Search Results for author: Ananya Kumar

Found 25 papers, 9 papers with code

Finetune like you pretrain: Improved finetuning of zero-shot vision models

1 code implementation1 Dec 2022 Sachin Goyal, Ananya Kumar, Sankalp Garg, Zico Kolter, aditi raghunathan

In total, these benchmarks establish contrastive finetuning as a simple, intuitive, and state-of-the-art approach for supervised finetuning of image-text models like CLIP.

Few-Shot Learning Transfer Learning

Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization?

no code implementations25 Nov 2022 Rishi Bommasani, Kathleen A. Creel, Ananya Kumar, Dan Jurafsky, Percy Liang

As the scope of machine learning broadens, we observe a recurring theme of algorithmic monoculture: the same systems, or systems that share components (e. g. training data), are deployed by multiple decision-makers.

Fairness

How to Fine-Tune Vision Models with SGD

no code implementations17 Nov 2022 Ananya Kumar, Ruoqi Shen, Sébastien Bubeck, Suriya Gunasekar

SGD (with momentum) and AdamW are the two most used optimizers for fine-tuning large neural networks in computer vision.

Surgical Fine-Tuning Improves Adaptation to Distribution Shifts

no code implementations20 Oct 2022 Yoonho Lee, Annie S. Chen, Fahim Tajwar, Ananya Kumar, Huaxiu Yao, Percy Liang, Chelsea Finn

A common approach to transfer learning under distribution shift is to fine-tune the last few layers of a pre-trained model, preserving learned features while also adapting to the new task.

Transfer Learning

Are Sample-Efficient NLP Models More Robust?

no code implementations12 Oct 2022 Nelson F. Liu, Ananya Kumar, Percy Liang, Robin Jia

Recent work has observed that pre-trained models have higher out-of-distribution (OOD) robustness when they are exposed to less in-distribution (ID) training data (Radford et al., 2021).

SST-2

Calibrated ensembles can mitigate accuracy tradeoffs under distribution shift

no code implementations18 Jul 2022 Ananya Kumar, Tengyu Ma, Percy Liang, aditi raghunathan

We often see undesirable tradeoffs in robust machine learning where out-of-distribution (OOD) accuracy is at odds with in-distribution (ID) accuracy: a robust classifier obtained via specialized techniques such as removing spurious features often has better OOD but worse ID accuracy compared to a standard classifier trained via ERM.

Beyond Separability: Analyzing the Linear Transferability of Contrastive Representations to Related Subpopulations

no code implementations6 Apr 2022 Jeff Z. HaoChen, Colin Wei, Ananya Kumar, Tengyu Ma

In particular, a linear classifier trained to separate the representations on the source domain can also predict classes on the target domain accurately, even though the representations of the two domains are far from each other.

Contrastive Learning Unsupervised Domain Adaptation

Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation

no code implementations1 Apr 2022 Kendrick Shen, Robbie Jones, Ananya Kumar, Sang Michael Xie, Jeff Z. HaoChen, Tengyu Ma, Percy Liang

We consider unsupervised domain adaptation (UDA), where labeled data from a source domain (e. g., photographs) and unlabeled data from a target domain (e. g., sketches) are used to learn a classifier for the target domain.

Contrastive Learning Unsupervised Domain Adaptation

Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution

no code implementations21 Feb 2022 Ananya Kumar, aditi raghunathan, Robbie Jones, Tengyu Ma, Percy Liang

However, in this paper, we find that fine-tuning can achieve worse accuracy than linear probing out-of-distribution (OOD) when the pretrained features are good and the distribution shift is large.

Extending the WILDS Benchmark for Unsupervised Adaptation

1 code implementation ICLR 2022 Shiori Sagawa, Pang Wei Koh, Tony Lee, Irena Gao, Sang Michael Xie, Kendrick Shen, Ananya Kumar, Weihua Hu, Michihiro Yasunaga, Henrik Marklund, Sara Beery, Etienne David, Ian Stavness, Wei Guo, Jure Leskovec, Kate Saenko, Tatsunori Hashimoto, Sergey Levine, Chelsea Finn, Percy Liang

Unlabeled data can be a powerful point of leverage for mitigating these distribution shifts, as it is frequently much more available than labeled data and can often be obtained from distributions beyond the source distribution as well.

Calibrated ensembles - a simple way to mitigate ID-OOD accuracy tradeoffs

no code implementations29 Sep 2021 Ananya Kumar, aditi raghunathan, Tengyu Ma, Percy Liang

We often see undesirable tradeoffs in robust machine learning where out-of-distribution (OOD) accuracy is at odds with in-distribution (ID) accuracy.

How does Contrastive Pre-training Connect Disparate Domains?

no code implementations29 Sep 2021 Kendrick Shen, Robbie Matthew Jones, Ananya Kumar, Sang Michael Xie, Percy Liang

We develop a conceptual model for contrastive learning under domain shifts, where data augmentations form connections between classes and domains that can be far apart.

Contrastive Learning Unsupervised Domain Adaptation

On the Opportunities and Risks of Foundation Models

no 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 Unsupervised Domain Adaptation

Selective Classification Can Magnify Disparities Across Groups

1 code implementation ICLR 2021 Erik Jones, Shiori Sagawa, Pang Wei Koh, Ananya Kumar, Percy Liang

In this paper, we find that while selective classification can improve average accuracies, it can simultaneously magnify existing accuracy disparities between various groups within a population, especially in the presence of spurious correlations.

Classification General Classification

Self-training Avoids Using Spurious Features Under Domain Shift

no code implementations NeurIPS 2020 Yining Chen, Colin Wei, Ananya Kumar, Tengyu Ma

In unsupervised domain adaptation, existing theory focuses on situations where the source and target domains are close.

Unsupervised Domain Adaptation

Understanding Self-Training for Gradual Domain Adaptation

2 code implementations ICML 2020 Ananya Kumar, Tengyu Ma, Percy Liang

Machine learning systems must adapt to data distributions that evolve over time, in applications ranging from sensor networks and self-driving car perception modules to brain-machine interfaces.

Unsupervised Domain Adaptation

Verified Uncertainty Calibration

3 code implementations NeurIPS 2019 Ananya Kumar, Percy Liang, Tengyu Ma

In these experiments, we also estimate the calibration error and ECE more accurately than the commonly used plugin estimators.

Weather Forecasting

Consistent Jumpy Predictions for Videos and Scenes

no code implementations ICLR 2019 Ananya Kumar, S. M. Ali Eslami, Danilo Rezende, Marta Garnelo, Fabio Viola, Edward Lockhart, Murray Shanahan

These models typically generate future frames in an autoregressive fashion, which is slow and requires the input and output frames to be consecutive.

3D Scene Reconstruction Video Prediction

Consistent Generative Query Networks

1 code implementation ICLR 2019 Ananya Kumar, S. M. Ali Eslami, Danilo J. Rezende, Marta Garnelo, Fabio Viola, Edward Lockhart, Murray Shanahan

These models typically generate future frames in an autoregressive fashion, which is slow and requires the input and output frames to be consecutive.

3D Scene Reconstruction Video Prediction

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