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07/20/2023
Identifying Interpretable Subspaces in Image Representations
We propose Automatic Feature Explanation using Contrasting Concepts (FAL...
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07/18/2023
Augmenting CLIP with Improved Visio-Linguistic Reasoning
Image-text contrastive models such as CLIP are useful for a variety of d...
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06/15/2023
ViP: A Differentially Private Foundation Model for Computer Vision
Artificial intelligence (AI) has seen a tremendous surge in capabilities...
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05/10/2023
Text-To-Concept (and Back) via Cross-Model Alignment
We observe that the mapping between an image's representation in one mod...
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04/04/2023
Defending Against Patch-based Backdoor Attacks on Self-Supervised Learning
Recently, self-supervised learning (SSL) was shown to be vulnerable to p...
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10/24/2022
Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano
Differential privacy (DP) is by far the most widely accepted framework f...
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10/14/2022
Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning
An oft-cited challenge of federated learning is the presence of heteroge...
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10/14/2022
COFFEE: Counterfactual Fairness for Personalized Text Generation in Explainable Recommendation
Personalized text generation has broad industrial applications, such as ...
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07/02/2022
FRAME: Evaluating Simulatability Metrics for Free-Text Rationales
Free-text rationales aim to explain neural language model (LM) behavior ...
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06/30/2022
Where to Begin? Exploring the Impact of Pre-Training and Initialization in Federated Learning
An oft-cited challenge of federated learning is the presence of data het...
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05/25/2022
ER-TEST: Evaluating Explanation Regularization Methods for NLP Models
Neural language models' (NLMs') reasoning processes are notoriously hard...
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04/27/2022
FedShuffle: Recipes for Better Use of Local Work in Federated Learning
The practice of applying several local updates before aggregation across...
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04/12/2022
Detection, Disambiguation, Re-ranking: Autoregressive Entity Linking as a Multi-Task Problem
We propose an autoregressive entity linking model, that is trained with ...
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04/08/2022
Federated Learning with Partial Model Personalization
We consider two federated learning algorithms for training partially per...
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03/03/2022
Understanding Failure Modes of Self-Supervised Learning
Self-supervised learning methods have shown impressive results in downst...
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12/31/2021
BARACK: Partially Supervised Group Robustness With Guarantees
While neural networks have shown remarkable success on classification ta...
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12/16/2021
UniREx: A Unified Learning Framework for Language Model Rationale Extraction
An extractive rationale explains a language model's (LM's) prediction on...
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09/13/2021
On Tilted Losses in Machine Learning: Theory and Applications
Exponential tilting is a technique commonly used in fields such as stati...
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01/06/2021
Modality-specific Distillation
Large neural networks are impractical to deploy on mobile devices due to...
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09/08/2020
Alternating Direction Method of Multipliers for Quantization
Quantization of the parameters of machine learning models, such as deep ...
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07/02/2020
Tilted Empirical Risk Minimization
Empirical risk minimization (ERM) is typically designed to perform well ...
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01/07/2020
FedDANE: A Federated Newton-Type Method
Federated learning aims to jointly learn statistical models over massive...
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11/22/2019
When Does Non-Orthogonal Tensor Decomposition Have No Spurious Local Minima?
We study the optimization problem for decomposing d dimensional fourth-o...
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05/25/2019
Fair Resource Allocation in Federated Learning
Federated learning involves training statistical models in massive, hete...
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04/22/2019
Training generative networks using random discriminators
In recent years, Generative Adversarial Networks (GANs) have drawn a lot...
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12/14/2018
On the Convergence of Federated Optimization in Heterogeneous Networks
The burgeoning field of federated learning involves training machine lea...
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12/07/2018
Solving Non-Convex Non-Concave Min-Max Games Under Polyak-Łojasiewicz Condition
In this short note, we consider the problem of solving a min-max zero-su...
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02/22/2018
Solving Approximate Wasserstein GANs to Stationarity
Generative Adversarial Networks (GANs) are one of the most practical str...
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05/30/2017