436 citations · 1.4k across the 41 of their papers we have counts for
11 papers · 1 filter
Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning
John Nguyen, Jianyu Wang, Kshitiz Malik +2
An oft-cited challenge of federated learning is the presence of heterogeneity. \emph{Data heterogeneity} refers to the fact that data from different clients may follow very differe…
COFFEE: Counterfactual Fairness for Personalized Text Generation in Explainable Recommendation
Nan Wang, Qifan Wang, Yi-Chia Wang +5
As language models become increasingly integrated into our digital lives, Personalized Text Generation (PTG) has emerged as a pivotal component with a wide range of applications. H…
Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano
Chuan Guo, Alexandre Sablayrolles, Maziar Sanjabi
Differential privacy (DP) is by far the most widely accepted framework for mitigating privacy risks in machine learning. However, exactly how small the privacy parameter needs…
FRAME: Evaluating Rationale-Label Consistency Metrics for Free-Text Rationales
Aaron Chan, Shaoliang Nie, Liang Tan +4
Following how humans communicate, free-text rationales aim to use natural language to explain neural language model (LM) behavior. However, free-text rationales' unconstrained natu…
Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning
John Nguyen, Jianyu Wang, Kshitiz Malik +2
An oft-cited challenge of federated learning is the presence of heterogeneity. \emph{Data heterogeneity} refers to the fact that data from different clients may follow very differe…
ER-Test: Evaluating Explanation Regularization Methods for Language Models
Brihi Joshi, Aaron Chan, Ziyi Liu +4
By explaining how humans would solve a given task, human rationales can provide strong learning signal for neural language models (LMs). Explanation regularization (ER) aims to imp…
Detection, Disambiguation, Re-ranking: Autoregressive Entity Linking as a Multi-Task Problem
Khalil Mrini, Shaoliang Nie, Jiatao Gu +3
We propose an autoregressive entity linking model, that is trained with two auxiliary tasks, and learns to re-rank generated samples at inference time. Our proposed novelties addre…
FedShuffle: Recipes for Better Use of Local Work in Federated Learning
Samuel Horváth, Maziar Sanjabi, Lin Xiao +2
The practice of applying several local updates before aggregation across clients has been empirically shown to be a successful approach to overcoming the communication bottleneck i…
Federated Learning with Partial Model Personalization
Krishna Pillutla, Kshitiz Malik, Abdelrahman Mohamed +3
We consider two federated learning algorithms for training partially personalized models, where the shared and personal parameters are updated either simultaneously or alternately…
Measuring Self-Supervised Representation Quality for Downstream Classification using Discriminative Features
Neha Kalibhat, Kanika Narang, Hamed Firooz +2
Self-supervised learning (SSL) has shown impressive results in downstream classification tasks. However, there is limited work in understanding their failure modes and interpreting…
BARACK: Partially Supervised Group Robustness With Guarantees
Nimit S. Sohoni, Maziar Sanjabi, Nicolas Ballas +4
While neural networks have shown remarkable success on classification tasks in terms of average-case performance, they often fail to perform well on certain groups of the data. Suc…