
Robustness of Conditional GANs to Noisy Labels
We study the problem of learning conditional generators from noisy label...
11/08/2018 ∙ by Kiran Koshy Thekumparampil, et al. ∙ 18 ∙ shareread it

Efficient Algorithms for Smooth Minimax Optimization
This paper studies first order methods for solving smooth minimax optimi...
07/02/2019 ∙ by Kiran Koshy Thekumparampil, et al. ∙ 10 ∙ shareread it

Robust conditional GANs under missing or uncertain labels
Matching the performance of conditional Generative Adversarial Networks ...
06/09/2019 ∙ by Kiran Koshy Thekumparampil, et al. ∙ 9 ∙ shareread it

Learning Onehiddenlayer Neural Networks under General Input Distributions
Significant advances have been made recently on training neural networks...
10/09/2018 ∙ by Weihao Gao, et al. ∙ 6 ∙ shareread it

Optimal transport mapping via input convex neural networks
In this paper, we present a novel and principled approach to learn the o...
08/28/2019 ∙ by Ashok Vardhan Makkuva, et al. ∙ 6 ∙ shareread it

InfoGANCR: Disentangling Generative Adversarial Networks with Contrastive Regularizers
Training disentangled representations with generative adversarial networ...
06/14/2019 ∙ by Zinan Lin, et al. ∙ 3 ∙ shareread it

Communication Algorithms via Deep Learning
Coding theory is a central discipline underpinning wireline and wireless...
05/23/2018 ∙ by Hyeji Kim, et al. ∙ 2 ∙ shareread it

LEARN Codes: Inventing Lowlatency Codes via Recurrent Neural Networks
Designing channel codes under low latency constraints is one of the most...
11/30/2018 ∙ by Yihan Jiang, et al. ∙ 2 ∙ shareread it

Discovering Potential Correlations via Hypercontractivity
Discovering a correlation from one variable to another variable is of fu...
09/12/2017 ∙ by Hyeji Kim, et al. ∙ 0 ∙ shareread it

Learning from Comparisons and Choices
When tracking userspecific online activities, each user's preference is...
04/24/2017 ∙ by Sahand Negahban, et al. ∙ 0 ∙ shareread it

Iterative Bayesian Learning for Crowdsourced Regression
Crowdsourcing platforms emerged as popular venues for purchasing human i...
02/28/2017 ∙ by Jungseul Ok, et al. ∙ 0 ∙ shareread it

Breaking the Bandwidth Barrier: Geometrical Adaptive Entropy Estimation
Estimators of information theoretic measures such as entropy and mutual ...
09/07/2016 ∙ by Weihao Gao, et al. ∙ 0 ∙ shareread it

Computational and Statistical Tradeoffs in Learning to Rank
For massive and heterogeneous modern datasets, it is of fundamental inte...
08/22/2016 ∙ by Ashish Khetan, et al. ∙ 0 ∙ shareread it

Demystifying Fixed kNearest Neighbor Information Estimators
Estimating mutual information from i.i.d. samples drawn from an unknown ...
04/11/2016 ∙ by Weihao Gao, et al. ∙ 0 ∙ shareread it

TopK Ranking from Pairwise Comparisons: When Spectral Ranking is Optimal
We explore the topK rank aggregation problem. Suppose a collection of i...
03/14/2016 ∙ by Minje Jang, et al. ∙ 0 ∙ shareread it

Optimal Inference in Crowdsourced Classification via Belief Propagation
Crowdsourcing systems are popular for solving largescale labelling task...
02/11/2016 ∙ by Jungseul Ok, et al. ∙ 0 ∙ shareread it

Achieving Budgetoptimality with Adaptive Schemes in Crowdsourcing
Crowdsourcing platforms provide marketplaces where task requesters can p...
02/10/2016 ∙ by Ashish Khetan, et al. ∙ 0 ∙ shareread it

Conditional Dependence via Shannon Capacity: Axioms, Estimators and Applications
We conduct an axiomatic study of the problem of estimating the strength ...
02/10/2016 ∙ by Weihao Gao, et al. ∙ 0 ∙ shareread it

Datadriven Rank Breaking for Efficient Rank Aggregation
Rank aggregation systems collect ordinal preferences from individuals to...
01/21/2016 ∙ by Ashish Khetan, et al. ∙ 0 ∙ shareread it

Collaboratively Learning Preferences from Ordinal Data
In applications such as recommendation systems and revenue management, i...
06/26/2015 ∙ by Sewoong Oh, et al. ∙ 0 ∙ shareread it

Learning Mixed Multinomial Logit Model from Ordinal Data
Motivated by generating personalized recommendations using ordinal (or p...
11/01/2014 ∙ by Sewoong Oh, et al. ∙ 0 ∙ shareread it

Minimaxoptimal Inference from Partial Rankings
This paper studies the problem of inferring a global preference based on...
06/21/2014 ∙ by Bruce Hajek, et al. ∙ 0 ∙ shareread it

Learning Mixtures of Discrete Product Distributions using Spectral Decompositions
We study the problem of learning a distribution from samples, when the u...
11/12/2013 ∙ by Prateek Jain, et al. ∙ 0 ∙ shareread it

Rank Centrality: Ranking from Pairwise Comparisons
The question of aggregating pairwise comparisons to obtain a global ran...
09/08/2012 ∙ by Sahand Negahban, et al. ∙ 0 ∙ shareread it

BudgetOptimal Task Allocation for Reliable Crowdsourcing Systems
Crowdsourcing systems, in which numerous tasks are electronically distri...
10/17/2011 ∙ by David R. Karger, et al. ∙ 0 ∙ shareread it

PacGAN: The power of two samples in generative adversarial networks
Generative adversarial networks (GANs) are innovative techniques for lea...
12/12/2017 ∙ by Zinan Lin, et al. ∙ 0 ∙ shareread it

Spectrum Estimation from a Few Entries
Singular values of a data in a matrix form provide insights on the struc...
03/18/2017 ∙ by Ashish Khetan, et al. ∙ 0 ∙ shareread it

Attentionbased Graph Neural Network for Semisupervised Learning
Recently popularized graph neural networks achieve the stateoftheart ...
03/10/2018 ∙ by Kiran K. Thekumparampil, et al. ∙ 0 ∙ shareread it

Deepcode: Feedback Codes via Deep Learning
The design of codes for communicating reliably over a statistically well...
07/02/2018 ∙ by Hyeji Kim, et al. ∙ 0 ∙ shareread it

Compounding of Wealth in ProofofStake Cryptocurrencies
Proofofstake (PoS) is a promising approach for designing efficient blo...
09/20/2018 ∙ by Giulia Fanti, et al. ∙ 0 ∙ shareread it

Rate Distortion For Model Compression: From Theory To Practice
As the size of neural network models increases dramatically today, study...
10/09/2018 ∙ by Weihao Gao, et al. ∙ 0 ∙ shareread it

Number of Connected Components in a Graph: Estimation via Counting Patterns
Due to the limited resources and the scale of the graphs in modern datas...
12/01/2018 ∙ by Ashish Khetan, et al. ∙ 0 ∙ shareread it

DeepTurbo: Deep Turbo Decoder
Presentday communication systems routinely use codes that approach the ...
03/06/2019 ∙ by Yihan Jiang, et al. ∙ 0 ∙ shareread it

Learning in Gated Neural Networks
Gating is a key feature in modern neural networks including LSTMs, GRUs ...
06/06/2019 ∙ by Ashok Vardhan Makkuva, et al. ∙ 0 ∙ shareread it

Minimax Rates of Estimating Approximate Differential Privacy
Differential privacy has become a widely accepted notion of privacy, lea...
05/24/2019 ∙ by Xiyang Liu, et al. ∙ 0 ∙ shareread it

PrivacyUtility Tradeoffs in Routing Cryptocurrency over Payment Channel Networks
Payment channel networks (PCNs) are viewed as one of the most promising ...
09/06/2019 ∙ by Weizhao Tang, et al. ∙ 0 ∙ shareread it

Barracuda: The Power of ℓpolling in ProofofStake Blockchains
A blockchain is a database of sequential events that is maintained by a ...
09/18/2019 ∙ by Giulia Fanti, et al. ∙ 0 ∙ shareread it

ProofofStake Longest Chain Protocols Revisited
The Nakamoto longest chain protocol has served Bitcoin well in its decad...
10/05/2019 ∙ by Xuechao Wang, et al. ∙ 0 ∙ shareread it

Turbo Autoencoder: Deep learning based channel codes for pointtopoint communication channels
Designing codes that combat the noise in a communication medium has rema...
11/08/2019 ∙ by Yihan Jiang, et al. ∙ 0 ∙ shareread it
Sewoong Oh
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Assistant Professor at University of Illinois at UrbanaChampaign