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07/25/2023
QuIP: 2-Bit Quantization of Large Language Models With Guarantees
This work studies post-training parameter quantization in large language...
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06/14/2023
InfoDiffusion: Representation Learning Using Information Maximizing Diffusion Models
While diffusion models excel at generating high-quality samples, their l...
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06/01/2023
Calibrated Propensity Scores for Causal Effect Estimation
Propensity scores are commonly used to balance observed covariates while...
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02/23/2023
Online Calibrated Regression for Adversarially Robust Forecasting
Accurately estimating uncertainty is a crucial component of decision-mak...
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10/16/2022
Model Criticism for Long-Form Text Generation
Language models have demonstrated the ability to generate highly fluent ...
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06/14/2022
Energy Flows: Towards Determinant-Free Training of Normalizing Flows
Normalizing flows are a popular approach for constructing probabilistic ...
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05/30/2022
Gradient Backpropagation Through Combinatorial Algorithms: Identity with Projection Works
Embedding discrete solvers as differentiable layers has given modern dee...
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05/24/2022
Semi-Parametric Deep Neural Networks in Linear Time and Memory
Recent advances in deep learning have been driven by large-scale paramet...
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03/18/2022
Multi-Modal Causal Inference with Deep Structural Equation Models
Accounting for the effects of confounders is one of the central challeng...
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12/14/2021
Calibrated and Sharp Uncertainties in Deep Learning via Simple Density Estimation
Predictive uncertainties can be characterized by two properties–calibrat...
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12/12/2021
Quantifying and Understanding Adversarial Examples in Discrete Input Spaces
Modern classification algorithms are susceptible to adversarial examples...
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12/09/2021
Autoregressive Quantile Flows for Predictive Uncertainty Estimation
Numerous applications of machine learning involve predicting flexible pr...
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12/08/2021
Calibration Improves Bayesian Optimization
Bayesian optimization is a procedure that allows obtaining the global op...
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09/14/2019
Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations
Learning representations that accurately capture long-range dependencies...
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06/19/2019
Calibrated Model-Based Deep Reinforcement Learning
Estimates of predictive uncertainty are important for accurate model-bas...
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07/01/2018
Accurate Uncertainties for Deep Learning Using Calibrated Regression
Methods for reasoning under uncertainty are a key building block of accu...
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05/27/2018
Adversarial Constraint Learning for Structured Prediction
Constraint-based learning reduces the burden of collecting labels by hav...
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11/07/2017
Neural Variational Inference and Learning in Undirected Graphical Models
Many problems in machine learning are naturally expressed in the languag...
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08/02/2017
Audio Super Resolution using Neural Networks
We introduce a new audio processing technique that increases the samplin...
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01/29/2015
Tensor Factorization via Matrix Factorization
Tensor factorization arises in many machine learning applications, such ...
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02/25/2014