29 citations · 69 across the 11 of their papers we have counts for
12 papers
Trained on Tokens, Calibrated on Concepts: The Emergence of Semantic Calibration in LLMs
Preetum Nakkiran, Arwen Bradley, Adam Goliński +3
Large Language Models (LLMs) often lack meaningful confidence estimates for their outputs. While base LLMs are known to exhibit next-token calibration, it remains unclear whether t…
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for LLMs
Yinong Oliver Wang, Nivedha Sivakumar, Falaah Arif Khan +6
The recent rapid adoption of large language models (LLMs) highlights the critical need for benchmarking their fairness. Conventional fairness metrics, which focus on discrete accur…
Revisiting Uncertainty Quantification Evaluation in Language Models: Spurious Interactions with Response Length Bias Results
Andrea Santilli, Adam Golinski, Michael Kirchhof +5
Uncertainty Quantification (UQ) in Language Models (LMs) is key to improving their safety and reliability. Evaluations often use metrics like AUROC to assess how well UQ methods (e…
Considerations for Distribution Shift Robustness of Diagnostic Models in Healthcare
Arno Blaas, Adam Goliński, Andrew Miller +3
We consider robustness to distribution shifts in the context of diagnostic models in healthcare, where the prediction target , e.g., the presence of a disease, is causally upstr…
The Role of Entropy and Reconstruction in Multi-View Self-Supervised Learning
Borja Rodríguez-Gálvez, Arno Blaas, Pau Rodríguez +5
The mechanisms behind the success of multi-view self-supervised learning (MVSSL) are not yet fully understood. Contrastive MVSSL methods have been studied through the lens of InfoN…
LIDL: Local Intrinsic Dimension Estimation Using Approximate Likelihood
Piotr Tempczyk, Rafał Michaluk, Łukasz Garncarek +3
Most of the existing methods for estimating the local intrinsic dimension of a data distribution do not scale well to high-dimensional data. Many of them rely on a non-parametric n…
COIN++: Neural Compression Across Modalities
Emilien Dupont, Hrushikesh Loya, Milad Alizadeh +3
Neural compression algorithms are typically based on autoencoders that require specialized encoder and decoder architectures for different data modalities. In this paper, we propos…
Expectation Programming: Adapting Probabilistic Programming Systems to Estimate Expectations Efficiently
Tim Reichelt, Adam Goliński, Luke Ong +1
We show that the standard computational pipeline of probabilistic programming systems (PPSs) can be inefficient for estimating expectations and introduce the concept of expectation…
COIN: COmpression with Implicit Neural representations
Emilien Dupont, Adam Goliński, Milad Alizadeh +2
We propose a new simple approach for image compression: instead of storing the RGB values for each pixel of an image, we store the weights of a neural network overfitted to the ima…
Feedback Recurrent Autoencoder for Video Compression
Adam Golinski, Reza Pourreza, Yang Yang +2
Recent advances in deep generative modeling have enabled efficient modeling of high dimensional data distributions and opened up a new horizon for solving data compression problems…
Amortized Monte Carlo Integration
Adam Goliński, Frank Wood, Tom Rainforth
Current approaches to amortizing Bayesian inference focus solely on approximating the posterior distribution. Typically, this approximation is, in turn, used to calculate expectati…
Faithful Inversion of Generative Models for Effective Amortized Inference
Stefan Webb, Adam Golinski, Robert Zinkov +4
Inference amortization methods share information across multiple posterior-inference problems, allowing each to be carried out more efficiently. Generally, they require the inversi…