Search Results for author:
Found 38 papers, 13 papers with code
Token Prediction as Implicit Classification to Identify LLM-Generated Text
This paper introduces a novel approach for identifying the possible large language models (LLMs) involved in text generation.
$\pi2\text{vec}$: Policy Representations with Successor Features
This paper describes $\pi2\text{vec}$, a method for representing behaviors of black box policies as feature vectors.
GPT-Sentinel: Distinguishing Human and ChatGPT Generated Content
This paper presents a novel approach for detecting ChatGPT-generated vs. human-written text using language models.
Discovering Attention-Based Genetic Algorithms via Meta-Black-Box Optimization
Genetic algorithms constitute a family of black-box optimization algorithms, which take inspiration from the principles of biological evolution.
Discovering Evolution Strategies via Meta-Black-Box Optimization
Optimizing functions without access to gradients is the remit of black-box methods such as evolution strategies.
NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research
A shared goal of several machine learning communities like continual learning, meta-learning and transfer learning, is to design algorithms and models that efficiently and robustly adapt to unseen tasks.
Multi-step Planning for Automated Hyperparameter Optimization with OptFormer
As machine learning permeates more industries and models become more expensive and time consuming to train, the need for efficient automated hyperparameter optimization (HPO) has never been more pressing.
Towards Learning Universal Hyperparameter Optimizers with Transformers
Meta-learning hyperparameter optimization (HPO) algorithms from prior experiments is a promising approach to improve optimization efficiency over objective functions from a similar distribution.
A Generalist Agent
Inspired by progress in large-scale language modeling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs.
Ranked #1 on
Skill Generalization
on RGB-Stacking
Importance Weighting Approach in Kernel Bayes' Rule
We study a nonparametric approach to Bayesian computation via feature means, where the expectation of prior features is updated to yield expected kernel posterior features, based on regression from learned neural net or kernel features of the observations.
Shaking the foundations: delusions in sequence models for interaction and control
The recent phenomenal success of language models has reinvigorated machine learning research, and large sequence models such as transformers are being applied to a variety of domains.
Introducing Symmetries to Black Box Meta Reinforcement Learning
We show that a recent successful meta RL approach that meta-learns an objective for backpropagation-based learning exhibits certain symmetries (specifically the reuse of the learning rule, and invariance to input and output permutations) that are not present in typical black-box meta RL systems.
Active Offline Policy Selection
We use multiple benchmarks, including real-world robotics, with a large number of candidate policies to show that the proposed approach improves upon state-of-the-art OPE estimates and pure online policy evaluation.
On Instrumental Variable Regression for Deep Offline Policy Evaluation
By applying different IV techniques to OPE, we are not only able to recover previously proposed OPE methods such as model-based techniques but also to obtain competitive new techniques.
Benchmarks for Deep Off-Policy Evaluation
Off-policy evaluation (OPE) holds the promise of being able to leverage large, offline datasets for both evaluating and selecting complex policies for decision making.
Regularized Behavior Value Estimation
Due to bootstrapping, these errors get amplified during training and can lead to divergence, thereby crippling learning.
Addressing Extrapolation Error in Deep Offline Reinforcement Learning
These errors can be compounded by bootstrapping when the function approximator overestimates, leading the value function to *grow unbounded*, thereby crippling learning.
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, and its diagnosis is usually based on manual myocardial segmentation of Magnetic Resonance Imaging (MRI) sequences.
Synth2Aug: Cross-domain speaker recognition with TTS synthesized speech
In recent years, Text-To-Speech (TTS) has been used as a data augmentation technique for speech recognition to help complement inadequacies in the training data.
Towards transformation-resilient provenance detection of digital media
In this paper, we introduce ReSWAT (Resilient Signal Watermarking via Adversarial Training), a framework for learning transformation-resilient watermark detectors that are able to detect a watermark even after a signal has been through several post-processing transformations.
Large-scale multilingual audio visual dubbing
The visual content is translated by synthesizing lip movements for the speaker to match the translated audio, creating a seamless audiovisual experience in the target language.
Learning Deep Features in Instrumental Variable Regression
We propose a novel method, deep feature instrumental variable regression (DFIV), to address the case where relations between instruments, treatments, and outcomes may be nonlinear.
Sequential Changepoint Detection in Neural Networks with Checkpoints
We introduce a framework for online changepoint detection and simultaneous model learning which is applicable to highly parametrized models, such as deep neural networks.
Provenance detection through learning transformation-resilient watermarking
In this paper, we introduce ReSWAT (Resilient Signal Watermarking via Adversarial Training), a framework for learning transformation-resilient watermark detectors that are able to detect a watermark even after a signal has been through several post-processing transformations.
Modular Meta-Learning with Shrinkage
Many real-world problems, including multi-speaker text-to-speech synthesis, can greatly benefit from the ability to meta-learn large models with only a few task-specific components.
Bayesian Optimization in AlphaGo
During the development of AlphaGo, its many hyper-parameters were tuned with Bayesian optimization multiple times.
Sample Efficient Adaptive Text-to-Speech
Instead, the aim is to produce a network that requires few data at deployment time to rapidly adapt to new speakers.
Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions
Deep autoregressive models have shown state-of-the-art performance in density estimation for natural images on large-scale datasets such as ImageNet.
Learning to Learn without Gradient Descent by Gradient Descent
We learn recurrent neural network optimizers trained on simple synthetic functions by gradient descent.
Herding as a Learning System with Edge-of-Chaos Dynamics
Herding defines a deterministic dynamical system at the edge of chaos.
Scalable Discrete Sampling as a Multi-Armed Bandit Problem
Drawing a sample from a discrete distribution is one of the building components for Monte Carlo methods.
Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data
Building on these ideas we propose a Bayesian model for the unsupervised task of distribution estimation of multivariate categorical data.
Sublinear-Time Approximate MCMC Transitions for Probabilistic Programs
Probabilistic programming languages can simplify the development of machine learning techniques, but only if inference is sufficiently scalable.
Bayesian Structure Learning for Markov Random Fields with a Spike and Slab Prior
In recent years a number of methods have been developed for automatically learning the (sparse) connectivity structure of Markov Random Fields.
Variational Gaussian Process State-Space Models
State-space models have been successfully used for more than fifty years in different areas of science and engineering.
Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget
Can we make Bayesian posterior MCMC sampling more efficient when faced with very large datasets?
Super-Samples from Kernel Herding
We extend the herding algorithm to continuous spaces by using the kernel trick.
On Herding and the Perceptron Cycling Theorem
The paper develops a connection between traditional perceptron algorithms and recently introduced herding algorithms.
