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06/22/2023
Communication-Efficient Federated Learning through Importance Sampling
The high communication cost of sending model updates from the clients to...
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06/08/2023
Exact Optimality of Communication-Privacy-Utility Tradeoffs in Distributed Mean Estimation
We study the mean estimation problem under communication and local diffe...
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05/03/2023
Toward Textual Transform Coding
Inspired by recent work on compression with and for young humans, the su...
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11/05/2022
Leveraging the Hints: Adaptive Bidding in Repeated First-Price Auctions
With the advent and increasing consolidation of e-commerce, digital adve...
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10/14/2022
Upper bounds on the Rate of Uniformly-Random Codes for the Deletion Channel
We consider the maximum coding rate achievable by uniformly-random codes...
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09/30/2022
Sparse Random Networks for Communication-Efficient Federated Learning
One main challenge in federated learning is the large communication cost...
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02/07/2022
Learning under Storage and Privacy Constraints
Storage-efficient privacy-guaranteed learning is crucial due to enormous...
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06/26/2021
Txt2Vid: Ultra-Low Bitrate Compression of Talking-Head Videos via Text
Video represents the majority of internet traffic today leading to a con...
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02/16/2021
Successive Pruning for Model Compression via Rate Distortion Theory
Neural network (NN) compression has become essential to enable deploying...
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02/15/2021
Neural Network Compression for Noisy Storage Devices
Compression and efficient storage of neural network (NN) parameters is c...
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07/09/2020
Learning to Bid Optimally and Efficiently in Adversarial First-price Auctions
First-price auctions have very recently swept the online advertising ind...
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03/22/2020
Optimal No-regret Learning in Repeated First-price Auctions
We study online learning in repeated first-price auctions with censored ...
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11/01/2019
LFZip: Lossy compression of multivariate floating-point time series data via improved prediction
Time series data compression is emerging as an important problem with th...
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07/02/2019
Minimum Power to Maintain a Nonequilibrium Distribution of a Markov Chain
Biological systems use energy to maintain non-equilibrium distributions ...
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04/05/2019
Optimal Communication Rates for Zero-Error Distributed Simulation under Blackboard Communication Protocols
We study the distributed simulation problem where n users aim to generat...
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11/19/2018
NECST: Neural Joint Source-Channel Coding
For reliable transmission across a noisy communication channel, classica...
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10/25/2018
Humans are still the best lossy image compressors
Lossy image compression has been studied extensively in the context of t...
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10/25/2018
Towards improved lossy image compression: Human image reconstruction with public-domain images
Lossy image compression has been studied extensively in the context of t...
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09/18/2018
Concentration Inequalities for the Empirical Distribution
We study concentration inequalities for the Kullback--Leibler (KL) diver...
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05/02/2018
Minimax redundancy for Markov chains with large state space
For any Markov source, there exist universal codes whose normalized code...
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05/02/2018
Lower bounds on the minimax redundancy for Markov chains with large state space
For any Markov source, there exist universal codes whose normalized code...
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02/23/2018
Geometric Lower Bounds for Distributed Parameter Estimation under Communication Constraints
We consider parameter estimation in distributed networks, where each nod...
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02/23/2018
Local moment matching: A unified methodology for symmetric functional estimation and distribution estimation under Wasserstein distance
We present Local Moment Matching (LMM), a unified methodology for symmet...
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02/22/2018
Entropy Rate Estimation for Markov Chains with Large State Space
Estimating the entropy based on data is one of the prototypical problems...
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12/19/2017
Approximate Profile Maximum Likelihood
We propose an efficient algorithm for approximate computation of the pro...
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11/06/2017
Optimal rates of entropy estimation over Lipschitz balls
We consider the problem of minimax estimation of the entropy of a densit...
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07/05/2017
Estimating the Fundamental Limits is Easier than Achieving the Fundamental Limits
We show through case studies that it is easier to estimate the fundament...
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11/03/2016
Demystifying ResNet
The Residual Network (ResNet), proposed in He et al. (2015), utilized sh...
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09/26/2014
Beyond Maximum Likelihood: from Theory to Practice
Maximum likelihood is the most widely used statistical estimation techni...
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05/20/2007