Percy Liang

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Stanford University

Assistant Professor at Stanford University

Robustness to Spurious Correlations via Human Annotations

The reliability of machine learning systems critically assumes that the ...
07/13/2020 ∙ by Megha Srivastava, et al. ∙ 14 ∙ share

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Learning Abstract Models for Strategic Exploration and Fast Reward Transfer

Model-based reinforcement learning (RL) is appealing because (i) it enab...
07/12/2020 ∙ by Evan Zheran Liu, et al. ∙ 12 ∙ share

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Concept Bottleneck Models

We seek to learn models that we can interact with using high-level conce...
07/09/2020 ∙ by Pang Wei Koh, et al. ∙ 14 ∙ share

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Simplifying Models with Unlabeled Output Data

We focus on prediction problems with high-dimensional outputs that are s...
06/29/2020 ∙ by Sang Michael Xie, et al. ∙ 8 ∙ share

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Selective Question Answering under Domain Shift

To avoid giving wrong answers, question answering (QA) models need to kn...
06/16/2020 ∙ by Amita Kamath, et al. ∙ 0 ∙ share

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Graph-based, Self-Supervised Program Repair from Diagnostic Feedback

We consider the problem of learning to repair programs from diagnostic f...
05/20/2020 ∙ by Michihiro Yasunaga, et al. ∙ 0 ∙ share

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Enabling Language Models to Fill in the Blanks

We present a simple approach for text infilling, the task of predicting ...
05/11/2020 ∙ by Chris Donahue, et al. ∙ 0 ∙ share

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An Investigation of Why Overparameterization Exacerbates Spurious Correlations

We study why overparameterization – increasing model size well beyond th...
05/09/2020 ∙ by Shiori Sagawa, et al. ∙ 0 ∙ share

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ExpBERT: Representation Engineering with Natural Language Explanations

Suppose we want to specify the inductive bias that married couples typic...
05/05/2020 ∙ by Shikhar Murty, et al. ∙ 13 ∙ share

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Robust Encodings: A Framework for Combating Adversarial Typos

Despite excellent performance on many tasks, NLP systems are easily fool...
05/04/2020 ∙ by Erik Jones, et al. ∙ 0 ∙ share

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Understanding Self-Training for Gradual Domain Adaptation

Machine learning systems must adapt to data distributions that evolve ov...
02/26/2020 ∙ by Ananya Kumar, et al. ∙ 5 ∙ share

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Understanding and Mitigating the Tradeoff Between Robustness and Accuracy

Adversarial training augments the training set with perturbations to imp...
02/25/2020 ∙ by Aditi Raghunathan, et al. ∙ 42 ∙ share

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Noise Induces Loss Discrepancy Across Groups for Linear Regression

We study the effect of feature noise (measurement error) on the discrepa...
11/22/2019 ∙ by Fereshte Khani, et al. ∙ 0 ∙ share

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Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization

Overparameterized neural networks can be highly accurate on average on a...
11/20/2019 ∙ by Shiori Sagawa, et al. ∙ 0 ∙ share

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Learning Autocomplete Systems as a Communication Game

We study textual autocomplete—the task of predicting a full sentence fro...
11/16/2019 ∙ by Mina Lee, et al. ∙ 0 ∙ share

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Shaping Visual Representations with Language for Few-shot Classification

Language is designed to convey useful information about the world, thus ...
11/06/2019 ∙ by Jesse Mu, et al. ∙ 0 ∙ share

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Verified Uncertainty Calibration

Applications such as weather forecasting and personalized medicine deman...
09/23/2019 ∙ by Ananya Kumar, et al. ∙ 2 ∙ share

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Designing and Interpreting Probes with Control Tasks

Probes, supervised models trained to predict properties (like parts-of-s...
09/08/2019 ∙ by John Hewitt, et al. ∙ 0 ∙ share

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Distributionally Robust Language Modeling

Language models are generally trained on data spanning a wide range of t...
09/04/2019 ∙ by Yonatan Oren, et al. ∙ 0 ∙ share

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Certified Robustness to Adversarial Word Substitutions

State-of-the-art NLP models can often be fooled by adversaries that appl...
09/03/2019 ∙ by Robin Jia, et al. ∙ 0 ∙ share

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A Tight Analysis of Greedy Yields Subexponential Time Approximation for Uniform Decision Tree

Decision Tree is a classic formulation of active learning: given n hypot...
06/26/2019 ∙ by Ray Li, et al. ∙ 0 ∙ share

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Selection Via Proxy: Efficient Data Selection For Deep Learning

Data selection methods such as active learning and core-set selection ar...
06/26/2019 ∙ by Cody Coleman, et al. ∙ 12 ∙ share

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Adversarial Training Can Hurt Generalization

While adversarial training can improve robust accuracy (against an adver...
06/14/2019 ∙ by Aditi Raghunathan, et al. ∙ 0 ∙ share

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SPoC: Search-based Pseudocode to Code

We consider the task of mapping pseudocode to long programs that are fun...
06/12/2019 ∙ by Sumith Kulal, et al. ∙ 0 ∙ share

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Maximum Weighted Loss Discrepancy

Though machine learning algorithms excel at minimizing the average loss ...
06/08/2019 ∙ by Fereshte Khani, et al. ∙ 0 ∙ share

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Unlabeled Data Improves Adversarial Robustness

We demonstrate, theoretically and empirically, that adversarial robustne...
05/31/2019 ∙ by Yair Carmon, et al. ∙ 0 ∙ share

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On the Accuracy of Influence Functions for Measuring Group Effects

Influence functions estimate the effect of removing particular training ...
05/30/2019 ∙ by Pang Wei Koh, et al. ∙ 0 ∙ share

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Pre-training Graph Neural Networks

Many applications of machine learning in science and medicine, including...
05/29/2019 ∙ by Weihua Hu, et al. ∙ 23 ∙ share

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Pun Generation with Surprise

We tackle the problem of generating a pun sentence given a pair of homop...
04/15/2019 ∙ by He He, et al. ∙ 0 ∙ share

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Unifying Human and Statistical Evaluation for Natural Language Generation

How can we measure whether a natural language generation system produces...
04/04/2019 ∙ by Tatsunori B. Hashimoto, et al. ∙ 0 ∙ share

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Defending against Whitebox Adversarial Attacks via Randomized Discretization

Adversarial perturbations dramatically decrease the accuracy of state-of...
03/25/2019 ∙ by Yuchen Zhang, et al. ∙ 0 ∙ share

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Ambitious Data Science Can Be Painless

Modern data science research can involve massive computational experimen...
01/25/2019 ∙ by Hatef Monajemi, et al. ∙ 0 ∙ share

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Uncertainty Sampling is Preconditioned Stochastic Gradient Descent on Zero-One Loss

Uncertainty sampling, a popular active learning algorithm, is used to re...
12/05/2018 ∙ by Stephen Mussmann, et al. ∙ 0 ∙ share

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A Retrieve-and-Edit Framework for Predicting Structured Outputs

For the task of generating complex outputs such as source code, editing ...
12/04/2018 ∙ by Tatsunori B. Hashimoto, et al. ∙ 0 ∙ share

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FrAngel: Component-Based Synthesis with Control Structures

In component-based program synthesis, the synthesizer generates a progra...
11/13/2018 ∙ by Kensen Shi, et al. ∙ 0 ∙ share

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Semidefinite relaxations for certifying robustness to adversarial examples

Despite their impressive performance on diverse tasks, neural networks f...
11/02/2018 ∙ by Aditi Raghunathan, et al. ∙ 0 ∙ share

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Stronger Data Poisoning Attacks Break Data Sanitization Defenses

Machine learning models trained on data from the outside world can be co...
11/02/2018 ∙ by Pang Wei Koh, et al. ∙ 0 ∙ share

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Transforming Question Answering Datasets Into Natural Language Inference Datasets

Existing datasets for natural language inference (NLI) have propelled re...
09/09/2018 ∙ by Dorottya Demszky, et al. ∙ 0 ∙ share

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Textual Analogy Parsing: What's Shared and What's Compared among Analogous Facts

To understand a sentence like "whereas only 10 below the poverty line, 2...
09/07/2018 ∙ by Matthew Lamm, et al. ∙ 0 ∙ share

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Decoupling Strategy and Generation in Negotiation Dialogues

We consider negotiation settings in which two agents use natural languag...
08/29/2018 ∙ by He He, et al. ∙ 0 ∙ share

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Mapping Natural Language Commands to Web Elements

The web provides a rich, open-domain environment with textual, structura...
08/28/2018 ∙ by Panupong Pasupat, et al. ∙ 0 ∙ share

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QuAC : Question Answering in Context

We present QuAC, a dataset for Question Answering in Context that contai...
08/21/2018 ∙ by Eunsol Choi, et al. ∙ 2 ∙ share

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Inferring Multi-Dimensional Rates of Aging from Cross-Sectional Data

Modeling how individuals evolve over time is a fundamental problem in th...
07/12/2018 ∙ by Emma Pierson, et al. ∙ 4 ∙ share

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The price of debiasing automatic metrics in natural language evaluation

For evaluating generation systems, automatic metrics such as BLEU cost n...
07/06/2018 ∙ by Arun Tejasvi Chaganty, et al. ∙ 0 ∙ share

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Fairness Without Demographics in Repeated Loss Minimization

Machine learning models (e.g., speech recognizers) are usually trained t...
06/20/2018 ∙ by Tatsunori B. Hashimoto, et al. ∙ 0 ∙ share

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On the Relationship between Data Efficiency and Error for Uncertainty Sampling

While active learning offers potential cost savings, the actual data eff...
06/15/2018 ∙ by Stephen Mussmann, et al. ∙ 0 ∙ share

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Know What You Don't Know: Unanswerable Questions for SQuAD

Extractive reading comprehension systems can often locate the correct an...
06/11/2018 ∙ by Pranav Rajpurkar, et al. ∙ 0 ∙ share

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Planning, Inference and Pragmatics in Sequential Language Games

We study sequential language games in which two players, each with priva...
05/30/2018 ∙ by Fereshte Khani, et al. ∙ 0 ∙ share

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Training Classifiers with Natural Language Explanations

Training accurate classifiers requires many labels, but each label provi...
05/10/2018 ∙ by Braden Hancock, et al. ∙ 0 ∙ share

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Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer

We consider the task of text attribute transfer: transforming a sentence...
04/17/2018 ∙ by Juncen Li, et al. ∙ 0 ∙ share

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Percy Liang

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