New method learns policies from past data.
problem Learning decision policies from experience.
method Fully Probabilistic Design (FPD) formalism for stochastic policy finding.
result Proposes a new approach for policy learning.
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48 results for “past data”
Bayesian model helps doctors diagnose by mixing past cases.
problem Doctors rely on past experience for diagnosis.
method Developed a novel mathematical model to mimic case-based reasoning.
result Yields predictive accuracy and insight into medical data.
Validates policies using past observational data with guarantees about out-of-sample performance.
problem Evaluating decision policies using past data observed under a different policy.
method Sample-splitting method to draw inferences about the entire loss distribution with finite-sample coverage guarantees.
result Valid inferences about out-of-sample loss with finite-sample coverage guarantees, accounting for model misspecifications.
Past lightcones of certain points in globally hyperbolic spacetimes determine the entire spacetime.
problem Determining the entire spacetime from the past lightcone of a point.
method Analyzing properties of globally hyperbolic spacetimes and using null lines and observer horizons.
result Past lightcones of certain points in globally hyperbolic spacetimes determine the entire spacetime (up to isometry).
New method improves continual learning by anchoring past knowledge.
problem Catastrophic forgetting in continual learning.
method Bilevel optimization to update current task knowledge while keeping past task predictions.
result Improves accuracy and forgetting metrics compared to experience replay.
BAM integrates new data while selectively remembering past observations.
problem Slow adaptation and convergence to incorrect parameter values in non-stationary environments.
method Bayes' theorem with adaptive memory selection.
result BAM generalizes and demonstrates continuous adaptation in changing environments.
A method to improve sequential learning by keeping past data errors in check.
problem Challenges in sequential learning with Gaussian processes due to accumulating errors.
method Memory-based dual sparse variational Gaussian processes.
result Improves accuracy in inference and learning for various applications.
DBULL learns new clusters without forgetting past knowledge in streaming unlabelled data.
problem Challenges in Unsupervised Lifelong Learning with evolving data distributions and class labels.
method Bayesian framework for incremental learning, Deep Bayesian Unsupervised Lifelong Learning (DBULL) algorithm, knowledge preservation mechanism, automatic cluster discovery.
result DBULL can progressively discover new clusters without forgetting past knowledge in unlabelled data.
CRRN detects printer defects in SPI data of SMT boards.
problem Detecting defects in solder paste inspection data.
method Convolutional Recurrent Reconstructive Network (CRRN) with CSTMs and ST-Attention.
result CRRN outperforms conventional models in anomaly detection.
Our goal is to build robust optimization problems for making decisions based on complex data from the past. In robust optimization (RO) generally, the goal is to create a policy for decision-making that is robust to our uncertainty about the future. In particular, we want our policy to best handle the the worst possibl…
Meta Optimal Transport learns from past problems to solve similar OT problems faster.
problem Solving similar optimal transport problems repeatedly from scratch is inefficient.
method Amortized optimization to predict optimal transport maps from past solutions.
result Meta OT models can solve new problems faster than standard methods.
LiDER refreshes past experiences in RL by dreaming about them.
problem Improving data efficiency in off-policy RL algorithms.
method Refreshing past experiences in a replay buffer using the current policy.
result LiDER consistently improves performance in Atari games.
A new method combines regularization and generative rehearsal for continual learning.
problem Catastrophic forgetting in neural networks over past tasks.
method Uses a normalizing flow to conditionally store past task data and regularize network embeddings.
result Performs favorably compared to state-of-the-art approaches with constant memory overhead.
A new method combines generative models and regularization to prevent forgetting in continual learning.
problem Catastrophic forgetting in neural networks when learning new tasks.
method Uses a normalizing flow as a generative model to keep past data embeddings and regularize them.
result Performs favorably compared to existing methods with constant memory overhead.
Meta-Q-Learningcs.LG
Meta-Q-Learning improves meta-Reinforcement Learning using past data.
problem Improving meta-Reinforcement Learning performance.
method Meta-Q-Learning combines Q-learning, multi-task objectives, and off-policy updates to adapt policies from past data.
result Meta-Q-Learning compares favorably with state-of-the-art meta-RL algorithms on benchmarks.
Framework uses experience replay to prevent deep networks from forgetting past tasks.
problem Deep networks forget past tasks after learning new ones in sequential multitask learning.
method Generative model that couples current task with past learned tasks through a discriminative embedding space.
result Framework learns a shared abstract distribution across all tasks, preventing catastrophic forgetting.
Study on RNNs' ability to approximate past-dependent Hölder functions and their application to regression.
problem Understanding and optimizing the approximation capacity of RNNs for regression tasks.
method Derivation of upper bounds on RNN approximation error for Hölder smooth functions and application to regression.
result Achievement of minimax optimal prediction error bounds for RNNs under various data assumptions.
Machine learning infers fluid dynamics from data without prior knowledge.
problem Inferring complex fluid behavior from limited data.
method Reservoir computing for partial and full inference of fluid variables and energy functions.
result Reservoir systems can infer long-term fluid dynamics and energy spectra from past data.
New method generates portfolios using market weights and past data.
problem Creating efficient trading strategies based on market weights.
method Pathwise generation of portfolios using market weights and past data.
result Improved conditions for outperforming the market over time.
Dynamic covariate balancing: estimating treatment effects over time with potential local projectionsecon.EM
Dynamic treatment effects estimated over time using covariate balancing.
problem Estimating treatment effects in panel data with dynamic treatments.
method Dynamic covariate balancing with potential local projections.
result Established inferential guarantees for the proposed method.
A simple approach improves performance on both past and future tasks in lifelong learning.
problem Forgetting in lifelong learning, where performance on past tasks degrades when learning new tasks.
method Representation ensembling to improve performance on both future and past tasks.
result Representation ensembling demonstrates both forward and backward transfer across various datasets.
EvoRate metric assesses learnability of sequential data by measuring predictive information.
problem Model misspecification due to misinterpreting patterns in sequential data.
method Predictive information framework based on mutual information between past and future.
result Temporal patterns fundamentally constrain learnability; optimal predictors cannot outperform intrinsic information limit.
Approach collects missing outcomes to improve fairness in classification.
problem Lack of true outcomes for incorrectly classified samples leads to biased classifiers.
method Exploration-based data collection to ensure all subpopulations are represented and fairness properties are encoded.
result Trained classifier converges to a fair classifier with bounded false positives.
A new spectrum recovers cobordism cut and paste groups of manifolds with boundary.
problem Defining and studying cobordism cut and paste groups of manifolds with boundary.
method Constructing a spectrum that recovers the cobordism cut and paste groups of manifolds with boundary.
result Construction of a spectrum that recovers the cobordism cut and paste groups of manifolds with boundary.
Modeling a temporal process as if it is Markovian assumes the present encodes all of the process's history. When this occurs, the present captures all of the dependency between past and future. We recently showed that if one randomly samples in the space of structured processes, this is almost never the case. So, how d…
A new method for exponentially weighted moving models using approximations.
problem Efficiently updating moving averages for time series data.
method Approximates EWMM using a fixed window and quadratic term, solving non-growing problems.
result Approximation produces estimates similar to exact EWMM.
Soccer Team Vectorscs.LG
STEVE creates vectors for soccer teams based on past match data.
problem Creating meaningful representations of soccer teams.
method Learning real valued vectors using past match data.
result STEVE outperforms competitors in team market value estimation.
Proposes a model for time-varying regression coefficients.
problem Uncertainty in forecasting due to changing correlations over time.
method Adopting state space literature, models how regression coefficients change over time.
result Accurate estimates for continuous outcomes but fails for binary outcomes.
In this paper, we extend the results of Klainerman and Rodnianski in \cite{KR:Trapped}, which were obtained for a finite region, by showing similar results from past null infinity. This allows us to recover and extend the results from past null infinity in the work of Christodoulou \cite{Chr:book}.
Continual state learning model using generative replay for RL.
problem Efficiently learn and adapt state representations as the environment changes.
method Variational Auto-Encoders for state representation and Generative Replay for past knowledge.
result Automatic environment change detection and efficient state representation.
New method reconstructs past foehn occurrences using unsupervised and supervised learning.
problem Reconstructing past foehn occurrences due to lack of direct measurement.
method Combining unsupervised and supervised learning methods to infer foehn occurrences from reanalysis data.
result Accurate hourly reconstructions of past foehn occurrences for 83 years.
The paper shows failure of smooth pasting principle in time-inconsistent stopping problems.
problem Time-inconsistent stopping problems with non-constant time preference rates.
method Analysis of the smooth pasting principle within the intra-personal game theoretic framework.
result The smooth pasting principle fails under time-inconsistency and does not guarantee equilibrium solutions.
In this paper, we shall be concerned with a relation between TQFTs and cut and paste invariants introduced by Karras, Kreck, Neumann and Ossa. Cut and paste invariants, or SK invariants, are functions on the set of smooth manifolds that are invariant under the cutting and pasting operation. Central to the work in this …
K-priors enable quick adaptation with minimal retraining.
problem Machine learning models struggle to adapt to changes efficiently.
method Combines weight and function-space priors to reconstruct past gradients.
result Adaptation with K-priors achieves similar performance to full retraining with less data.
Adaptive object detection method synthesizes target domain images from source domain images.
problem Large domain gap between source and target domains in object detection.
method Cross-domain CutMix with adversarial learning.
result Higher accuracy in different domain settings compared to conventional methods.
New method prevents deep learning forgetting past by remembering key examples.
problem Catastrophic forgetting in continual learning.
method Functional regularisation using Gaussian Process formulation.
result Achieves state-of-the-art performance on benchmarks.
The paper studies the past inextendibility of FLRW spacetimes using the VDR asymptote.
problem Investigating the past inextendibility of FLRW spacetimes.
method Using the volume-distance-ratio (VDR) asymptote to assess spacetime inextendibility criteria.
result Conditions for past inextendibility of FLRW spacetimes are identified.
A simple method to create new 4-manifolds by altering fundamental groups.
problem Creating new 4-manifolds with specific fundamental groups.
method Cut-and-paste mechanism to alter fundamental groups.
result Constructs new 4-manifolds with fixed-point free involutions.
New algorithm reduces online learning regret by exploiting historical invariances.
problem Stochastic non-stationary linear bandits with changing reward models.
method ISD-linUCB algorithm that learns invariances in reward model.
result Significant regret improvements in fast-changing environments with historical data.
The study examines how investor protection and past information affect stock returns and interest rates.
problem Empirical regularities related to investor protection and past information in asset pricing models.
method Developed a dynamic asset pricing model with a controlling shareholder and good/bad memory in budget dynamics.
result Good/bad memory of investors on historical market information affects stock returns and interest rates, strengthening investor protection in high ownership concentration.
Crowdbreaks tracks health trends using social media and crowdsourcing.
problem Challenges in tracking health trends using social media data.
method Open platform for continuous crowdsourced labelling of social media content.
result Accelerates research process in public health.
PMP extends GNNs to handle past states efficiently.
problem Efficient querying of data structures dependent on previous states.
method Persistent Message Passing (PMP) which persists past states through new nodes.
result Significantly outperforms GNNs in handling out-of-distribution data.
No-PASt-BO improves GP-Hedge by reducing past influence and normalizing acquisition functions.
problem GP-Hedge's reliance on past performance can lead to poor acquisition function dominance.
method No-PASt-BO reduces past influence and normalizes acquisition functions.
result No-PASt-BO outperforms GP-Hedge on both synthetic and real-world tasks.
Enhances SAC for better sample efficiency in continuous-action tasks.
problem Improving sample efficiency in soft actor-critic algorithms.
method Integrating Emphasizing Recent Experience (ERE) with Soft Actor-Critic (SAC) and Priority Experience Replay (PER).
result ERE significantly improves sample efficiency compared to vanilla SAC, especially for continuous-action tasks.
AdapVAE learns streaming data clustering and feature learning adaptively.
problem Adaptive clustering and feature learning for streaming data.
method Bayesian Nonparametric (BNP) modeling with Deep Neural Networks (DNNs) for feature learning, online variational inference algorithm.
result AdapVAE can adaptively detect novel clusters in emerging data without catastrophic forgetting.
Modeling financial crises and cryptocurrency shocks using copulae clustering.
problem Detecting financial crises and shock events in stock and cryptocurrency markets.
method Copulae clustering based on probability distribution distances.
result Successfully detected all past crises and shock events in stock and cryptocurrency markets.
Extends Penrose's method to null shells with pressure and energy flux.
problem Constructing null thin shells with arbitrary gravitational/matter content.
method Derive locally Lipschitz metric and coordinate transformation.
result Example of null shell with non-trivial energy density, flux, and pressure in Minkowski space.
The paper develops a method to learn robust decision policies from observational data, reducing high-cost outcomes.
problem Learning safe decision policies from observational data with high-risk outcomes.
method Develops a method to learn policies that reduce high-cost outcomes, valid under finite samples and uneven feature overlap.
result Validates the method with real and synthetic data, providing statistical bounds on decision costs.