b'Richard Zemel'

research

∙ 08/08/2023

FLIRT: Feedback Loop In-context Red Teaming

Warning: this paper contains content that may be inappropriate or offens...

0 Ninareh Mehrabi, et al. ∙

research

∙ 05/17/2023

"I'm fully who I am": Towards Centering Transgender and Non-Binary Voices to Measure Biases in Open Language Generation

Transgender and non-binary (TGNB) individuals disproportionately experie...

1 Anaelia Ovalle, et al. ∙

research

∙ 04/13/2023

SURFSUP: Learning Fluid Simulation for Novel Surfaces

Modeling the mechanics of fluid in complex scenes is vital to applicatio...

0 Arjun Mani, et al. ∙

research

∙ 12/27/2022

Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions

Rigorous guarantees about the performance of predictive algorithms are n...

13 Jake C. Snell, et al. ∙

research

∙ 11/17/2022

Is the Elephant Flying? Resolving Ambiguities in Text-to-Image Generative Models

Natural language often contains ambiguities that can lead to misinterpre...

5 Ninareh Mehrabi, et al. ∙

research

∙ 05/26/2022

Differentially Private Decoding in Large Language Models

Recent large-scale natural language processing (NLP) systems use a pre-t...

8 Jimit Majmudar, et al. ∙

research

∙ 05/02/2022

Semantically Informed Slang Interpretation

Slang is a predominant form of informal language making flexible and ext...

4 Zhewei Sun, et al. ∙

research

∙ 04/07/2022

Mapping the Multilingual Margins: Intersectional Biases of Sentiment Analysis Systems in English, Spanish, and Arabic

As natural language processing systems become more widespread, it is nec...

3 Antonio Camara, et al. ∙

research

∙ 02/14/2022

Deep Ensembles Work, But Are They Necessary?

Ensembling neural networks is an effective way to increase accuracy, and...

5 Taiga Abe, et al. ∙

research

∙ 01/26/2022

Variational Model Inversion Attacks

Given the ubiquity of deep neural networks, it is important that these m...

11 Kuan-Chieh Wang, et al. ∙

research

∙ 12/29/2021

Disentanglement and Generalization Under Correlation Shifts

Correlations between factors of variation are prevalent in real-world da...

16 Christina M. Funke, et al. ∙

research

∙ 10/25/2021

Identifying and Benchmarking Natural Out-of-Context Prediction Problems

Deep learning systems frequently fail at out-of-context (OOC) prediction...

3 David Madras, et al. ∙

research

∙ 09/13/2021

Online Unsupervised Learning of Visual Representations and Categories

Real world learning scenarios involve a nonstationary distribution of cl...

19 Mengye Ren, et al. ∙

research

∙ 06/25/2021

NP-DRAW: A Non-Parametric Structured Latent Variable Modelfor Image Generation

In this paper, we present a non-parametric structured latent variable mo...

17 Xiaohui Zeng, et al. ∙

research

∙ 05/14/2021

Learning a Universal Template for Few-shot Dataset Generalization

Few-shot dataset generalization is a challenging variant of the well-stu...

15 Eleni Triantafillou, et al. ∙

research

∙ 04/22/2021

Analyzing Monotonic Linear Interpolation in Neural Network Loss Landscapes

Linear interpolation between initial neural network parameters and conve...

5 James Lucas, et al. ∙

research

∙ 02/03/2021

A Computational Framework for Slang Generation

Slang is a common type of informal language, but its flexible nature and...

14 Zhewei Sun, et al. ∙

research

∙ 12/14/2020

A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks

In this paper, we derive generalization bounds for the two primary class...

10 Renjie Liao, et al. ∙

research

∙ 12/10/2020

Flexible Few-Shot Learning with Contextual Similarity

Existing approaches to few-shot learning deal with tasks that have persi...

17 Mengye Ren, et al. ∙

research

∙ 11/12/2020

Fairness and Robustness in Invariant Learning: A Case Study in Toxicity Classification

Robustness is of central importance in machine learning and has given ri...

58 Robert Adragna, et al. ∙

research

∙ 10/14/2020

Exchanging Lessons Between Algorithmic Fairness and Domain Generalization

Standard learning approaches are designed to perform well on average for...

0 Elliot Creager, et al. ∙

research

∙ 10/14/2020

Theoretical bounds on estimation error for meta-learning

Machine learning models have traditionally been developed under the assu...

20 James Lucas, et al. ∙

research

∙ 08/27/2020

SketchEmbedNet: Learning Novel Concepts by Imitating Drawings

Sketch drawings are an intuitive visual domain that appeals to human ins...

0 Alexander Wang, et al. ∙

research

∙ 07/31/2020

Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach

Most recommender systems (RS) research assumes that a user's utility can...

4 Martin Mladenov, et al. ∙

research

∙ 07/20/2020

Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes

Few-shot classification (FSC), the task of adapting a classifier to unse...

0 Jake Snell, et al. ∙

research

∙ 06/18/2020

Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data

Standard causal discovery methods must fit a new model whenever they enc...

41 Sindy Löwe, et al. ∙

research

∙ 04/16/2020

Shortcut Learning in Deep Neural Networks

Deep learning has triggered the current rise of artificial intelligence ...

3 Robert Geirhos, et al. ∙

research

∙ 02/13/2020

Cutting out the Middle-Man: Training and Evaluating Energy-Based Models without Sampling

We present a new method for evaluating and training unnormalized density...

1 Will Grathwohl, et al. ∙

research

∙ 11/06/2019

A Divergence Minimization Perspective on Imitation Learning Methods

In many settings, it is desirable to learn decision-making and control p...

15 Seyed Kamyar Seyed Ghasemipour, et al. ∙

research

∙ 09/18/2019

Causal Modeling for Fairness in Dynamical Systems

In this work, we present causal directed acyclic graphs (DAGs) as a unif...

12 Elliot Creager, et al. ∙

research

∙ 06/22/2019

Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models

We introduce a new molecular dataset, named Alchemy, for developing mach...

15 Guangyong Chen, et al. ∙

research

∙ 06/06/2019

Flexibly Fair Representation Learning by Disentanglement

We consider the problem of learning representations that achieve group a...

4 Elliot Creager, et al. ∙

research

∙ 06/04/2019

Conditional Generative Models are not Robust

Class-conditional generative models are an increasingly popular approach...

0 Ethan Fetaya, et al. ∙

research

∙ 03/26/2019

High-Level Perceptual Similarity is Enabled by Learning Diverse Tasks

Predicting human perceptual similarity is a challenging subject of ongoi...

10 Amir Rosenfeld, et al. ∙

research

∙ 12/14/2018

Learning Latent Subspaces in Variational Autoencoders

Variational autoencoders (VAEs) are widely used deep generative models c...

0 Jack Klys, et al. ∙

research

∙ 11/01/2018

Excessive Invariance Causes Adversarial Vulnerability

Despite their impressive performance, deep neural networks exhibit strik...

16 Jörn-Henrik Jacobsen, et al. ∙

research

∙ 10/08/2018

Understanding the Origins of Bias in Word Embeddings

The power of machine learning systems not only promises great technical ...

0 Marc-Etienne Brunet, et al. ∙

research

∙ 09/08/2018

Neural Guided Constraint Logic Programming for Program Synthesis

Synthesizing programs using example input/outputs is a classic problem i...

0 Lisa Zhang, et al. ∙

research

∙ 09/07/2018

Fairness Through Causal Awareness: Learning Latent-Variable Models for Biased Data

How do we learn from biased data? Historical datasets often reflect hist...

4 David Madras, et al. ∙

research

∙ 08/09/2018

The Elephant in the Room

We showcase a family of common failures of state-of-the art object detec...

6 Amir Rosenfeld, et al. ∙

research

∙ 06/27/2018

Adversarial Distillation of Bayesian Neural Network Posteriors

Bayesian neural networks (BNNs) allow us to reason about uncertainty in ...

4 Kuan-Chieh Wang, et al. ∙

research

∙ 04/01/2018

Aggregated Momentum: Stability Through Passive Damping

Momentum is a simple and widely used trick which allows gradient-based o...

0 James Lucas, et al. ∙

research

∙ 03/21/2018

Inference in Probabilistic Graphical Models by Graph Neural Networks

A useful computation when acting in a complex environment is to infer th...

0 KiJung Yoon, et al. ∙

research

∙ 03/16/2018

Reviving and Improving Recurrent Back-Propagation

In this paper, we revisit the recurrent back-propagation (RBP) algorithm...

0 Renjie Liao, et al. ∙

research

∙ 03/16/2018

Graph Partition Neural Networks for Semi-Supervised Classification

We present graph partition neural networks (GPNN), an extension of graph...

0 Renjie Liao, et al. ∙

research

∙ 02/17/2018

Learning Adversarially Fair and Transferable Representations

In this work, we advocate for representation learning as the key to miti...

0 David Madras, et al. ∙

research

∙ 02/13/2018

Neural Relational Inference for Interacting Systems

Interacting systems are prevalent in nature, from dynamical systems in p...

0 Thomas Kipf, et al. ∙

research

∙ 11/17/2017

Predict Responsibly: Increasing Fairness by Learning To Defer

Machine learning systems, which are often used for high-stakes decisions...

0 David Madras, et al. ∙

research

∙ 06/19/2017

Dualing GANs

Generative adversarial nets (GANs) are a promising technique for modelin...

0 Yujia Li, et al. ∙

research

∙ 05/24/2017

Causal Effect Inference with Deep Latent-Variable Models

Learning individual-level causal effects from observational data, such a...

0 Christos Louizos, et al. ∙

Richard Zemel

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