
Robust Metalearning for Mixed Linear Regression with Small Batches
A common challenge faced in practical supervised learning, such as medic...
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Metalearning for mixed linear regression
In modern supervised learning, there are a large number of tasks, but ma...
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Turbo Autoencoder: Deep learning based channel codes for pointtopoint communication channels
Designing codes that combat the noise in a communication medium has rema...
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ProofofStake Longest Chain Protocols Revisited
The Nakamoto longest chain protocol has served Bitcoin well in its decad...
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Barracuda: The Power of ℓpolling in ProofofStake Blockchains
A blockchain is a database of sequential events that is maintained by a ...
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PrivacyUtility Tradeoffs in Routing Cryptocurrency over Payment Channel Networks
Payment channel networks (PCNs) are viewed as one of the most promising ...
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Optimal transport mapping via input convex neural networks
In this paper, we present a novel and principled approach to learn the o...
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Efficient Algorithms for Smooth Minimax Optimization
This paper studies first order methods for solving smooth minimax optimi...
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InfoGANCR: Disentangling Generative Adversarial Networks with Contrastive Regularizers
Training disentangled representations with generative adversarial networ...
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Robust conditional GANs under missing or uncertain labels
Matching the performance of conditional Generative Adversarial Networks ...
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Learning in Gated Neural Networks
Gating is a key feature in modern neural networks including LSTMs, GRUs ...
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Minimax Rates of Estimating Approximate Differential Privacy
Differential privacy has become a widely accepted notion of privacy, lea...
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DeepTurbo: Deep Turbo Decoder
Presentday communication systems routinely use codes that approach the ...
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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...
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LEARN Codes: Inventing Lowlatency Codes via Recurrent Neural Networks
Designing channel codes under low latency constraints is one of the most...
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Robustness of Conditional GANs to Noisy Labels
We study the problem of learning conditional generators from noisy label...
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Rate Distortion For Model Compression: From Theory To Practice
As the size of neural network models increases dramatically today, study...
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Learning Onehiddenlayer Neural Networks under General Input Distributions
Significant advances have been made recently on training neural networks...
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Compounding of Wealth in ProofofStake Cryptocurrencies
Proofofstake (PoS) is a promising approach for designing efficient blo...
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Deepcode: Feedback Codes via Deep Learning
The design of codes for communicating reliably over a statistically well...
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Communication Algorithms via Deep Learning
Coding theory is a central discipline underpinning wireline and wireless...
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Attentionbased Graph Neural Network for Semisupervised Learning
Recently popularized graph neural networks achieve the stateoftheart ...
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PacGAN: The power of two samples in generative adversarial networks
Generative adversarial networks (GANs) are innovative techniques for lea...
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Discovering Potential Correlations via Hypercontractivity
Discovering a correlation from one variable to another variable is of fu...
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Learning from Comparisons and Choices
When tracking userspecific online activities, each user's preference is...
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Spectrum Estimation from a Few Entries
Singular values of a data in a matrix form provide insights on the struc...
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Iterative Bayesian Learning for Crowdsourced Regression
Crowdsourcing platforms emerged as popular venues for purchasing human i...
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Breaking the Bandwidth Barrier: Geometrical Adaptive Entropy Estimation
Estimators of information theoretic measures such as entropy and mutual ...
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Computational and Statistical Tradeoffs in Learning to Rank
For massive and heterogeneous modern datasets, it is of fundamental inte...
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Demystifying Fixed kNearest Neighbor Information Estimators
Estimating mutual information from i.i.d. samples drawn from an unknown ...
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TopK Ranking from Pairwise Comparisons: When Spectral Ranking is Optimal
We explore the topK rank aggregation problem. Suppose a collection of i...
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Optimal Inference in Crowdsourced Classification via Belief Propagation
Crowdsourcing systems are popular for solving largescale labelling task...
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Achieving Budgetoptimality with Adaptive Schemes in Crowdsourcing
Crowdsourcing platforms provide marketplaces where task requesters can p...
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Conditional Dependence via Shannon Capacity: Axioms, Estimators and Applications
We conduct an axiomatic study of the problem of estimating the strength ...
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Datadriven Rank Breaking for Efficient Rank Aggregation
Rank aggregation systems collect ordinal preferences from individuals to...
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Collaboratively Learning Preferences from Ordinal Data
In applications such as recommendation systems and revenue management, i...
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Learning Mixed Multinomial Logit Model from Ordinal Data
Motivated by generating personalized recommendations using ordinal (or p...
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Minimaxoptimal Inference from Partial Rankings
This paper studies the problem of inferring a global preference based on...
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Learning Mixtures of Discrete Product Distributions using Spectral Decompositions
We study the problem of learning a distribution from samples, when the u...
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Rank Centrality: Ranking from Pairwise Comparisons
The question of aggregating pairwise comparisons to obtain a global ran...
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BudgetOptimal Task Allocation for Reliable Crowdsourcing Systems
Crowdsourcing systems, in which numerous tasks are electronically distri...
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Sewoong Oh
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Associate Professor at University of Washington