Search Results for author: aditi raghunathan

Found 28 papers, 17 papers with code

Overparameterization hurts worst-group accuracy with spurious correlations

no code implementations ICML 2020 Shiori Sagawa, aditi raghunathan, Pang Wei Koh, Percy Liang

Increasing model capacity well beyond the point of zero training error has been observed to improve average test accuracy.

Agreement-on-the-Line: Predicting the Performance of Neural Networks under Distribution Shift

no code implementations27 Jun 2022 Christina Baek, Yiding Jiang, aditi raghunathan, Zico Kolter

In this paper, we show a similar but surprising phenomenon also holds for the agreement between pairs of neural network classifiers: whenever accuracy-on-the-line holds, we observe that the OOD agreement between the predictions of any two pairs of neural networks (with potentially different architectures) also observes a strong linear correlation with their ID agreement.

Model Selection

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.

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.

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 Kohd, 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

Just Train Twice: Improving Group Robustness without Training Group Information

1 code implementation19 Jul 2021 Evan Zheran Liu, Behzad Haghgoo, Annie S. Chen, aditi raghunathan, Pang Wei Koh, Shiori Sagawa, Percy Liang, Chelsea Finn

Standard training via empirical risk minimization (ERM) can produce models that achieve high accuracy on average but low accuracy on certain groups, especially in the presence of spurious correlations between the input and label.

Image Classification Natural Language Processing

Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming

2 code implementations NeurIPS 2020 Sumanth Dathathri, Krishnamurthy Dvijotham, Alexey Kurakin, aditi raghunathan, Jonathan Uesato, Rudy Bunel, Shreya Shankar, Jacob Steinhardt, Ian Goodfellow, Percy Liang, Pushmeet Kohli

In this work, we propose a first-order dual SDP algorithm that (1) requires memory only linear in the total number of network activations, (2) only requires a fixed number of forward/backward passes through the network per iteration.

Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices

2 code implementations6 Aug 2020 Evan Zheran Liu, aditi raghunathan, Percy Liang, Chelsea Finn

Learning a new task often requires both exploring to gather task-relevant information and exploiting this information to solve the task.

Meta Reinforcement Learning reinforcement-learning +1

The Pitfalls of Simplicity Bias in Neural Networks

2 code implementations NeurIPS 2020 Harshay Shah, Kaustav Tamuly, aditi raghunathan, Prateek Jain, Praneeth Netrapalli

Furthermore, previous settings that use SB to theoretically justify why neural networks generalize well do not simultaneously capture the non-robustness of neural networks---a widely observed phenomenon in practice [Goodfellow et al. 2014, Jo and Bengio 2017].

Explore then Execute: Adapting without Rewards via Factorized Meta-Reinforcement Learning

no code implementations ICML Workshop LifelongML 2020 Evan Zheran Liu, aditi raghunathan, Percy Liang, Chelsea Finn

In principle, meta-reinforcement learning approaches can exploit this shared structure, but in practice, they fail to adapt to new environments when adaptation requires targeted exploration (e. g., exploring the cabinets to find ingredients in a new kitchen).

Meta Reinforcement Learning reinforcement-learning +1

An Investigation of Why Overparameterization Exacerbates Spurious Correlations

2 code implementations9 May 2020 Shiori Sagawa, aditi raghunathan, Pang Wei Koh, Percy Liang

We study why overparameterization -- increasing model size well beyond the point of zero training error -- can hurt test error on minority groups despite improving average test error when there are spurious correlations in the data.

Inductive Bias

DROCC: Deep Robust One-Class Classification

1 code implementation ICML 2020 Sachin Goyal, aditi raghunathan, Moksh Jain, Harsha Vardhan Simhadri, Prateek Jain

Classical approaches for one-class problems such as one-class SVM and isolation forest require careful feature engineering when applied to structured domains like images.

Anomaly Detection Classification +3

Understanding and Mitigating the Tradeoff Between Robustness and Accuracy

1 code implementation ICML 2020 Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John Duchi, Percy Liang

In this work, we precisely characterize the effect of augmentation on the standard error in linear regression when the optimal linear predictor has zero standard and robust error.

Adversarial Training Can Hurt Generalization

no code implementations ICML Workshop Deep_Phenomen 2019 Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, Percy Liang

While adversarial training can improve robust accuracy (against an adversary), it sometimes hurts standard accuracy (when there is no adversary).

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

Unlabeled Data Improves Adversarial Robustness

4 code implementations NeurIPS 2019 Yair Carmon, aditi raghunathan, Ludwig Schmidt, Percy Liang, John C. Duchi

We demonstrate, theoretically and empirically, that adversarial robustness can significantly benefit from semisupervised learning.

Adversarial Robustness Robust classification

Semidefinite relaxations for certifying robustness to adversarial examples

2 code implementations NeurIPS 2018 Aditi Raghunathan, Jacob Steinhardt, Percy Liang

One promise of ending the arms race is developing certified defenses, ones which are provably robust against all attackers in some family.

Certified Defenses against Adversarial Examples

4 code implementations ICLR 2018 Aditi Raghunathan, Jacob Steinhardt, Percy Liang

While neural networks have achieved high accuracy on standard image classification benchmarks, their accuracy drops to nearly zero in the presence of small adversarial perturbations to test inputs.

Adversarial Attack Adversarial Defense +1

Estimating the unseen from multiple populations

2 code implementations ICML 2017 Aditi Raghunathan, Greg Valiant, James Zou

We generalize this extrapolation and related unseen estimation problems to the multiple population setting, where population $j$ has an unknown distribution $D_j$ from which we observe $n_j$ samples.

Learning Mixture of Gaussians with Streaming Data

no code implementations NeurIPS 2017 Aditi Raghunathan, Ravishankar Krishnaswamy, Prateek Jain

However, by using a streaming version of the classical (soft-thresholding-based) EM method that exploits the Gaussian distribution explicitly, we show that for a mixture of two Gaussians the true means can be estimated consistently, with estimation error decreasing at nearly optimal rate, and tending to $0$ for $N\rightarrow \infty$.

Estimation from Indirect Supervision with Linear Moments

1 code implementation10 Aug 2016 Aditi Raghunathan, Roy Frostig, John Duchi, Percy Liang

In structured prediction problems where we have indirect supervision of the output, maximum marginal likelihood faces two computational obstacles: non-convexity of the objective and intractability of even a single gradient computation.

Structured Prediction

Probabilistic Dependency Networks for Prediction and Diagnostics

no code implementations13 Aug 2015 Narayanan U. Edakunni, aditi raghunathan, Abhishek Tripathi, John Handley, Fredric Roulland

Research in transportation frequently involve modelling and predicting attributes of events that occur at regular intervals.

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