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08/08/2023
Probabilistic Invariant Learning with Randomized Linear Classifiers
Designing models that are both expressive and preserve known invariances...
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06/12/2023
Benchmarking Neural Network Training Algorithms
Training algorithms, broadly construed, are an essential part of every d...
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10/04/2022
Contrastive Learning Can Find An Optimal Basis For Approximately View-Invariant Functions
Contrastive learning is a powerful framework for learning self-supervise...
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06/27/2022
Learning To Cut By Looking Ahead: Cutting Plane Selection via Imitation Learning
Cutting planes are essential for solving mixed-integer linear problems (...
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03/04/2022
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights
Combinatorial optimization is a well-established area in operations rese...
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02/17/2022
Augment with Care: Contrastive Learning for the Boolean Satisfiability Problem
Supervised learning can improve the design of state-of-the-art solvers f...
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02/09/2022
Bayesian Nonparametrics for Offline Skill Discovery
Skills or low-level policies in reinforcement learning are temporally ex...
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11/11/2021
Learning Generalized Gumbel-max Causal Mechanisms
To perform counterfactual reasoning in Structural Causal Models (SCMs), ...
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09/24/2021
Unbiased Gradient Estimation with Balanced Assignments for Mixtures of Experts
Training large-scale mixture of experts models efficiently on modern har...
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06/21/2021
Lossy Compression for Lossless Prediction
Most data is automatically collected and only ever "seen" by algorithms....
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05/28/2021
Learning to Extend Program Graphs to Work-in-Progress Code
Source code spends most of its time in a broken or incomplete state duri...
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02/22/2021
Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding
Latent variable models have been successfully applied in lossless compre...
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02/08/2021
Oops I Took A Gradient: Scalable Sampling for Discrete Distributions
We propose a general and scalable approximate sampling strategy for prob...
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10/09/2020
Rao-Blackwellizing the Straight-Through Gumbel-Softmax Gradient Estimator
Gradient estimation in models with discrete latent variables is a challe...
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07/07/2020
Learning Branching Heuristics for Propositional Model Counting
Propositional model counting or #SAT is the problem of computing the num...
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06/15/2020
Gradient Estimation with Stochastic Softmax Tricks
The Gumbel-Max trick is the basis of many relaxed gradient estimators. T...
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10/11/2019
On Empirical Comparisons of Optimizers for Deep Learning
Selecting an optimizer is a central step in the contemporary deep learni...
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06/14/2019
Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces
Direct optimization is an appealing approach to differentiating through ...
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01/17/2019
Hierarchical Representations with Poincaré Variational Auto-Encoders
The Variational Auto-Encoder (VAE) model has become widely popular as a ...
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10/09/2018
Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives
Deep latent variable models have become a popular model choice due to th...
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09/13/2018
Hamiltonian Descent Methods
We propose a family of optimization methods that achieve linear converge...
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07/04/2018
Conditional Neural Processes
Deep neural networks excel at function approximation, yet they are typic...
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02/13/2018
Tighter Variational Bounds are Not Necessarily Better
We provide theoretical and empirical evidence that using tighter evidenc...
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05/25/2017
Filtering Variational Objectives
When used as a surrogate objective for maximum likelihood estimation in ...
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03/21/2017
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models
Learning in models with discrete latent variables is challenging due to ...
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11/02/2016
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
The reparameterization trick enables optimizing large scale stochastic c...
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12/20/2014
Move Evaluation in Go Using Deep Convolutional Neural Networks
The game of Go is more challenging than other board games, due to the di...
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10/31/2014
A* Sampling
The problem of drawing samples from a discrete distribution can be conve...
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01/02/2014