research
∙
06/16/2023
Ï€2vec: Policy Representations with Successor Features
This paper describes π2vec, a method for representing behaviors of black...
share
research
∙
05/13/2023
GPT-Sentinel: Distinguishing Human and ChatGPT Generated Content
This paper presents a novel approach for detecting ChatGPT-generated vs....
research
∙
10/10/2022
Multi-step Planning for Automated Hyperparameter Optimization with OptFormer
As machine learning permeates more industries and models become more exp...
research
∙
05/26/2022
Towards Learning Universal Hyperparameter Optimizers with Transformers
Meta-learning hyperparameter optimization (HPO) algorithms from prior ex...
research
∙
05/12/2022
A Generalist Agent
Inspired by progress in large-scale language modeling, we apply a simila...
research
∙
02/05/2022
Importance Weighting Approach in Kernel Bayes' Rule
We study a nonparametric approach to Bayesian computation via feature me...
research
∙
10/20/2021
Shaking the foundations: delusions in sequence models for interaction and control
The recent phenomenal success of language models has reinvigorated machi...
research
∙
06/18/2021
Active Offline Policy Selection
This paper addresses the problem of policy selection in domains with abu...
research
∙
05/21/2021
On Instrumental Variable Regression for Deep Offline Policy Evaluation
We show that the popular reinforcement learning (RL) strategy of estimat...
research
∙
03/30/2021
Benchmarks for Deep Off-Policy Evaluation
Off-policy evaluation (OPE) holds the promise of being able to leverage ...
research
∙
03/17/2021
Regularized Behavior Value Estimation
Offline reinforcement learning restricts the learning process to rely on...
research
∙
12/29/2020
Myocardial Segmentation of Cardiac MRI Sequences with Temporal Consistency for Coronary Artery Disease Diagnosis
Coronary artery disease (CAD) is the most common cause of death globally...
research
∙
11/24/2020
Synth2Aug: Cross-domain speaker recognition with TTS synthesized speech
In recent years, Text-To-Speech (TTS) has been used as a data augmentati...
research
∙
11/14/2020
Towards transformation-resilient provenance detection of digital media
Advancements in deep generative models have made it possible to synthesi...
research
∙
11/06/2020
Large-scale multilingual audio visual dubbing
We describe a system for large-scale audiovisual translation and dubbing...
research
∙
10/14/2020
Learning Deep Features in Instrumental Variable Regression
Instrumental variable (IV) regression is a standard strategy for learnin...
research
∙
10/06/2020
Sequential Changepoint Detection in Neural Networks with Checkpoints
We introduce a framework for online changepoint detection and simultaneo...
research
∙
09/12/2019
Modular Meta-Learning with Shrinkage
Most gradient-based approaches to meta-learning do not explicitly accoun...
research
∙
12/17/2018
Bayesian Optimization in AlphaGo
During the development of AlphaGo, its many hyper-parameters were tuned ...
research
∙
09/27/2018
Sample Efficient Adaptive Text-to-Speech
We present a meta-learning approach for adaptive text-to-speech (TTS) wi...
research
∙
10/27/2017
Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions
Deep autoregressive models have shown state-of-the-art performance in de...
research
∙
11/11/2016
Learning to Learn without Gradient Descent by Gradient Descent
We learn recurrent neural network optimizers trained on simple synthetic...
research
∙
02/09/2016
Herding as a Learning System with Edge-of-Chaos Dynamics
Herding defines a deterministic dynamical system at the edge of chaos. I...
research
∙
06/30/2015
Scalable Discrete Sampling as a Multi-Armed Bandit Problem
Drawing a sample from a discrete distribution is one of the building com...
research
∙
03/07/2015
Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data
Multivariate categorical data occur in many applications of machine lear...
research
∙
11/06/2014
Sublinear-Time Approximate MCMC Transitions for Probabilistic Programs
Probabilistic programming languages can simplify the development of mach...
research
∙
08/09/2014
Bayesian Structure Learning for Markov Random Fields with a Spike and Slab Prior
In recent years a number of methods have been developed for automaticall...
research
∙
04/19/2013
Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget
Can we make Bayesian posterior MCMC sampling more efficient when faced w...
research
∙
01/17/2013
Herded Gibbs Sampling
The Gibbs sampler is one of the most popular algorithms for inference in...
research
∙
03/15/2012