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Found 41 papers, 12 papers with code
Learning Causally Invariant Reward Functions from Diverse Demonstrations
Inverse reinforcement learning methods aim to retrieve the reward function of a Markov decision process based on a dataset of expert demonstrations.
Measuring Orthogonality in Representations of Generative Models
Our findings suggest that representation quality is closer related to the orthogonality of independent generative processes rather than their disentanglement, offering a new direction for evaluating and improving unsupervised learning models.
Breeding Programs Optimization with Reinforcement Learning
Crop breeding is crucial in improving agricultural productivity while potentially decreasing land usage, greenhouse gas emissions, and water consumption.
Point-In-Context: Understanding Point Cloud via In-Context Learning
With the emergence of large-scale models trained on diverse datasets, in-context learning has emerged as a promising paradigm for multitasking, notably in natural language processing and image processing.
Hypernetwork-Driven Model Fusion for Federated Domain Generalization
Our study contributes significantly to the under-explored field of Federated Domain Generalization (FDG), setting a new benchmark for performance in this area.
Invariant Anomaly Detection under Distribution Shifts: A Causal Perspective
In this work, by leveraging tools from causal inference we attempt to increase the resilience of anomaly detection models to different kinds of distribution shifts.
Regularizing Adversarial Imitation Learning Using Causal Invariance
Imitation learning methods are used to infer a policy in a Markov decision process from a dataset of expert demonstrations by minimizing a divergence measure between the empirical state occupancy measures of the expert and the policy.
Improving Explainability of Disentangled Representations using Multipath-Attribution Mappings
We propose a framework that utilizes interpretable disentangled representations for downstream-task prediction.
Explore In-Context Learning for 3D Point Cloud Understanding
With the rise of large-scale models trained on broad data, in-context learning has become a new learning paradigm that has demonstrated significant potential in natural language processing and computer vision tasks.
How to Enable Uncertainty Estimation in Proximal Policy Optimization
To overcome this, we formulate a Pareto optimization problem in which we simultaneously optimize for reward and OOD detection performance.
Deep Reinforcement Learning
Out of Distribution (OOD) Detection
+1
Learning to Drop Out: An Adversarial Approach to Training Sequence VAEs
In principle, applying variational autoencoders (VAEs) to sequential data offers a method for controlled sequence generation, manipulation, and structured representation learning.
BARReL: Bottleneck Attention for Adversarial Robustness in Vision-Based Reinforcement Learning
Robustness to adversarial perturbations has been explored in many areas of computer vision.
Gated Domain Units for Multi-source Domain Generalization
To address this problem, we postulate that real-world distributions are composed of latent Invariant Elementary Distributions (I. E. D) across different domains.
Learning Invariant Reward Functions through Trajectory Interventions
Inverse reinforcement learning methods aim to retrieve the reward function of a Markov decision process based on a dataset of expert demonstrations.
Spatially Dependent U-Nets: Highly Accurate Architectures for Medical Imaging Segmentation
In clinical practice, regions of interest in medical imaging often need to be identified through a process of precise image segmentation.
Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling
We show that augmenting the decoder of a hierarchical VAE by spatial dependency layers considerably improves density estimation over baseline convolutional architectures and the state-of-the-art among the models within the same class.
On maximum-likelihood estimation in the all-or-nothing regime
The result follows from two main technical points: (i) the connection established between the MLE and the MMSE, using the first and second-moment methods in the constrained signal space, (ii) a recovery regime for the MMSE stricter than the simple error vanishing characterization given in the standard AoN, that is here proved as a general result.
Statistical and computational thresholds for the planted $k$-densest sub-hypergraph problem
In this work, we consider the problem of recovery a planted $k$-densest sub-hypergraph on $d$-uniform hypergraphs.
Neural collaborative filtering for unsupervised mitral valve segmentation in echocardiography
The segmentation of the mitral valve annulus and leaflets specifies a crucial first step to establish a machine learning pipeline that can support physicians in performing multiple tasks, e. g.\ diagnosis of mitral valve diseases, surgical planning, and intraoperative procedures.
Continuous Submodular Function Maximization
We start by a thorough characterization of the class of continuous submodular functions, and show that continuous submodularity is equivalent to a weak version of the diminishing returns (DR) property.
From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models
Submodular functions have been studied extensively in machine learning and data mining.
Variational Federated Multi-Task Learning
Despite federated multi-task learning being shown to be an effective paradigm for real-world datasets, it has been applied only on convex models.
Disentangled State Space Representations
Sequential data often originates from diverse domains across which statistical regularities and domain specifics exist.
DISENTANGLED STATE SPACE MODELS: UNSUPERVISED LEARNING OF DYNAMICS ACROSS HETEROGENEOUS ENVIRONMENTS
To learn robust cross-environment descriptions of sequences we introduce disentangled state space models (DSSM).
Learning Counterfactual Representations for Estimating Individual Dose-Response Curves
Estimating what would be an individual's potential response to varying levels of exposure to a treatment is of high practical relevance for several important fields, such as healthcare, economics and public policy.
Optimal DR-Submodular Maximization and Applications to Provable Mean Field Inference
Mean field inference in probabilistic models is generally a highly nonconvex problem.
Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEs
Parameter identification and comparison of dynamical systems is a challenging task in many fields.
Efficient and Flexible Inference for Stochastic Systems
Many real world dynamical systems are described by stochastic differential equations.
Continuous DR-submodular Maximization: Structure and Algorithms
Concretely, we first devise a "two-phase" algorithm with $1/4$ approximation guarantee.
Scalable Variational Inference for Dynamical Systems
That is why, despite the high computational cost, numerical integration is still the gold standard in many applications.
Guarantees for Greedy Maximization of Non-submodular Functions with Applications
Our guarantees are characterized by a combination of the (generalized) curvature $\alpha$ and the submodularity ratio $\gamma$.
Scalable Adaptive Stochastic Optimization Using Random Projections
We show that the regret of Ada-LR is close to the regret of full-matrix AdaGrad which can have an up-to exponentially smaller dependence on the dimension than the diagonal variant.
Mean-Field Variational Inference for Gradient Matching with Gaussian Processes
The essence of gradient matching is to model the prior over state variables as a Gaussian process which implies that the joint distribution given the ODE's and GP kernels is also Gaussian distributed.
Model Selection for Gaussian Process Regression by Approximation Set Coding
Gaussian processes are powerful, yet analytically tractable models for supervised learning.
Guaranteed Non-convex Optimization: Submodular Maximization over Continuous Domains
Submodular continuous functions are a category of (generally) non-convex/non-concave functions with a wide spectrum of applications.
Multi-Organ Cancer Classification and Survival Analysis
Accurate and robust cell nuclei classification is the cornerstone for a wider range of tasks in digital and Computational Pathology.
TI-POOLING: transformation-invariant pooling for feature learning in Convolutional Neural Networks
This more efficient use of training data results in better performance on popular benchmark datasets with smaller number of parameters when comparing to standard convolutional neural networks with dataset augmentation and to other baselines.
Computational Pathology: Challenges and Promises for Tissue Analysis
The histological assessment of human tissue has emerged as the key challenge for detection and treatment of cancer.
Transformation-Invariant Convolutional Jungles
Many Computer Vision problems arise from information processing of data sources with nuisance variances like scale, orientation, contrast, perspective foreshortening or - in medical imaging - staining and local warping.
Fast and Robust Least Squares Estimation in Corrupted Linear Models
Subsampling methods have been recently proposed to speed up least squares estimation in large scale settings.
Correlated random features for fast semi-supervised learning
Random views are justified by recent theoretical and empirical work showing that regression with random features closely approximates kernel regression, implying that random views can be expected to contain accurate estimators.
