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06/14/2023
Batches Stabilize the Minimum Norm Risk in High Dimensional Overparameterized Linear Regression
Learning algorithms that divide the data into batches are prevalent in m...
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06/17/2022
Beyond Ridge Regression for Distribution-Free Data
In supervised batch learning, the predictive normalized maximum likeliho...
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10/18/2021
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection
Detecting out-of-distribution (OOD) samples is vital for developing mach...
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09/04/2021
Utilizing Adversarial Targeted Attacks to Boost Adversarial Robustness
Adversarial attacks have been shown to be highly effective at degrading ...
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02/14/2021
The Predictive Normalized Maximum Likelihood for Over-parameterized Linear Regression with Norm Constraint: Regret and Double Descent
A fundamental tenet of learning theory is that a trade-off exists betwee...
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01/29/2021
Sequential prediction under log-loss and misspecification
We consider the question of sequential prediction under the log-loss in ...
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11/20/2020
Efficient Data-Dependent Learnability
The predictive normalized maximum likelihood (pNML) approach has recentl...
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03/13/2020
Can Implicit Bias Explain Generalization? Stochastic Convex Optimization as a Case Study
The notion of implicit bias, or implicit regularization, has been sugges...
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01/12/2020
One shot approach to lossy source coding under average distortion constraints
This paper present a one shot analysis of the lossy compression problem ...
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05/12/2019
A New Look at an Old Problem: A Universal Learning Approach to Linear Regression
Linear regression is a classical paradigm in statistics. A new look at i...
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04/28/2019
Deep pNML: Predictive Normalized Maximum Likelihood for Deep Neural Networks
The Predictive Normalized Maximum Likelihood (pNML) scheme has been rece...
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12/22/2018
Universal Supervised Learning for Individual Data
Universal supervised learning is considered from an information theoreti...
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11/25/2018
Innovation Representation of Stochastic Processes with Application to Causal Inference
Typically, real-world stochastic processes are not easy to analyze. In t...
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10/31/2018
An Information-Theoretic Framework for Non-linear Canonical Correlation Analysis
Canonical Correlation Analysis (CCA) is a linear representation learning...
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09/16/2018
Linear Independent Component Analysis over Finite Fields: Algorithms and Bounds
Independent Component Analysis (ICA) is a statistical tool that decompos...
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07/06/2018
