Search Results for author:
Found 22 papers, 6 papers with code
Reasoning Elicitation in Language Models via Counterfactual Feedback
Second, we propose several fine-tuning approaches that aim to elicit better reasoning mechanisms, in the sense of the proposed metrics.
Beyond Flatland: A Geometric Take on Matching Methods for Treatment Effect Estimation
First, we learn a low-dimensional, latent Riemannian manifold that accounts for uncertainty and geometry of the original input data.
Does Reasoning Emerge? Examining the Probabilities of Causation in Large Language Models
Recent advances in AI have been significantly driven by the capabilities of large language models (LLMs) to solve complex problems in ways that resemble human thinking.
Beyond Words: A Mathematical Framework for Interpreting Large Language Models
Large language models (LLMs) are powerful AI tools that can generate and comprehend natural language text and other complex information.
TRIALSCOPE: A Unifying Causal Framework for Scaling Real-World Evidence Generation with Biomedical Language Models
The rapid digitization of real-world data offers an unprecedented opportunity for optimizing healthcare delivery and accelerating biomedical discovery.
Dynamic Causal Bayesian Optimization
This paper studies the problem of performing a sequence of optimal interventions in a causal dynamical system where both the target variable of interest and the inputs evolve over time.
BayesIMP: Uncertainty Quantification for Causal Data Fusion
While causal models are becoming one of the mainstays of machine learning, the problem of uncertainty quantification in causal inference remains challenging.
Multi-task Causal Learning with Gaussian Processes
This paper studies the problem of learning the correlation structure of a set of intervention functions defined on the directed acyclic graph (DAG) of a causal model.
Structure Mapping for Transferability of Causal Models
Human beings learn causal models and constantly use them to transfer knowledge between similar environments.
Learning Inconsistent Preferences with Gaussian Processes
We revisit widely used preferential Gaussian processes by Chu et al.(2005) and challenge their modelling assumption that imposes rankability of data items via latent utility function values.
Causal Bayesian Optimization
This paper studies the problem of globally optimizing a variable of interest that is part of a causal model in which a sequence of interventions can be performed.
BOFFIN TTS: Few-Shot Speaker Adaptation by Bayesian Optimization
We present BOFFIN TTS (Bayesian Optimization For FIne-tuning Neural Text To Speech), a novel approach for few-shot speaker adaptation.
Bandit optimisation of functions in the Matérn kernel RKHS
We consider the problem of optimising functions in the reproducing kernel Hilbert space (RKHS) of a Mat\'ern kernel with smoothness parameter $\nu$ over the domain $[0, 1]^d$ under noisy bandit feedback.
Deep Gaussian Processes for Multi-fidelity Modeling
Multi-fidelity methods are prominently used when cheaply-obtained, but possibly biased and noisy, observations must be effectively combined with limited or expensive true data in order to construct reliable models.
Bayesian Optimization with Tree-structured Dependencies
The benefit of leveraging this structure is twofold: we explore the search space more efficiently and posterior inference scales more favorably with the number of observations than Gaussian Process-based approaches published in the literature.
Preferential Bayesian Optmization
We present a new framework for this scenario that we call Preferential Bayesian Optimization (PBO) and that allows to find the optimum of a latent function that can only be queried through pairwise comparisons, so-called duels.
Correcting boundary over-exploration deficiencies in Bayesian optimization with virtual derivative sign observations
Bayesian optimization (BO) is a global optimization strategy designed to find the minimum of an expensive black-box function, typically defined on a compact subset of $\mathcal{R}^d$, by using a Gaussian process (GP) as a surrogate model for the objective.
Variational Auto-encoded Deep Gaussian Processes
We develop a scalable deep non-parametric generative model by augmenting deep Gaussian processes with a recognition model.
GLASSES: Relieving The Myopia Of Bayesian Optimisation
We present GLASSES: Global optimisation with Look-Ahead through Stochastic Simulation and Expected-loss Search.
Batch Bayesian Optimization via Local Penalization
The approach assumes that the function of interest, $f$, is a Lipschitz continuous function.
Bayesian Optimization for Synthetic Gene Design
We address the problem of synthetic gene design using Bayesian optimization.
Reproducing kernel Hilbert space based estimation of systems of ordinary differential equations
Non-linear systems of differential equations have attracted the interest in fields like system biology, ecology or biochemistry, due to their flexibility and their ability to describe dynamical systems.
