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08/08/2023
FLIRT: Feedback Loop In-context Red Teaming
Warning: this paper contains content that may be inappropriate or offens...
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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...
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04/13/2023
SURFSUP: Learning Fluid Simulation for Novel Surfaces
Modeling the mechanics of fluid in complex scenes is vital to applicatio...
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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...
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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...
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05/26/2022
Differentially Private Decoding in Large Language Models
Recent large-scale natural language processing (NLP) systems use a pre-t...
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05/02/2022
Semantically Informed Slang Interpretation
Slang is a predominant form of informal language making flexible and ext...
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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...
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02/14/2022
Deep Ensembles Work, But Are They Necessary?
Ensembling neural networks is an effective way to increase accuracy, and...
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01/26/2022
Variational Model Inversion Attacks
Given the ubiquity of deep neural networks, it is important that these m...
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12/29/2021
Disentanglement and Generalization Under Correlation Shifts
Correlations between factors of variation are prevalent in real-world da...
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10/25/2021
Identifying and Benchmarking Natural Out-of-Context Prediction Problems
Deep learning systems frequently fail at out-of-context (OOC) prediction...
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09/13/2021
Online Unsupervised Learning of Visual Representations and Categories
Real world learning scenarios involve a nonstationary distribution of cl...
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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...
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05/14/2021
Learning a Universal Template for Few-shot Dataset Generalization
Few-shot dataset generalization is a challenging variant of the well-stu...
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04/22/2021
Analyzing Monotonic Linear Interpolation in Neural Network Loss Landscapes
Linear interpolation between initial neural network parameters and conve...
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02/03/2021
A Computational Framework for Slang Generation
Slang is a common type of informal language, but its flexible nature and...
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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...
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12/10/2020
Flexible Few-Shot Learning with Contextual Similarity
Existing approaches to few-shot learning deal with tasks that have persi...
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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...
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10/14/2020
Exchanging Lessons Between Algorithmic Fairness and Domain Generalization
Standard learning approaches are designed to perform well on average for...
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10/14/2020
Theoretical bounds on estimation error for meta-learning
Machine learning models have traditionally been developed under the assu...
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08/27/2020
SketchEmbedNet: Learning Novel Concepts by Imitating Drawings
Sketch drawings are an intuitive visual domain that appeals to human ins...
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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...
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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...
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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...
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04/16/2020
Shortcut Learning in Deep Neural Networks
Deep learning has triggered the current rise of artificial intelligence ...
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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...
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11/06/2019
A Divergence Minimization Perspective on Imitation Learning Methods
In many settings, it is desirable to learn decision-making and control p...
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09/18/2019
Causal Modeling for Fairness in Dynamical Systems
In this work, we present causal directed acyclic graphs (DAGs) as a unif...
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06/22/2019
Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models
We introduce a new molecular dataset, named Alchemy, for developing mach...
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06/06/2019
Flexibly Fair Representation Learning by Disentanglement
We consider the problem of learning representations that achieve group a...
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06/04/2019
Conditional Generative Models are not Robust
Class-conditional generative models are an increasingly popular approach...
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03/26/2019
High-Level Perceptual Similarity is Enabled by Learning Diverse Tasks
Predicting human perceptual similarity is a challenging subject of ongoi...
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12/14/2018
Learning Latent Subspaces in Variational Autoencoders
Variational autoencoders (VAEs) are widely used deep generative models c...
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11/01/2018
Excessive Invariance Causes Adversarial Vulnerability
Despite their impressive performance, deep neural networks exhibit strik...
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10/08/2018
Understanding the Origins of Bias in Word Embeddings
The power of machine learning systems not only promises great technical ...
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09/08/2018
Neural Guided Constraint Logic Programming for Program Synthesis
Synthesizing programs using example input/outputs is a classic problem i...
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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...
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08/09/2018
The Elephant in the Room
We showcase a family of common failures of state-of-the art object detec...
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06/27/2018
Adversarial Distillation of Bayesian Neural Network Posteriors
Bayesian neural networks (BNNs) allow us to reason about uncertainty in ...
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04/01/2018
Aggregated Momentum: Stability Through Passive Damping
Momentum is a simple and widely used trick which allows gradient-based o...
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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...
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03/16/2018
Reviving and Improving Recurrent Back-Propagation
In this paper, we revisit the recurrent back-propagation (RBP) algorithm...
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03/16/2018
Graph Partition Neural Networks for Semi-Supervised Classification
We present graph partition neural networks (GPNN), an extension of graph...
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02/17/2018
Learning Adversarially Fair and Transferable Representations
In this work, we advocate for representation learning as the key to miti...
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02/13/2018
Neural Relational Inference for Interacting Systems
Interacting systems are prevalent in nature, from dynamical systems in p...
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11/17/2017
Predict Responsibly: Increasing Fairness by Learning To Defer
Machine learning systems, which are often used for high-stakes decisions...
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06/19/2017
Dualing GANs
Generative adversarial nets (GANs) are a promising technique for modelin...
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05/24/2017
