Search Results for author: Fereshte Khani

Found 13 papers, 8 papers with code

Prompt Engineering a Prompt Engineer

no code implementations9 Nov 2023 Qinyuan Ye, Maxamed Axmed, Reid Pryzant, Fereshte Khani

In addition, inspired by common optimization concepts such as batch size, step size and momentum, we introduce their verbalized counterparts to the meta-prompt and investigate their effects.

counterfactual Counterfactual Reasoning +2

Targeted Data Generation: Finding and Fixing Model Weaknesses

no code implementations28 May 2023 Zexue He, Marco Tulio Ribeiro, Fereshte Khani

Even when aggregate accuracy is high, state-of-the-art NLP models often fail systematically on specific subgroups of data, resulting in unfair outcomes and eroding user trust.

Data Augmentation Natural Language Inference +2

Collaborative Development of NLP models

no code implementations20 May 2023 Fereshte Khani, Marco Tulio Ribeiro

Our main insight is learning a \emph{local} model for each concept, and a \emph{global} model to integrate the original data with all concepts.

Language Modelling Large Language Model

MaskTune: Mitigating Spurious Correlations by Forcing to Explore

1 code implementation30 Sep 2022 Saeid Asgari Taghanaki, Aliasghar Khani, Fereshte Khani, Ali Gholami, Linh Tran, Ali Mahdavi-Amiri, Ghassan Hamarneh

A fundamental challenge of over-parameterized deep learning models is learning meaningful data representations that yield good performance on a downstream task without over-fitting spurious input features.

Counterbalancing Teacher: Regularizing Batch Normalized Models for Robustness

no code implementations4 Jul 2022 Saeid Asgari Taghanaki, Ali Gholami, Fereshte Khani, Kristy Choi, Linh Tran, Ran Zhang, Aliasghar Khani

Batch normalization (BN) is a ubiquitous technique for training deep neural networks that accelerates their convergence to reach higher accuracy.

On the Opportunities and Risks of Foundation Models

3 code implementations16 Aug 2021 Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri Chatterji, Annie Chen, Kathleen Creel, Jared Quincy Davis, Dora Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren Gillespie, Karan Goel, Noah Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Omar Khattab, Pang Wei Koh, Mark Krass, Ranjay Krishna, Rohith Kuditipudi, Ananya Kumar, Faisal Ladhak, Mina Lee, Tony Lee, Jure Leskovec, Isabelle Levent, Xiang Lisa Li, Xuechen Li, Tengyu Ma, Ali Malik, Christopher D. Manning, Suvir Mirchandani, Eric Mitchell, Zanele Munyikwa, Suraj Nair, Avanika Narayan, Deepak Narayanan, Ben Newman, Allen Nie, Juan Carlos Niebles, Hamed Nilforoshan, Julian Nyarko, Giray Ogut, Laurel Orr, Isabel Papadimitriou, Joon Sung Park, Chris Piech, Eva Portelance, Christopher Potts, aditi raghunathan, Rob Reich, Hongyu Ren, Frieda Rong, Yusuf Roohani, Camilo Ruiz, Jack Ryan, Christopher Ré, Dorsa Sadigh, Shiori Sagawa, Keshav Santhanam, Andy Shih, Krishnan Srinivasan, Alex Tamkin, Rohan Taori, Armin W. Thomas, Florian Tramèr, Rose E. Wang, William Wang, Bohan Wu, Jiajun Wu, Yuhuai Wu, Sang Michael Xie, Michihiro Yasunaga, Jiaxuan You, Matei Zaharia, Michael Zhang, Tianyi Zhang, Xikun Zhang, Yuhui Zhang, Lucia Zheng, Kaitlyn Zhou, Percy Liang

AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.

Transfer Learning

In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution Robustness

1 code implementation ICLR 2021 Sang Michael Xie, Ananya Kumar, Robbie Jones, Fereshte Khani, Tengyu Ma, Percy Liang

To get the best of both worlds, we introduce In-N-Out, which first trains a model with auxiliary inputs and uses it to pseudolabel all the in-distribution inputs, then pre-trains a model on OOD auxiliary outputs and fine-tunes this model with the pseudolabels (self-training).

Time Series Time Series Analysis +1

Removing Spurious Features can Hurt Accuracy and Affect Groups Disproportionately

1 code implementation7 Dec 2020 Fereshte Khani, Percy Liang

The presence of spurious features interferes with the goal of obtaining robust models that perform well across many groups within the population.

Feature Noise Induces Loss Discrepancy Across Groups

1 code implementation ICML 2020 Fereshte Khani, Percy Liang

Our main result is that even when there is no information deficiency specific to one group (e. g., both groups have infinite data), adding the same amount of feature noise to all individuals leads to loss discrepancy.

Maximum Weighted Loss Discrepancy

1 code implementation8 Jun 2019 Fereshte Khani, aditi raghunathan, Percy Liang

To capture this inequality, we introduce and study a notion we call maximum weighted loss discrepancy (MWLD), the maximum (weighted) difference between the loss of a group and the loss of the population.

Fairness Generalization Bounds

Planning, Inference and Pragmatics in Sequential Language Games

1 code implementation TACL 2018 Fereshte Khani, Noah D. Goodman, Percy Liang

We study sequential language games in which two players, each with private information, communicate to achieve a common goal.

Unanimous Prediction for 100% Precision with Application to Learning Semantic Mappings

1 code implementation20 Jun 2016 Fereshte Khani, Martin Rinard, Percy Liang

Specifically, we introduce the unanimity principle: only predict when all models consistent with the training data predict the same output.

Semantic Parsing

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