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Found 13 papers, 11 papers with code
Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators
Even simple, known confounders such as preference for longer outputs remain in existing automated evaluation metrics.
Identifying the Risks of LM Agents with an LM-Emulated Sandbox
Alongside the emulator, we develop an LM-based automatic safety evaluator that examines agent failures and quantifies associated risks.
AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback
As a demonstration of the research possible in AlpacaFarm, we find that methods that use a reward model can substantially improve over supervised fine-tuning and that our reference PPO implementation leads to a +10% improvement in win-rate against Davinci003.
Evaluating Self-Supervised Learning via Risk Decomposition
Our decomposition consists of four error components: approximation, representation usability, probe generalization, and encoder generalization.
Improving Self-Supervised Learning by Characterizing Idealized Representations
For non-contrastive learning, we use our framework to derive a simple and novel objective.
Is a Caption Worth a Thousand Images? A Controlled Study for Representation Learning
The development of CLIP [Radford et al., 2021] has sparked a debate on whether language supervision can result in vision models with more transferable representations than traditional image-only methods.
Learning Instance-Specific Augmentations by Capturing Local Invariances
We introduce InstaAug, a method for automatically learning input-specific augmentations from data.
Optimal Representations for Covariate Shift
Machine learning systems often experience a distribution shift between training and testing.
Ranked #38 on
Image Classification
on ObjectNet
(using extra training data)
Lossy Compression for Lossless Prediction
Most data is automatically collected and only ever "seen" by algorithms.
Ranked #1 on
Image Compression
on Oxford-IIIT Pet Dataset
(using extra training data)
Learning Optimal Representations with the Decodable Information Bottleneck
We address the question of characterizing and finding optimal representations for supervised learning.
Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes
Stationary stochastic processes (SPs) are a key component of many probabilistic models, such as those for off-the-grid spatio-temporal data.
Location Attention for Extrapolation to Longer Sequences
We hypothesize that models with a separate content- and location-based attention are more likely to extrapolate than those with common attention mechanisms.
Convolutional Conditional Neural Processes
We introduce the Convolutional Conditional Neural Process (ConvCNP), a new member of the Neural Process family that models translation equivariance in the data.
