Adapting the Linearised Laplace Model Evidence for Modern Deep Learning

• URL: http://arxiv.org/abs/2206.08900v1
• Date: Fri, 17 Jun 2022 17:18:31 GMT
• Title: Adapting the Linearised Laplace Model Evidence for Modern Deep Learning
• Authors: Javier Antor\'an, David Janz, James Urquhart Allingham, Erik Daxberger, Riccardo Barbano, Eric Nalisnick, Jos\'e Miguel Hern\'andez-Lobato
• Abstract summary: The linearised Laplace method for estimating model uncertainty has received renewed attention in the deep learning community. We show that these assumptions interact poorly with some now-standard tools of deep learning. We make recommendations for how to better adapt this classic method to the modern setting.
• Score: 3.459382629188014
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The linearised Laplace method for estimating model uncertainty has received renewed attention in the Bayesian deep learning community. The method provides reliable error bars and admits a closed-form expression for the model evidence, allowing for scalable selection of model hyperparameters. In this work, we examine the assumptions behind this method, particularly in conjunction with model selection. We show that these interact poorly with some now-standard tools of deep learning--stochastic approximation methods and normalisation layers--and make recommendations for how to better adapt this classic method to the modern setting. We provide theoretical support for our recommendations and validate them empirically on MLPs, classic CNNs, residual networks with and without normalisation layers, generative autoencoders and transformers.

Related papers

• SMILE: Zero-Shot Sparse Mixture of Low-Rank Experts Construction From Pre-Trained Foundation Models [85.67096251281191]
  We present an innovative approach to model fusion called zero-shot Sparse MIxture of Low-rank Experts (SMILE) construction. SMILE allows for the upscaling of source models into an MoE model without extra data or further training. We conduct extensive experiments across diverse scenarios, such as image classification and text generation tasks, using full fine-tuning and LoRA fine-tuning.
  arXiv  Detail & Related papers  (2024-08-19T17:32:15Z)
• A Diffusion Model Framework for Unsupervised Neural Combinatorial Optimization [7.378582040635655]
  Current deep learning approaches rely on generative models that yield exact sample likelihoods. This work introduces a method that lifts this restriction and opens the possibility to employ highly expressive latent variable models. We experimentally validate our approach in data-free Combinatorial Optimization and demonstrate that our method achieves a new state-of-the-art on a wide range of benchmark problems.
  arXiv  Detail & Related papers  (2024-06-03T17:55:02Z)
• Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods [25.05409184943328]
  We show that well-known model-based methods perform poorly in their ability to adapt to local environmental changes. We identify elements that hurt adaptive behavior and link these to underlying techniques frequently used in deep model-based RL. We provide insights into the challenges of building an adaptive nonlinear model-based method.
  arXiv  Detail & Related papers  (2022-04-25T06:45:16Z)
• Predictive machine learning for prescriptive applications: a coupled training-validating approach [77.34726150561087]
  We propose a new method for training predictive machine learning models for prescriptive applications. This approach is based on tweaking the validation step in the standard training-validating-testing scheme. Several experiments with synthetic data demonstrate promising results in reducing the prescription costs in both deterministic and real models.
  arXiv  Detail & Related papers  (2021-10-22T15:03:20Z)
• On-Policy Model Errors in Reinforcement Learning [9.507323314334572]
  We present a novel method that combines real world data and a learned model in order to get the best of both worlds. The core idea is to exploit the real world data for on-policy predictions and use the learned model only to generalize to different actions. We show that our method can drastically improve existing model-based approaches without introducing additional tuning parameters.
  arXiv  Detail & Related papers  (2021-10-15T10:15:53Z)
• Revisiting Design Choices in Model-Based Offline Reinforcement Learning [39.01805509055988]
  Offline reinforcement learning enables agents to leverage large pre-collected datasets of environment transitions to learn control policies. This paper compares and designs novel protocols to investigate their interaction with other hyper parameters, such as the number of models, or imaginary rollout horizon.
  arXiv  Detail & Related papers  (2021-10-08T13:51:34Z)
• Gone Fishing: Neural Active Learning with Fisher Embeddings [55.08537975896764]
  There is an increasing need for active learning algorithms that are compatible with deep neural networks. This article introduces BAIT, a practical representation of tractable, and high-performing active learning algorithm for neural networks.
  arXiv  Detail & Related papers  (2021-06-17T17:26:31Z)
• Last Layer Marginal Likelihood for Invariance Learning [12.00078928875924]
  We introduce a new lower bound to the marginal likelihood, which allows us to perform inference for a larger class of likelihood functions. We work towards bringing this approach to neural networks by using an architecture with a Gaussian process in the last layer.
  arXiv  Detail & Related papers  (2021-06-14T15:40:51Z)
• Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning [78.83598532168256]
  Marginal-likelihood based model-selection is rarely used in deep learning due to estimation difficulties. Our work shows that marginal likelihoods can improve generalization and be useful when validation data is unavailable.
  arXiv  Detail & Related papers  (2021-04-11T09:50:24Z)
• COMBO: Conservative Offline Model-Based Policy Optimization [120.55713363569845]
  Uncertainty estimation with complex models, such as deep neural networks, can be difficult and unreliable. We develop a new model-based offline RL algorithm, COMBO, that regularizes the value function on out-of-support state-actions. We find that COMBO consistently performs as well or better as compared to prior offline model-free and model-based methods.
  arXiv  Detail & Related papers  (2021-02-16T18:50:32Z)
• Evaluating the Disentanglement of Deep Generative Models through Manifold Topology [66.06153115971732]
  We present a method for quantifying disentanglement that only uses the generative model. We empirically evaluate several state-of-the-art models across multiple datasets.
  arXiv  Detail & Related papers  (2020-06-05T20:54:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.