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05/29/2023
Beyond Confidence: Reliable Models Should Also Consider Atypicality
While most machine learning models can provide confidence in their predi...
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02/11/2023
Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks
Recent advances in instruction-following large language models (LLMs) ha...
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02/20/2021
Learning Neural Network Subspaces
Recent observations have advanced our understanding of the neural networ...
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07/09/2020
AdaScale SGD: A User-Friendly Algorithm for Distributed Training
When using large-batch training to speed up stochastic gradient descent,...
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06/18/2020
Set Distribution Networks: a Generative Model for Sets of Images
Images with shared characteristics naturally form sets. For example, in ...
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06/13/2020
Equivariant Neural Rendering
We propose a framework for learning neural scene representations directl...
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05/08/2020
Beyond Accuracy: Behavioral Testing of NLP models with CheckList
Although measuring held-out accuracy has been the primary approach to ev...
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10/29/2019
Adversarial Fisher Vectors for Unsupervised Representation Learning
We examine Generative Adversarial Networks (GANs) through the lens of de...
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05/15/2019
Addressing the Loss-Metric Mismatch with Adaptive Loss Alignment
In most machine learning training paradigms a fixed, often handcrafted, ...
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09/20/2018
Over-Optimization of Academic Publishing Metrics: Observing Goodhart's Law in Action
The academic publishing world is changing significantly, with ever-growi...
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07/20/2018
A Fast, Principled Working Set Algorithm for Exploiting Piecewise Linear Structure in Convex Problems
By reducing optimization to a sequence of smaller subproblems, working s...
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07/11/2018
VTA: An Open Hardware-Software Stack for Deep Learning
Hardware acceleration is an enabler for ubiquitous and efficient deep le...
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05/21/2018
Learning to Optimize Tensor Programs
We introduce a learning-based framework to optimize tensor programs for ...
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05/01/2018
Compact Factorization of Matrices Using Generalized Round-Rank
Matrix factorization is a well-studied task in machine learning for comp...
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02/12/2018
TVM: An Automated End-to-End Optimizing Compiler for Deep Learning
There is an increasing need to bring machine learning to a wide diversit...
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02/12/2018
TVM: End-to-End Optimization Stack for Deep Learning
Scalable frameworks, such as TensorFlow, MXNet, Caffe, and PyTorch drive...
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11/22/2016
Programs as Black-Box Explanations
Recent work in model-agnostic explanations of black-box machine learning...
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11/17/2016
Nothing Else Matters: Model-Agnostic Explanations By Identifying Prediction Invariance
At the core of interpretable machine learning is the question of whether...
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06/16/2016
Model-Agnostic Interpretability of Machine Learning
Understanding why machine learning models behave the way they do empower...
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06/01/2016
Scaling Submodular Maximization via Pruned Submodularity Graphs
We propose a new random pruning method (called "submodular sparsificatio...
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02/16/2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Despite widespread adoption, machine learning models remain mostly black...
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06/22/2014
Divide-and-Conquer Learning by Anchoring a Conical Hull
We reduce a broad class of machine learning problems, usually addressed ...
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02/17/2014
Stochastic Gradient Hamiltonian Monte Carlo
Hamiltonian Monte Carlo (HMC) sampling methods provide a mechanism for d...
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01/15/2014
Optimal Value of Information in Graphical Models
Many real-world decision making tasks require us to choose among several...
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01/15/2014
Efficient Informative Sensing using Multiple Robots
The need for efficient monitoring of spatio-temporal dynamics in large e...
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06/27/2012
Hierarchical Exploration for Accelerating Contextual Bandits
Contextual bandit learning is an increasingly popular approach to optimi...
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04/11/2012
Concept Modeling with Superwords
In information retrieval, a fundamental goal is to transform a document ...
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06/07/2010