, Andrea Montanari, and Sewoong Oh
  ISIT 2009

• Generating Random Tanner-graphs with Large Girth
  Mohsen Bayati, Raghunandan H. Keshavan, Andrea montanari, Sewoong Oh, and Amin Saberi
  Information Theory Workshop 2009, [ code ]

• Learning low rank matrices from O(n) entries
  Raghunandan Keshavan, Andrea Montanari, and Sewoong Oh
  Allerton 2008

• Computing the threshold shift for general channels
  Jeremie Ezri, Andrea Montanari, Sewoong Oh, and Ruediger Urbanke
  ISIT 2008

• The Slope Scaling Parameter for General Channels, Decoders and Ensembles
  Jeremie Ezri, Andrea Montanari, Sewoong Oh, and Ruediger Urbanke
  ISIT 2008

Journal papers

MAUVE scores for generative models: Theory and practice

Krishna Pillutla, Lang Liu, John Thickstun, Sean Welleck, Swabha Swayamdipta, Rowan Zellers, Sewoong Oh, Yejin Choi, Zaid Harchaoui
Journal of Machine Learning Research, 2023

Towards a Defense Against Federated Backdoor Attacks Under Continuous Training

Shuaiqi Wang, Jonathan Hayase, Giulia Fanti, Sewoong Oh
Transactions on Machine Learning Research (TMLR), 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 Transactions on Selected Areas in Information Theory (JSAIT) , 2023

Gradient flows on graphons: existence, convergence, continuity equations

Sewoong Oh, Soumik Pal, Raghav Somani, Raghavendra Tripathi
Journal of Theoretical Probability, 2023,
presented at the NeurIPS 2021 workshop on Optimal Transport and Machine Learning

Evaluating proteomics imputation methods with improved criteria

L Harris, WE Fondrie, S Oh, WS Noble
Journal of Proteome Research, 2023, 22 (11), 3427-3438

Reducing peptide sequence bias in quantitative mass spectrometry data with machine learning

AB Dincer, Y Lu, DK Schweppe, S Oh, WS Noble
Journal of Proteome Research, 2022, 21 (7), 1771-1782

Physical Layer Communication via Deep Learning

Hyeji Kim, Sewoong Oh, Pramod Viswanath
IEEE Transactions on Selected Areas in Information Theory (JSAIT), Vol.1, no.1, pp.5-18, 2020,

LEARN Codes: Inventing Low-latency Codes via Recurrent Neural Networks

Yihan Jiang, Hyeji Kim, Himanshu Asnani, Sreeram Kannan, Sewoong Oh, and Pramod Viswanath
IEEE Transactions on Selected Areas in Information Theory (JSAIT), Vol.1, no.1, pp.207-216, 2020,

PacGAN: The power of two samples in generative adversarial networks

Zinan Lin, Ashish Khetan, Giulia Fanti, Sewoong Oh
IEEE Transactions on Selected Areas in Information Theory (JSAIT), 2 Vol.1, no.1, pp.324-335, 2020, [ code ], [ project page]

Deepcode: Feedback Codes via Deep Learning

Hyeji Kim, Yihan Jiang, Sreeram Kannan, Sewoong Oh, Pramod Viswanath
IEEE Transactions on Selected Areas in Information Theory (JSAIT), Vol.1, no.1, pp.194-206, 2020, [ code by Hyeji Kim ], [ code by Yihan Jiang ]

Spectrum Estimation from a Few Entries

Ashish Khetan, Sewoong Oh
Journal of Machine Learning Research, Vol.20, Issue:21, January 2019

Learning from Comparisons and Choices

Sahand Negahban, Sewoong Oh, Kiran Thekumparampil, and Jiaming Xu,
Journal of Machine Learning Research, Vol.19, Issue:40, pp.1-95, September 2018

Generalized Rank-breaking: Computational and Statistical Tradeoffs

Ashish Khetan, Sewoong Oh
Journal of Machine Learning Research, Vol.19, Issue:28, pp.1-42, September 2018 [bibtex]

Optimality of Belief Propagation for Crowdsourced Classification

Jungseul Ok, Sewoong Oh, Jinwoo Shin, Yung Yi
IEEE Transactions on Information Theory, Vol.64, Issue:9, pp.6127-6138, September 2018,

Demystifying Fixed k-Nearest Neighbor Information Estimators

Weihao Gao, Sewoong Oh, Pramod Viswanath
IEEE Transactions on Information Theory, Vol.64, Issue:8, pp.5629-5661 February 2018, [bibtex]

Breaking the Bandwidth Barrier: Geometrical Adaptive Entropy Estimation

Weihao Gao, Sewoong Oh, Pramod Viswanath
IEEE Transactions on Information Theory, Vol.64, Issue:5, pp.3313-3330, May 2018, [bibtex]

Discovering Potential Correlations via Hypercontractivity

Hyeji Kim, Weihao Gao, Sreeram Kannan, Sewoong Oh, and Pramod Viswanath
Entropy, Vol.19, Issue:11, pp.586, October 2017, [ code ], [bibtex]

Data-driven Rank Breaking for Efficient Rank Aggregation

Ashish Khetan, Sewoong Oh
Journal of Machine Learning Research, Vol.17, no.193, pp.1-54, October 2016 [bibtex]

Hiding the Rumor Source

Giulia Fanti, Peter Kairouz, Sewoong Oh, Kannan Ramchandran, and Pramod Viswanath
IEEE Transactions on Information Theory, Vol.63, Issue:10, pp.6679-6713, October 2017 [bibtex]

Metadata-conscious Anonymous Messaging

Giulia Fanti, Peter Kairouz, Sewoong Oh, Kannan Ramchandran, and Pramod Viswanath
IEEE Transactions on Signal and Information Processing over Networks, Volume: 2, Issue: 4, pp.582 - 594, December 2016

Detecting Sponsored Recommendations

Subhashini Krishnasamy, Rajat Sen, Sewoong Oh, and Sanjay Shakkottai
ACM Transactions on Modeling and Performance Evaluation of Computing Systems, Volume 2, Issue 1, pp.6:1–6:29, November 2016

The Composition Theorem for Differential Privacy

Peter Kairouz, Sewoong Oh and Pramod Viswanath
IEEE Transaction on Information Theory, Volume 63, Issue 6, pp.4037-4049, June 2017 [bibtex]

Extremal Mechanisms for Local Differential Privacy

Peter Kairouz, Sewoong Oh, and Pramod Viswanath
Journal of Machine Learning Research, Volume 17, no.17, pp.1-51, April 2016 [bibtex]

RankCentrality: Ranking from Pair-wise Comparisons

Sahand Negahban, Sewoong Oh, and Devavrat Shah
Operations Research, Vol.65, no.1, pp.266-287, October 2016 [bibtex]

The Staircase Mechanisms in Differential Privacy

Q. Geng, P. Kairouz, S. Oh, and P. Viswanath
Selected Topics in Signal Processing, April 2015

Budget-optimal Task Allocation for Reliable Crowdsourcing Systems

David R. Karger, Sewoong Oh and Devavrat Shah
Operations Research, Volume 62 Issue 1, pp.1-24, January-February 2014 [bibtex]

Robust Localization from Incomplete Local Information

Amin Karbasi and Sewoong Oh
IEEE Transactions on Networking, Vol 21, pp.1131-1144, August 2013, [bibtex]

Calibration using Matrix Completion with Application to Ultrasound Tomography

Reza Parhizkar, Amin Karbai, Sewoong Oh and Martin Vetterli
IEEE Transactions on Signal Processing, Vol 61, pp.4923-4933, October 2013, [bibtex]

Counting with the Crowd

Adam Marcus, David Karger, Samuel Madden, Robert Miller, Sewoong Oh
Journal of the VLDB Endowment, Vol. 6, issue 2, pp.109-120, December 2012, [bibtex]

Matrix Completion from Noisy Entries

Raghunandan Keshavan, Andrea Montanari and Sewoong Oh
Journal of Machine Learning Research, vol. 11, pp.2057-2078, July 2010, [ bibtex , code ]

Matrix Completion from a Few Entries

Raghunandan Keshavan, Andrea Montanari and Sewoong Oh
IEEE Transactions on Information Theory,vol. 56,no. 6, pp.2980-2998, June 2010, [ bibtex , code ]

Dissertation

Matrix Completion: Fundamental Limits and Efficient Algorithms

Ph.D. Dissertation, Stanford Univesiry, December 2010