MO2 learns useful behaviours from past experience for new tasks.
problem Discovering useful behaviours from past experience and transferring them to new tasks.
method Model-Based Offline Options (MO2) framework supporting sample-efficient bottleneck option discovery over continuous state-action spaces.
result MO2 outperforms recent option learning methods on complex long-horizon continuous control tasks.
A deep RL method using a bottleneck state improves data efficiency.
problem Lack of data-efficiency in deep reinforcement learning.
method Model-based approach combining learned transition model and rollout simulations.
result The Bottleneck Simulator achieves excellent performance on natural language tasks.
InfoBot learns decision states from prior experience to guide exploration.
problem Discovering effective policies in sparse reward environments.
method InfoBot uses an information bottleneck to learn decision states from prior experience.
result InfoBot effectively identifies decision states, even in partially observed settings.
Q*BERT learns to navigate text-based games by building a knowledge graph.
problem Text-based games have bottlenecks that standard RL agents struggle to overcome.
method Q*BERT learns a knowledge graph and uses intrinsic motivation to detect and overcome bottlenecks.
result Q*BERT outperforms state-of-the-art agents in text games, including Zork.
The paper develops a method to create robust control policies for robots using information bottlenecks.
problem Robotic control policies are sensitive to task-irrelevant state and sensor changes.
method Derives a policy gradient algorithm that creates an information bottleneck between states and task-relevant representations.
result Task-driven policies are more robust to sensor noise and environmental changes.
Graph neural networks struggle to propagate long-range information, causing over-squashing.
problem Graph neural networks struggle to propagate long-range information.
method Identified over-squashing as the bottleneck in GNNs, demonstrated on various models.
result Breaking the bottleneck improves GNNs' performance on long-range problems.
New method uses information bottleneck to estimate causal effects from missing data.
problem Estimating causal effects from high-dimensional observational data with missing covariates.
method Uses information bottleneck to compress covariates and transfer relevant information.
result Achieves state-of-the-art performance in causal inference benchmarks and real applications.
Information bottleneck (IB) is a technique for extracting information in one random variable X that is relevant for predicting another random variable Y. IB works by encoding X in a compressed "bottleneck" random variable M from which Y can be accurately decoded. However, finding the optimal bottleneck variab…
Proposes a multi-task learning model using variational information bottleneck.
problem Balancing performance and robustness across different tasks in multi-task learning.
method Variational Information Bottleneck (VIB) architecture for multi-task learning.
result The proposed model achieves competitive prediction accuracy under adversarial attacks.
With pressure to increase graduation rates and reduce time to degree in higher education, it is important to identify at-risk students early. Automated early warning systems are therefore highly desirable. In this paper, we use unsupervised clustering techniques to predict the graduation status of declared majors in fi…
Concept bottleneck models enable concept manipulation for model interpretation.
problem Training models to directly predict labels without intermediate concepts.
method Train models to predict intermediate concepts at training time, then use these concepts to predict labels.
result Concept bottleneck models achieve competitive accuracy with standard models while enabling concept manipulation.
Paper proposes a method to estimate individual treatment effects reliably from observational data.
problem Estimating individual treatment effects from observational data is challenging and important.
method The approach uses the Information Bottleneck principle to find more reliable representations for ITE estimation.
result The proposed model achieves state-of-the-art results and provides more reliable prediction performances with uncertainty information.
VIBI interprets black-box systems by selecting key features that are both brief and comprehensive.
problem Lack of concise and comprehensive explanations for black-box decision systems.
method VIBI uses the information bottleneck principle to select key features that are maximally compressed and informative.
result VIBI provides more concise and comprehensive explanations compared to existing methods.
Extends information bottleneck to multi-view unsupervised learning.
problem Identifying superfluous information in unlabeled multi-view data.
method Multi-view information bottleneck model, leveraging data augmentation.
result State-of-the-art results on Sketchy and MIR-Flickr datasets.
Graph Information Bottleneck (GIB) optimizes graph representations for robustness against adversarial attacks.
problem Challenges in learning graph representations due to structure and feature information.
method GIB is an information-theoretic principle that balances expressiveness and robustness by maximizing mutual information between representation and target, while constraining mutual information with input data.
result GIB-based models are more robust to adversarial attacks, achieving up to 31% improvement.
Proposes a new method to selectively access privileged information in reinforcement learning.
problem Selective compression of privileged information in reinforcement learning.
method Formulates a variational bandwidth bottleneck to decide stochastically whether to access privileged information.
result Improves generalization and reduces access to costly information in reinforcement learning experiments.
Finet uses FBN for efficient, lightweight neural networks.
problem Building efficient neural networks with limited computational resources.
method Introduces Fine-grained Batch Normalization (FBN) and a novel light-weight network (Finet) that combines FBN with standard convolution.
result Finet achieves state-of-the-art performance on ImageNet classification with reduced computational complexity.
New research shows non-bottlenecked autoencoders can outperform bottlenecked ones for anomaly detection.
problem The necessity of a bottleneck in autoencoders for anomaly detection.
method Investigated two ways to remove bottlenecks: overparameterising the latent layer and introducing skip connections. Carried out extensive experiments on various AE types and datasets.
result Non-bottlenecked autoencoders can outperform bottlenecked ones, improving anomaly detection performance.
The paper interprets VQ-VAE loss as a form of information bottleneck.
problem Understanding the VQ-VAE loss function.
method Interpreted VQ-VAE loss as variational deterministic information bottleneck (VDIB) and variational information bottleneck (VIB).
result VQ-VAE loss can be derived from VDIB and approximated by VIB.
SCBMs model causal effects using low-dimensional bottlenecks.
problem Causal effect estimation in high-dimensional systems.
method Structural causal models with low-dimensional summary statistics.
result SCBMs provide a flexible framework for task-specific dimension reduction.
Wide neural networks with narrow bottlenecks behave like deep Gaussian processes.
problem Understanding the behavior of neural networks with narrow layers in the wide limit.
method Analyzing the wide limit of BNNs with narrow bottlenecks, showing they behave like a composition of GPs.
result Wide neural networks with narrow bottlenecks form a composition of GPs, termed a bottleneck NNGP.
Softmax is an output activation function for modeling categorical probability distributions in many applications of deep learning. However, a recent study revealed that softmax can be a bottleneck of representational capacity of neural networks in language modeling (the softmax bottleneck). In this paper, we propose an…
AIB method improves robustness against adversarial perturbations.
problem Optimizing the IB principle for better robustness and understanding compression effects.
method Proposes adversarial information bottleneck (AIB) method to optimize IB principle without explicit distribution assumptions.
result Demonstrates effectiveness in learning more invariant representations and mitigating adversarial perturbations.
Improves deep network generalization for image sequence reconstruction.
problem Improving generalization of deep networks for inverse image reconstruction.
method Proposes a network optimized by a variational approximation of the information bottleneck principle with stochastic latent space.
result Demonstrates improved generalization ability of inverse reconstruction networks through stochasticity and information bottleneck.
AC-State discovers minimal latent state for control.
problem Discover minimal latent state from sensory information.
method Multi-step inverse model with information bottleneck.
result Guaranteed discovery of control-endogenous latent states.
Study shows bottlenecks improve image segmentation quality.
problem Robust object discovery in real-world images remains challenging.
method Empirical investigation of reconstruction bottlenecks in GENESIS model.
result Reconstruction bottlenecks determine reconstruction and segmentation quality.
Deep ResNets favor low bottleneck rank with proper hyperparameters.
problem Understanding the inductive bias of deep neural networks.
method Computed minimum-norm weights of a deep linear ResNet.
result Deep nonlinear ResNets have an inductive bias towards minimizing bottleneck rank.
Derives a new variational approach to information bottleneck.
problem Information theoretic inference and predictive modeling.
method Variational lower bound of predictive information bottleneck.
result Generalizes modern inference procedures and suggests new ones.
Paper introduces a technique to simplify RNN policies for better understanding and analysis.
problem Difficulty in explaining and analyzing RNN policies due to continuous-valued memory vectors and observation features.
method Quantized Bottleneck Insertion technique to learn finite representations of RNN vectors and features.
result Finite representations of RNN policies can be as small as 3 discrete memory states and 10 observations, improving interpretability.
Paper revisits Deep Variational Information Bottleneck and proposes a new optimization approach.
problem Limitations of Deep Variational Information Bottleneck in optimizing mutual information.
method Proposes a new optimization approach by circumventing the limitation of requiring both Markov chains during optimisation.
result Shows how to optimise a lower bound for mutual information, circumventing the limitation of requiring both Markov chains.
GWIB improves counterfactual regression by balancing latent distributions and reducing selection bias.
problem Selection bias between control and treatment groups negatively impacts counterfactual regression performance.
method GWIB uses Gromov-Wasserstein information bottleneck to maximize mutual information between covariates and outcomes while penalizing kernelized mutual information between latent representations and covariates.
result GWIB consistently outperforms state-of-the-art CFR methods in ITE estimation tasks.
Improves unsupervised domain adaptation by enforcing feature extractor to focus on task-relevant information.
problem Leveraging label information from source domain for accurate target domain models without labels.
method Variational Information Bottleneck (VBDA) method that explicitly enforces feature extractor to ignore irrelevant task factors.
result Significantly outperforms state-of-the-art methods across three domain adaptation benchmark datasets.
HSIC bottleneck trains deep networks without backpropagation.
problem Training deep neural networks with exploding and vanishing gradients.
method HSIC bottleneck, alternative to cross-entropy loss and backpropagation.
result HSIC bottleneck achieves comparable performance to backpropagation.
FAVAE learns disentangled representations from sequential data.
problem Learning disentangled and interpretable representations from sequential data.
method FAVAE uses the information bottleneck principle without supervision.
result FAVAE can disentangle multiple dynamic factors.
New method uses reinforcement learning to accurately estimate available network bandwidth.
problem Accurate and fast estimation of available bandwidth in networks with varying cross-traffic.
method Employed reinforcement learning, specifically the ε-greedy algorithm in a multi-armed bandit approach. result Proposed method identifies available bandwidth with high precision and converges under various challenging conditions.
CB-APM uses analyst consensus as a bottleneck to interpret stock returns.
problem Tackles the challenge of understanding and predicting stock returns using professional beliefs.
method Embeds analyst consensus as a structural bottleneck, treating it as a sufficient statistic for market information.
result CB-APM portfolios exhibit strong monotonic return gradients and robust across different economic conditions.
The paper identifies overfitting as the main bottleneck in efficient deep reinforcement learning.
problem Improving sample efficiency in deep reinforcement learning.
method Empirical analysis on DMC tasks to identify overfitting as the main issue and developing a hill-climbing method targeting validation TD error.
result Overfitting is the primary bottleneck in sample-efficient deep RL, and regularization techniques can control this.
Wide neural networks become linear, but adding bottlenecks makes them bilinear or multilinear.
problem Understanding the transition of neural networks from linearity to higher-order functions.
method Analyzing the behavior of randomly initialized wide neural networks with and without bottleneck layers.
result Bottleneck layers transform the network's function from linear to bilinear or multilinear.
SySCD improves SCD scalability and speeds up training.
problem Scalability issues in parallel SCD algorithms.
method Developed a system-aware parallel SCD algorithm (SySCD) to avoid bottlenecks.
result Offers up to x42 speedup compared to state-of-the-art GLM solvers.
New method quantifies redundant information using information bottleneck.
problem Quantifying redundant information among multiple sources.
method Formulated as an information bottleneck problem, termed redundancy bottleneck.
result Extracts information that best predicts the target without revealing source identity.
ECBMs unify concept-based interpretations in deep learning models.
problem Suboptimal final accuracy and lack of concept interaction and conditional dependencies.
method ECBMs use a set of neural networks to define joint energy, enabling concept correction and conditional dependency quantification.
result ECBMs achieve higher accuracy and richer concept interpretations compared to state-of-the-art methods.
New learning rules from information bottleneck improve deep learning without precise labels.
problem Training deep neural networks with backpropagation is biologically implausible.
method Kernelized information bottleneck principle with 3-factor Hebbian structure.
result The new learning rules perform nearly as well as backpropagation on image classification tasks.
VUSFA improves transfer learning for target-driven navigation in AI2THOR.
problem Improving transfer reinforcement learning for complex visual navigation tasks.
method Introducing SFDP and Variational Information Bottlenecks to A3C agent.
result VUSFA achieves state-of-the-art performance and generalizability.
The Mahler volume of a centrally symmetric convex body K is defined as M(K)= (Vol K)(Vol K^dual). Mahler conjectured that this volume is minimized when K is a cube. We introduce the bottleneck conjecture, which stipulates that a certain convex body K^diamond subset K X K^dual has least volume when K is an ellipsoid. If…
The Information bottleneck method is an unsupervised non-parametric data organization technique. Given a joint distribution P(A,B), this method constructs a new variable T that extracts partitions, or clusters, over the values of A that are informative about B. The information bottleneck has already been applied to doc…
Quantum machine learning classification depends on mutual informations between state and parameter spaces.
problem Generalization in quantum machine learning models.
method Link between quantum machine learning and quantum hypothesis testing, using mutual informations.
result Quantum classifier accuracy and generalization depend on mutual informations between state and parameter spaces.
PLUMAGE improves large model training efficiency and stability.
problem Accelerator memory and networking constraints during large model training.
method Probabilistic Low rank Unbiased Minimum Variance Gradient Estimator (PLUMAGE) that resolves bias and variance issues.
result PLUMAGE reduces training loss by 28% on average across the GLUE benchmark.
VIB improves classification and uncertainty quantification.
problem Improving classification calibration and uncertainty quantification.
method Presented a simple case study of VIB.
result VIB improves classification calibration and uncertainty quantification naturally.