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09/07/2023
Optimal Transport with Tempered Exponential Measures
In the field of optimal transport, two prominent subfields face each oth...
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06/08/2023
Boosting with Tempered Exponential Measures
One of the most popular ML algorithms, AdaBoost, can be derived from the...
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05/26/2023
A Mechanism for Sample-Efficient In-Context Learning for Sparse Retrieval Tasks
We study the phenomenon of in-context learning (ICL) exhibited by large ...
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09/15/2022
Layerwise Bregman Representation Learning with Applications to Knowledge Distillation
In this work, we propose a novel approach for layerwise representation l...
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02/13/2022
Learning from Randomly Initialized Neural Network Features
We present the surprising result that randomly initialized neural networ...
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01/31/2022
Step-size Adaptation Using Exponentiated Gradient Updates
Optimizers like Adam and AdaGrad have been very successful in training l...
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06/11/2021
LocoProp: Enhancing BackProp via Local Loss Optimization
We study a local loss construction approach for optimizing neural networ...
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04/03/2021
Exponentiated Gradient Reweighting for Robust Training Under Label Noise and Beyond
Many learning tasks in machine learning can be viewed as taking a gradie...
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11/21/2020
Rank-smoothed Pairwise Learning In Perceptual Quality Assessment
Conducting pairwise comparisons is a widely used approach in curating hu...
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10/16/2020
A case where a spindly two-layer linear network whips any neural network with a fully connected input layer
It was conjectured that any neural network of any structure and arbitrar...
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02/24/2020
Interpolating Between Gradient Descent and Exponentiated Gradient Using Reparameterized Gradient Descent
Continuous-time mirror descent (CMD) can be seen as the limit case of th...
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10/01/2019
TriMap: Large-scale Dimensionality Reduction Using Triplets
We introduce "TriMap"; a dimensionality reduction technique based on tri...
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09/11/2019
An Implicit Form of Krasulina's k-PCA Update without the Orthonormality Constraint
We shed new insights on the two commonly used updates for the online k-P...
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07/08/2019
Unbiased estimators for random design regression
In linear regression we wish to estimate the optimum linear least square...
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06/08/2019
Robust Bi-Tempered Logistic Loss Based on Bregman Divergences
We introduce a temperature into the exponential function and replace the...
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02/20/2019
Adaptive scale-invariant online algorithms for learning linear models
We consider online learning with linear models, where the algorithm pred...
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02/11/2019
Divergence-Based Motivation for Online EM and Combining Hidden Variable Models
Expectation-Maximization (EM) is the fallback method for parameter estim...
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02/04/2019
Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression
In experimental design, we are given a large collection of vectors, each...
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12/05/2018
Unlabeled sample compression schemes and corner peelings for ample and maximum classes
We examine connections between combinatorial notions that arise in machi...
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10/04/2018
Correcting the bias in least squares regression with volume-rescaled sampling
Consider linear regression where the examples are generated by an unknow...
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06/06/2018
Reverse iterative volume sampling for linear regression
We study the following basic machine learning task: Given a fixed set of...
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04/18/2018
Online Non-Additive Path Learning under Full and Partial Information
We consider the online path learning problem in a graph with non-additiv...
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03/10/2018
Speech Recognition: Keyword Spotting Through Image Recognition
The problem of identifying voice commands has always been a challenge du...
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03/01/2018
A more globally accurate dimensionality reduction method using triplets
We first show that the commonly used dimensionality reduction (DR) metho...
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02/19/2018
Tail bounds for volume sampled linear regression
The n × d design matrix in a linear regression problem is given, but the...
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05/19/2017
Two-temperature logistic regression based on the Tsallis divergence
We develop a variant of multiclass logistic regression that achieves thr...
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11/30/2016
Low-dimensional Data Embedding via Robust Ranking
We describe a new method called t-ETE for finding a low-dimensional embe...
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06/16/2015
PCA with Gaussian perturbations
Most of machine learning deals with vector parameters. Ideally we would ...
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08/09/2014
A Bayesian Probability Calculus for Density Matrices
One of the main concepts in quantum physics is a density matrix, which i...
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01/23/2013
