Maziar Sanjabi

research

∙ 07/20/2023

Identifying Interpretable Subspaces in Image Representations

We propose Automatic Feature Explanation using Contrasting Concepts (FAL...

0 Neha Kalibhat, et al. ∙

research

∙ 07/18/2023

Augmenting CLIP with Improved Visio-Linguistic Reasoning

Image-text contrastive models such as CLIP are useful for a variety of d...

0 Samyadeep Basu, et al. ∙

research

∙ 06/15/2023

ViP: A Differentially Private Foundation Model for Computer Vision

Artificial intelligence (AI) has seen a tremendous surge in capabilities...

0 Yaodong Yu, et al. ∙

research

∙ 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...

0 Mazda Moayeri, et al. ∙

research

∙ 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...

0 Ajinkya Tejankar, et al. ∙

research

∙ 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...

0 Chuan Guo, et al. ∙

research

∙ 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...

0 John Nguyen, et al. ∙

research

∙ 10/14/2022

COFFEE: Counterfactual Fairness for Personalized Text Generation in Explainable Recommendation

Personalized text generation has broad industrial applications, such as ...

0 Nan Wang, et al. ∙

research

∙ 07/02/2022

FRAME: Evaluating Simulatability Metrics for Free-Text Rationales

Free-text rationales aim to explain neural language model (LM) behavior ...

0 Aaron Chan, et al. ∙

research

∙ 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...

0 John Nguyen, et al. ∙

research

∙ 05/25/2022

ER-TEST: Evaluating Explanation Regularization Methods for NLP Models

Neural language models' (NLMs') reasoning processes are notoriously hard...

0 Brihi Joshi, et al. ∙

research

∙ 04/27/2022

FedShuffle: Recipes for Better Use of Local Work in Federated Learning

The practice of applying several local updates before aggregation across...

1 Samuel Horvath, et al. ∙

research

∙ 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 ...

0 Khalil Mrini, et al. ∙

research

∙ 04/08/2022

Federated Learning with Partial Model Personalization

We consider two federated learning algorithms for training partially per...

0 Krishna Pillutla, et al. ∙

research

∙ 03/03/2022

Understanding Failure Modes of Self-Supervised Learning

Self-supervised learning methods have shown impressive results in downst...

59 Neha Mukund Kalibhat, et al. ∙

research

∙ 12/31/2021

BARACK: Partially Supervised Group Robustness With Guarantees

While neural networks have shown remarkable success on classification ta...

4 Nimit Sohoni, et al. ∙

research

∙ 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...

0 Aaron Chan, et al. ∙

research

∙ 09/13/2021

On Tilted Losses in Machine Learning: Theory and Applications

Exponential tilting is a technique commonly used in fields such as stati...

0 Tian Li, et al. ∙

research

∙ 01/06/2021

Modality-specific Distillation

Large neural networks are impractical to deploy on mobile devices due to...

0 Woojeong Jin, et al. ∙

research

∙ 09/08/2020

Alternating Direction Method of Multipliers for Quantization

Quantization of the parameters of machine learning models, such as deep ...

0 Tianjian Huang, et al. ∙

research

∙ 07/02/2020

Tilted Empirical Risk Minimization

Empirical risk minimization (ERM) is typically designed to perform well ...

0 Tian Li, et al. ∙

research

∙ 01/07/2020

FedDANE: A Federated Newton-Type Method

Federated learning aims to jointly learn statistical models over massive...

0 Tian Li, et al. ∙

research

∙ 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...

0 Maziar Sanjabi, et al. ∙

research

∙ 05/25/2019

Fair Resource Allocation in Federated Learning

Federated learning involves training statistical models in massive, hete...

0 Tian Li, et al. ∙

research

∙ 04/22/2019

Training generative networks using random discriminators

In recent years, Generative Adversarial Networks (GANs) have drawn a lot...

0 Babak Barazandeh, et al. ∙

research

∙ 12/14/2018

On the Convergence of Federated Optimization in Heterogeneous Networks

The burgeoning field of federated learning involves training machine lea...

0 Anit Kumar Sahu, et al. ∙

research

∙ 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...

0 Maziar Sanjabi, et al. ∙

research

∙ 02/22/2018

Solving Approximate Wasserstein GANs to Stationarity

Generative Adversarial Networks (GANs) are one of the most practical str...

0 Maziar Sanjabi, et al. ∙

research

∙ 05/30/2017

Federated Multi-Task Learning

Federated learning poses new statistical and systems challenges in train...

0 Virginia Smith, et al. ∙

Maziar Sanjabi

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