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07/19/2023
The importance of feature preprocessing for differentially private linear optimization
Training machine learning models with differential privacy (DP) has rece...
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07/06/2023
When Does Confidence-Based Cascade Deferral Suffice?
Cascades are a classical strategy to enable inference cost to vary adapt...
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02/03/2023
ResMem: Learn what you can and memorize the rest
The impressive generalization performance of modern neural networks is a...
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01/30/2023
On student-teacher deviations in distillation: does it pay to disobey?
Knowledge distillation has been widely-used to improve the performance o...
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01/29/2023
Learning to reject meets OOD detection: Are all abstentions created equal?
Learning to reject (L2R) and out-of-distribution (OOD) detection are two...
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01/28/2023
Supervision Complexity and its Role in Knowledge Distillation
Despite the popularity and efficacy of knowledge distillation, there is ...
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01/27/2023
EmbedDistill: A Geometric Knowledge Distillation for Information Retrieval
Large neural models (such as Transformers) achieve state-of-the-art perf...
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10/28/2022
When does mixup promote local linearity in learned representations?
Mixup is a regularization technique that artificially produces new sampl...
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06/13/2022
Robust Distillation for Worst-class Performance
Knowledge distillation has proven to be an effective technique in improv...
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04/27/2022
ELM: Embedding and Logit Margins for Long-Tail Learning
Long-tail learning is the problem of learning under skewed label distrib...
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10/19/2021
When in Doubt, Summon the Titans: Efficient Inference with Large Models
Scaling neural networks to "large" sizes, with billions of parameters, h...
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07/09/2021
Training Over-parameterized Models with Non-decomposable Objectives
Many modern machine learning applications come with complex and nuanced ...
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06/19/2021
Teacher's pet: understanding and mitigating biases in distillation
Knowledge distillation is widely used as a means of improving the perfor...
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05/12/2021
Disentangling Sampling and Labeling Bias for Learning in Large-Output Spaces
Negative sampling schemes enable efficient training given a large number...
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04/16/2021
Interval-censored Hawkes processes
This work builds a novel point process and tools to use the Hawkes proce...
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02/13/2021
Distilling Double Descent
Distillation is the technique of training a "student" model based on exa...
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10/06/2020
SupMMD: A Sentence Importance Model for Extractive Summarization using Maximum Mean Discrepancy
Most work on multi-document summarization has focused on generic summari...
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05/21/2020
Why distillation helps: a statistical perspective
Knowledge distillation is a technique for improving the performance of a...
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04/23/2020
Doubly-stochastic mining for heterogeneous retrieval
Modern retrieval problems are characterised by training sets with potent...
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04/21/2020
Federated Learning with Only Positive Labels
We consider learning a multi-class classification model in the federated...
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03/05/2020
Does label smoothing mitigate label noise?
Label smoothing is commonly used in training deep learning models, where...
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02/10/2020
Supervised Learning: No Loss No Cry
Supervised learning requires the specification of a loss function to min...
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09/20/2019
Online Hierarchical Clustering Approximations
Hierarchical clustering is a widely used approach for clustering dataset...
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01/30/2019
Noise-tolerant fair classification
Fair machine learning concerns the analysis and design of learning algor...
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01/24/2019
Fairness risk measures
Ensuring that classifiers are non-discriminatory or fair with respect to...
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01/18/2019
Cold-start Playlist Recommendation with Multitask Learning
Playlist recommendation involves producing a set of songs that a user mi...
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12/06/2018
Comparative Document Summarisation via Classification
This paper considers extractive summarisation in a comparative setting: ...
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10/10/2018
Complementary-Label Learning for Arbitrary Losses and Models
In contrast to the standard classification paradigm where the true (or p...
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06/08/2018
Monge beats Bayes: Hardness Results for Adversarial Training
The last few years have seen extensive empirical study of the robustness...
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02/18/2018
Anomaly Detection using One-Class Neural Networks
We propose a one-class neural network (OC-NN) model to detect anomalies ...
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07/14/2017
f-GANs in an Information Geometric Nutshell
Nowozin et al showed last year how to extend the GAN principle to all f-...
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04/22/2017
Robust, Deep and Inductive Anomaly Detection
PCA is a classical statistical technique whose simplicity and maturity h...
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07/01/2016
A scaled Bregman theorem with applications
Bregman divergences play a central role in the design and analysis of a ...
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06/04/2015
An Average Classification Algorithm
Many classification algorithms produce a classifier that is a weighted a...
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09/17/2012