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07/12/2023
No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-based Language Models
The computation necessary for training Transformer-based language models...
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01/27/2023
DAG Learning on the Permutahedron
We propose a continuous optimization framework for discovering a latent ...
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12/21/2022
Adapting to Latent Subgroup Shifts via Concepts and Proxies
We address the problem of unsupervised domain adaptation when the source...
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01/28/2022
Local Latent Space Bayesian Optimization over Structured Inputs
Bayesian optimization over the latent spaces of deep autoencoder models ...
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09/15/2021
MPC-Friendly Commitments for Publicly Verifiable Covert Security
We address the problem of efficiently verifying a commitment in a two-pa...
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12/21/2020
Barking up the right tree: an approach to search over molecule synthesis DAGs
When designing new molecules with particular properties, it is not only ...
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10/09/2020
Learning Binary Trees via Sparse Relaxation
One of the most classical problems in machine learning is how to learn b...
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06/11/2020
A Class of Algorithms for General Instrumental Variable Models
Causal treatment effect estimation is a key problem that arises in a var...
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03/16/2020
A Survey on Contextual Embeddings
Contextual embeddings, such as ELMo and BERT, move beyond global word re...
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03/03/2020
Differentiable Causal Backdoor Discovery
Discovering the causal effect of a decision is critical to nearly all fo...
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11/05/2019
Cumulo: A Dataset for Learning Cloud Classes
One of the greatest sources of uncertainty in future climate projections...
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07/08/2019
QUOTIENT: Two-Party Secure Neural Network Training and Prediction
Recently, there has been a wealth of effort devoted to the design of sec...
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07/01/2019
The Sensitivity of Counterfactual Fairness to Unmeasured Confounding
Causal approaches to fairness have seen substantial recent interest, bot...
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06/12/2019
A Model to Search for Synthesizable Molecules
Deep generative models are able to suggest new organic molecules by gene...
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01/13/2019
Gradient Regularized Budgeted Boosting
As machine learning transitions increasingly towards real world applicat...
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06/09/2018
TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service
Machine learning methods are widely used for a variety of prediction pro...
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06/08/2018
Blind Justice: Fairness with Encrypted Sensitive Attributes
Recent work has explored how to train machine learning models which do n...
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06/06/2018
Causal Interventions for Fairness
Most approaches in algorithmic fairness constrain machine learning metho...
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05/23/2018
Predicting Electron Paths
Chemical reactions can be described as the stepwise redistribution of el...
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05/15/2018
Causal Reasoning for Algorithmic Fairness
In this work, we argue for the importance of causal reasoning in creatin...
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12/05/2017
Learning a Generative Model for Validity in Complex Discrete Structures
Deep generative models have been successfully used to learn representati...
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03/20/2017
Counterfactual Fairness
Machine learning can impact people with legal or ethical consequences wh...
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03/06/2017
Grammar Variational Autoencoder
Deep generative models have been wildly successful at learning coherent ...
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11/12/2016
GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution
Generative Adversarial Networks (GAN) have limitations when the goal is ...
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12/17/2015
Private Causal Inference
Causal inference deals with identifying which random variables "cause" o...
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11/19/2015
Deep Manifold Traversal: Changing Labels with Convolutional Features
Many tasks in computer vision can be cast as a "label changing" problem,...
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01/16/2015
Differentially Private Bayesian Optimization
Bayesian optimization is a powerful tool for fine-tuning the hyper-param...
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12/04/2014
Image Data Compression for Covariance and Histogram Descriptors
Covariance and histogram image descriptors provide an effective way to c...
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10/09/2012
