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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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06/12/2023
Benchmarking Neural Network Training Algorithms
Training algorithms, broadly construed, are an essential part of every d...
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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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02/04/2023
Harnessing Simulation for Molecular Embeddings
While deep learning has unlocked advances in computational biology once ...
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11/04/2022
Clustering above Exponential Families with Tempered Exponential Measures
The link with exponential families has allowed k-means clustering to be ...
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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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04/18/2022
Extracting Targeted Training Data from ASR Models, and How to Mitigate It
Recent work has designed methods to demonstrate that model updates in AS...
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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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09/10/2021
Efficiently Identifying Task Groupings for Multi-Task Learning
Multi-task learning can leverage information learned by one task to bene...
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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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02/21/2021
Privacy-Preserving Wireless Federated Learning Exploiting Inherent Hardware Impairments
We consider a wireless federated learning system where multiple data hol...
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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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10/29/2020
Measuring and Harnessing Transference in Multi-Task Learning
Multi-task learning can leverage information learned by one task to bene...
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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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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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02/11/2019
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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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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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