UAED discovers adaptive environments for robust learning.
problem Avoiding spurious correlations in data.
method Unified framework that learns a distribution over data transformations.
result Improves worst-case accuracy on standard benchmarks.
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
168,742 papers · 148 categories
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48 results for “distributed environments”
GALA framework learns invariant graph representations via environment augmentation with minimal assumptions.
problem Learning invariant graph representations from different environments without additional assumptions.
method Developed GALA framework with minimal assumptions of variation sufficiency and consistency. Uses an assistant model to differentiate graph environment changes.
result Extracting maximally invariant subgraphs to proxy predictions identifies underlying invariant subgraphs for successful out-of-distribution generalization.
A neurally plausible model learns successor representations in partially observable environmentsstat.ML
Animals need to devise strategies to maximize returns while interacting with their environment based on incoming noisy sensory observations. Task-relevant states, such as the agent's location within an environment or the presence of a predator, are often not directly observable but must be inferred using available sens…
Locally private reinforcement learning protects individual environments from reverse engineering.
problem Protecting private information in distributed reinforcement learning environments.
method Locally differentially private algorithms that protect local agents' models from adversarial reverse engineering.
result Demonstrated that the proposed algorithm performs well under local differential privacy (LDP).
SAMPLR optimizes for ground truth in aleatoric parameters to avoid curriculum-induced covariate shift.
problem Curriculum learning shifts training distribution, leading to suboptimal policies in aleatoric settings.
method SAMPLR optimizes ground-truth utility function, avoiding curriculum-induced covariate shift.
result SAMPLR preserves optimality under ground-truth distribution, promoting robustness across various environments.
The paper explores how agents can generalize to new environments with unseen variables.
problem Generalizing to new environments with unseen variables.
method Investigates and proposes a method for efficient re-use of past marginal information to achieve out-of-variable generalization.
result The residual distribution after fitting a classifier reveals partial derivatives of the true generating function with respect to unobserved causal parents.
Proposes MSS to identify causal structure from heterogeneous environments.
problem Distribution shifts between environments violate i.i.d. data assumption.
method Sparse mechanism shift hypothesis, score-based approach.
result Identifies entire causal structure with high probability.
Paper tackles noise-robust domain adaptation in noisy environments.
problem Learning machines struggle with domain adaptation in noisy environments.
method The paper proposes offline curriculum learning and proxy distribution based margin discrepancy to mitigate label and feature noise.
result The proposed algorithm significantly outperforms state-of-the-art methods in noisy environments.
New model-based methods adapt pre-trained policies to unseen environments efficiently.
problem High sample complexity in reinforcement learning limits practical applications.
method Combines online learning and adaptive control to adapt policies in unseen environments.
result Proves policies can quickly recover trajectories from source to target environments.
GIB improves neural network generalization by dynamically selecting task-relevant features across different sequential environments.
problem Poor generalization of deep neural networks to unseen environments.
method Proposes a gated information bottleneck (GIB) approach that dynamically drops spurious correlations and selects task-relevant features.
result GIB outperforms other IB approaches in adversarial robustness and OOD detection.
MaxRM uses random forests to minimize maximum risk across different environments.
problem Designing methods that generalize better to test environments with different distributions.
method Introducing variants of random forests based on the principle of MaxRM (Maximum Risk Minimization).
result Proved statistical consistency for the proposed method and provided an out-of-sample guarantee for MaxRM with regret.
Iterative Feature Matching: Toward Provable Domain Generalization with Logarithmic Environmentscs.LG
New algorithm guarantees domain generalization with few environments.
problem Performing well on unseen environments with limited training data.
method Iterative feature matching algorithm with theoretical guarantees.
result Guaranteed domain generalization with logarithmic environments.
New method for ancestral inference in branching processes with random environments.
problem Determining ancestor distribution parameters in branching processes with random environments.
method Generalized method of moments for ancestral inference.
result Limiting distribution of ancestor and offspring estimators decouple and converge to independent Gaussian variables under certain conditions.
Markov chains in random environment with applications in queueing theory and machine learningmath.PR
Proves limiting distributions for Markov chains in random environments.
problem Analyzing Markov chains in random environments.
method Proves existence of limiting distributions using drift and minorization conditions.
result Law of large numbers holds for bounded functionals of the process.
OBSER framework infers sub-environments from objects, outperforming scene-based methods.
problem Zero-shot recognition of environments from object distributions.
method Bayesian framework using metric and self-supervised learning models to estimate object distributions in latent space.
result OBSER framework reliably performs inference in open-world and photorealistic environments, outperforming scene-based methods.
Simpler one-step distributional RL framework for control.
problem Lack of a unified theory for DistrRL in control.
method One-step distributional reinforcement learning (OS-DistrRL) framework.
result Unified theory for policy evaluation and control.
CEA augments reinforcement learning by generating counterfactual experiences.
problem Challenges in reinforcement learning, especially out-of-distribution and inefficient exploration.
method CEA uses variational autoencoders to model state transitions and introduces randomness for non-stationarity. It expands learning data through counterfactual inference.
result CEA outperforms SOTA algorithms in diverse environments.
Paper introduces ITD for detecting distributional changes in decentralized learning environments.
problem Detecting distributional changes in decentralized learning environments with data privacy and heterogeneity concerns.
method Introduces Integrated Transportation Distance (ITD) for two-sample testing in federated learning.
result ITD effectively aggregates information across distributed clients, detecting subtle distributional shifts.
Policy gradient methods ignore the potential value of adjusting environment variables: unobservable state features that are randomly determined by the environment in a physical setting, but are controllable in a simulator. This can lead to slow learning, or convergence to suboptimal policies, if the environment variabl…
Improving Generalization of Reinforcement Learning with Minimax Distributional Soft Actor-Criticcs.LG
Improves RL generalization by minimizing adversarial risk.
problem Overfitting to training environments and poor generalization to unseen scenarios.
method Introduces minimax formulation and distributional framework to RL.
result Trained policy shows improved generalization to different environments.
This paper considers the problem of efficient exploration of unseen environments, a key challenge in AI. We propose a `learning to explore' framework where we learn a policy from a distribution of environments. At test time, presented with an unseen environment from the same distribution, the policy aims to generalize …
RIA method improves OoD generalization for covariate shift.
problem Improving out-of-distribution generalization under covariate shift.
method Adversarial label invariant graph data augmentations for OoD generalization.
result RIA method achieves high accuracy compared to OoD baselines.
Multi-armed bandit algorithms have become a reference solution for handling the explore/exploit dilemma in recommender systems, and many other important real-world problems, such as display advertisement. However, such algorithms usually assume a stationary reward distribution, which hardly holds in practice as users' …
Bayesian model for multi-environment prediction with latent variable changes.
problem Prediction in environments with changing latent variable distributions.
method Bayesian model with empirical Bayes prior and amortized variational algorithm.
result Method outperforms previous approaches in new environments.
DiffATD efficiently discovers targets in partially observable environments using diffusion dynamics.
problem Efficiently discovering targets in partially observable environments with limited sampling.
method DiffATD uses diffusion dynamics to maintain a belief distribution over unobserved states, balancing exploration and exploitation.
result DiffATD outperforms baselines and supervised methods in diverse domains.
PSRL extension for continuing environments reduces regret.
problem Formalizing and analyzing resampling approach for reinforcement learning.
method Continuing PSRL maintains a model of the environment and replaces it with samples from the posterior distribution.
result Established an bound on Bayesian regret.
Optimal financing and dividend distribution in a general diffusion model with regime switchingmath.OC
We study the optimal financing and dividend distribution problem with restricted dividend rates in a diffusion type surplus model where the drift and volatility coefficients are general functions of the level of surplus and the external environment regime. The environment regime is modeled by a Markov process. Both cap…
Bayesian Federated Learning improves model reliability in dynamic environments.
problem Uncertainty quantification and robust adaptation in distributed learning.
method Proposes a continual BFL framework using SGLD for sequential updates and continual learning challenges.
result Continual Bayesian updates preserve knowledge and adapt to evolving data.
OSAMD adapts online to changing distributions with limited labels.
problem Models struggle with continual distribution shifts and expensive labeling in changing environments.
method Online Active Continual Adaptation with OSAMD, an online teacher-student structure and margin-based criterion.
result OSAMD achieves favorable dynamic regret bounds under changing environments with limited labels.
New framework improves model robustness by focusing on stable relations across environments.
problem Standard supervised learning fails under data distribution shift.
method Gradient-based learning framework derived from the principle of independent causal mechanisms (ICM).
result Models generalize well to unseen scenarios, ignoring unstable relations.
New method learns robust representations by modeling environment variation.
problem Learning invariant representations across varying environments.
method Explicitly modeling variation across environments and marginalizing it out.
result Proposed method outperforms invariant-learning methods in various settings.
Research shows collective learning across diverse environments is hard due to privacy and security concerns.
problem Privacy, security, and equity concerns restrict information sharing in diverse AI environments.
method Characterized learning algorithms as choice correspondences, provided minimum requirements for rational learning algorithms.
result The only rational learning algorithm in heterogeneous environments is unilaterally learning from a single environment without information sharing.
Efficient Empowermentstat.ML
Empowerment quantifies the influence an agent has on its environment. This is formally achieved by the maximum of the expected KL-divergence between the distribution of the successor state conditioned on a specific action and a distribution where the actions are marginalised out. This is a natural candidate for an intr…
SMEs provide a transparent testbed for RL evaluation.
problem Lack of precise, white-box diagnostics in RL environments.
method Synthetic Monitoring Environments (SMEs) with fully configurable task characteristics and known optimal policies.
result SMEs allow for precise evaluation of RL algorithms, revealing the impact of specific environmental properties.
Paper formalizes continual learning, proposing a feature extraction approach.
problem Learning from multiple environments without forgetting previous ones.
method Feature extraction framework and gradient-based algorithm DPGD.
result Efficient algorithm DPGD avoids catastrophic forgetting.
XRM discovers environments without human annotations for OOD methods.
problem Costly and biased manual annotations limit OOD methods.
method XRM trains twin networks to mimic mistakes, eliminating hyper-parameters.
result XRM achieves oracle worst-group-accuracy for OOD methods.
New framework learns sufficient invariant features robustly across distribution shifts.
problem Learning robust models under distribution shifts between training and test datasets.
method Sufficient Invariant Learning (SIL) framework and Adaptive Sharpness-aware Group Distributionally Robust Optimization (ASGDRO) algorithm.
result Empirical evaluations confirm ASGDRO's robustness against distribution shifts.
A new validation scheme improves Federated Learning performance.
problem Learning a joint model from distributed, heterogeneous data.
method DVW scheme that uses distributed validation sets.
result DVW outperforms FedAvg in heterogeneous environments.
Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Criticscs.LG
Paper tackles overestimation bias in continuous control, improving performance by 25%.
problem Overestimation bias in off-policy learning.
method Truncated Quantile Critics (TQC) combines distributional representation, truncation, and ensembling of critics.
result TQC outperforms state-of-the-art methods by 25% on the Humanoid environment.
LEADS improves model generalization across different environments.
problem Modeling dynamical systems from varied environments leads to biased or scarce solutions.
method LEADS learns a shared model capturing common dynamics and additional terms for environment-specific dynamics.
result LEADS improves model generalization for both known and novel environments.
ReOPD uses pre-collected teacher trajectories to distill knowledge from multi-turn interactions.
problem The cost of fully online on-policy distillation for multi-turn interactions.
method ReOPD, an off-environment alternative that reuses pre-collected teacher trajectories as replayed prefixes, addressing the prefix trap and distribution shift.
result ReOPD preserves or improves OPD-level accuracy, uses zero tool calls, and is at least 4 faster per training step.
FADE adapts machine learning models to evolving data efficiently.
problem Sequential covariate shift in dynamic environments.
method FADE uses Fisher information geometry for robust learning under SCS.
result FADE achieves up to 19% higher accuracy under severe shifts.
Improved distributed learning with reduced communication costs.
problem Efficient communication in resource-constrained environments for distributed learning.
method Proposed a cost-effective partial communication protocol.
result Communication cost is reduced to , improving significantly on full communication.
New algorithm improves RL performance across different environments.
problem Improving reinforcement learning performance across various environments.
method Designing a fully model-free DRRL algorithm that learns from a single trajectory.
result Demonstrates superior robustness and sample efficiency compared to existing methods.
Estimator improves prediction with missing data in multi-environment settings.
problem Handling missing data in multi-environment settings for robust prediction.
method Derive an estimator from invariance objective under missing outcomes.
result The estimator achieves lower prediction error despite using a biased imputation model.
Meta-learning agents excel at rapidly learning new tasks from open-ended task distributions; yet, they forget what they learn about each task as soon as the next begins. When tasks reoccur - as they do in natural environments - metalearning agents must explore again instead of immediately exploiting previously discover…
NetHack Learning Environment (NLE) tests RL algorithms, offering scalable, complex, and challenging gameplay.
problem Challenging environments for testing RL algorithms.
method Procedurally generated, stochastic, rich, and complex NetHack environment.
result Demonstrates empirical success for early stages of NetHack using RL.
Conditional meta-learning improves meta-learning performance in diverse task environments.
problem Meta-learning struggles with tasks that have heterogeneous complexity.
method Conditional meta-learning infers a task-specific meta-parameter vector.
result Conditional meta-learning outperforms standard meta-learning in diverse task environments.