Study aggregation of statistical evidence under unknown dependence using group-invariance.
problem Aggregating statistical evidence under unknown and complex dependence structures.
method Develops a framework using group-invariance and permutation-based constructions to aggregate evidence across transformed datasets.
result Shows uniform improvement in critical values for single-batch aggregation over deterministic calibrations, adapting to unknown dependence structures.
The paper introduces BCART models for aggregate claim amount, improving frequency-severity and joint modeling.
problem Modeling aggregate claim amount with frequency-severity and joint dependencies.
method Developed three types of BCART models: frequency-severity, sequential, and joint models. Used various distributions for claim severity data.
result Weibull distribution outperforms gamma and lognormal for right-skewed, heavy-tailed claim severity data.
Adaptive Bayesian learning aggregates experts to improve performance.
problem Bayesian online learning's performance depends on inferential choices.
method Treat Bayesian update rules as experts and aggregate them based on sequential predictive losses.
result The aggregate competes with the best expert in hindsight at a low aggregation cost.
A new method for federated learning aggregates data from multiple sites efficiently.
problem Aggregating data from multiple sites securely and effectively.
method Sequential federated learning with distributed computing.
result Preserves information from individual analyses and accelerates the analysis process.
Predict sepsis early from EHR data with aggregated clinical events.
problem Predict sepsis from clinical data in EHR with temporal interactions.
method Aggregates heterogeneous clinical events, captures temporal interactions with LSTM.
result Achieved high utility score (0.321) in PhysioNet/Computing in Cardiology Challenge 2019.
A simple algorithm improves model generalization in expert advice settings.
problem Improving model generalization in expert advice settings.
method A naive aggregation algorithm for point estimations of high-dimensional nonlinear functions.
result Conditions for optimal parameter estimates through sequential mixing distribution strategies.
This paper shows how to combine optimal tests into log-optimal processes.
problem How to combine optimal sequential tests into log-optimal processes.
method Using a new class of WAIT e-processes, the paper aggregates asymptotically optimal sequential tests into asymptotically log-optimal processes.
result It is possible to aggregate asymptotically optimal sequential tests into asymptotically log-optimal e-processes.
ComiRec framework predicts user interests for personalized recommendations.
problem Predicting user interests from sequential behavior data.
method ComiRec framework captures multiple user interests and balances recommendation accuracy and diversity.
result ComiRec achieves significant improvements over state-of-the-art models in sequential recommendation.
New method detects changes without partitioning distributions.
problem Detects changes in unknown probability distributions without partitioning.
method Aggregates e-processes over possible changepoints and infimum over candidate no-change distributions.
result Achieves first-order asymptotically optimal detection delay.
AAggFF improves federated learning fairness through sequential decision making.
problem Achieving client-level fairness in federated learning systems.
method Unified online convex optimization framework for adaptive aggregation strategies.
result AAggFF achieves better client-level fairness in federated learning.
We consider the setting of sequential prediction of arbitrary sequences based on specialized experts. We first provide a review of the relevant literature and present two theoretical contributions: a general analysis of the specialist aggregation rule of Freund et al. (1997) and an adaptation of fixed-share rules of He…
New algorithm improves graph inference tasks.
problem Complex graph reasoning and prediction tasks.
method Policy Message Passing algorithm reformulates graph inference as stochastic sequential processes.
result Consistently outperforms state-of-the-art models.
The paper proposes a machine learning approach for production forecasting without model calibration.
problem Generating accurate production forecasts for reservoir development.
method Sequential model aggregation using machine learning algorithms without model calibration.
result The proposed method provides robust multi-step-ahead production forecasts.
Paper tightens statistical aggregation results using local complexity.
problem Combining predictors to achieve nearly optimal predictions.
method Replacing global complexity with local complexity, using PAC-Bayes localization.
result Localized versions of classical aggregation bounds proven, improving previous results.
Active learning method reduces labeling cost for regression models with aggregated data.
problem Reducing labeling cost for training regression models with aggregated data.
method Sequentially selects sets to be labeled using mutual information quantifying model parameter uncertainty.
result Achieves better predictive performance with fewer labeled sets.
Synthesizes robust estimators for domain adaptation.
problem Improving prediction accuracy in target domain with limited data.
method Synthesizes a family of robust least squares estimators using convex optimization.
result Robust strategies can outperform non-robust interpolations.
Study online monotone density estimation with expert aggregation and log-optimal calibration.
problem Online monotone density estimation and log-optimal calibration.
method Proposed two online estimators: Grenander estimator and expert aggregation estimator.
result Online estimators achieve O ( n 1 / 3 ) O(n^{1/3}) O ( n 1/3 ) cumulative log-likelihood gap and n log n \sqrt{n\log{n}} n log n pathwise regret bound. Stochastic simulation techniques employed for the analysis of portfolios of insurance/reinsurance risk, often referred to as `Aggregate Risk Analysis', can benefit from exploiting state-of-the-art high-performance computing platforms. In this paper, parallel methods to speed-up aggregate risk analysis for supporting re…
Ensemble methods for classification and clustering have been effectively used for decades, while ensemble learning for outlier detection has only been studied recently. In this work, we design a new ensemble approach for outlier detection in multi-dimensional point data, which provides improved accuracy by reducing err…
Study optimal policy regret in partially observable Markov games with adaptive opponents.
problem Optimal sequential decision-making in partially observable environments against strategic, adaptive opponents.
method An epoch-based optimistic maximum-likelihood algorithm that selects one policy per epoch using confidence sets built cumulatively from past data.
result Achieves i l d e O ( T ) ilde{O}(\sqrt{T}) i l d e O ( T ) policy regret for fixed problem parameters, with explicit dependence on horizon, adversary memory, confidence radius, and aggregate Eluder dimension. Paper tackles learning win-win solutions in aggregation systems.
problem Learning approximate equilibrium solutions in aggregation systems to keep suppliers.
method Multi-Agent Reinforcement Learning (MARL) with variance reduction mechanism.
result Approach reduces value variance and moves joint solution closer to Nash Equilibrium.
New model accounts for sequential dependence in LLM reliability.
problem Uncertainty in LLM reliability assessment due to sequential interactions.
method Extended Bayesian framework with Hidden Markov Model for sequential dependence.
result Ignoring sequential dependence leads to overconfident reliability estimates.
New algorithm predicts piecewise regular functions online.
problem Online prediction of piecewise regular functions.
method Modified sleeping experts aggregation algorithm.
result Oracle risk bounds for all local regions.
A new GNN model SPIN achieves state-of-the-art performance on diverse real-world datasets.
problem Graph classification efficiency and accuracy.
method Parallel neighborhood aggregations (PA-GNNs) and SPIN model.
result SPIN model achieves state-of-the-art performance on diverse real-world datasets.
This paper shows how to construct sequential tests with power one against weakly compact sets in Polish spaces.
problem Testing composite null hypotheses involving weakly compact sets in Polish spaces.
method Develops sequential tests for i.i.d. laws in Polish spaces, providing a sufficient condition for power one.
result Power-one sequential tests exist for weakly compact sets against their complements in i.i.d. laws in Polish spaces.
Sequential hypothesis testing is a desirable decision making strategy in any time sensitive scenario. Compared with fixed sample-size testing, sequential testing is capable of achieving identical probability of error requirements using less samples in average. For a binary detection problem, it is well known that for k…
A hierarchical model shows how scaling laws emerge from sequential feature recovery.
problem Emergence of scaling laws from feature learning in multi-layer networks.
method Layer-wise spectral algorithm adapted to compositional structure, sequential feature detection.
result Sequential detection of latent features, leading to explicit power-law decay of prediction error.
New method combines adaptive learning rate and flexible prediction for online loss aggregation.
problem Online aggregation of unbounded losses using shifting experts.
method Adapted AdaHedge algorithm with Fixed Share meta-algorithm for signed unbounded losses.
result Improved shifting regret and validity of regret bounds in adversarial setting.
New methods optimize experiment selection for sequential data, improving model accuracy.
problem Optimizing experiment selection for sequential data in multidimensional cases.
method Adopting greedy experiment selection methods for maximum likelihood estimation.
result Proposed methods produce consistent and asymptotically normal estimators.
This paper compares neural networks with traditional methods for churn prediction using financial data.
problem Churn prediction with sequential data and deep neural networks.
method Assesses LSTM neural networks combined with RFM variables against logistic regression models.
result LSTM neural networks outperform traditional methods in churn prediction.
Framework for online hypothesis testing across various data types.
problem Testing various nonparametric hypotheses in data streams.
method Unified framework using operators on data distributions, leveraging ML models.
result Efficient, adaptive, and error-controlled sequential tests.
pFedGame uses game theory for decentralized federated learning in dynamic networks.
problem Performance bottlenecks, data bias, model convergence issues, and model poisoning attacks in federated learning.
method pFedGame employs game theory to decentralize federated learning, avoiding a central aggregation server and addressing dynamic network challenges.
result pFedGame achieves higher accuracy (over 70%) in heterogeneous data compared to existing methods.
Paper tackles cold-start problems in online recommendation with few-shot learning and meta learning.
problem Cold-start problems in practical recommendations with limited interaction data.
method Combines scenario-specific learning with sequential meta-learning to create an integrated end-to-end framework.
result Significant gains over state-of-the-arts for cold-start problems in online recommendation.
Predicts next item in sequential bundles using Transformers.
problem Predicting next item in sequentially consumed bundles.
method Used custom Transformers, GPT-3, LSTM, reinforcement learning, Markov models.
result Custom Transformer with decoder-only architecture most accurate.
We study a new aggregation operator for gradients coming from a mini-batch for stochastic gradient (SG) methods that allows a significant speed-up in the case of sparse optimization problems. We call this method AdaBatch and it only requires a few lines of code change compared to regular mini-batch SGD algorithms. We p…
New method recalibrates VaR for option books, reducing forecast errors.
problem Inaccurate VaR forecasts due to missing operational choices.
method Marking-aware sequential VaR recalibration targeting normalized book-level loss.
result Sequential VaR recalibration improves VaR performance across different markets and options.
Proposes a new method for sentiment analysis using ensemble learning.
problem Capturing useful features for word-based sentiment analysis.
method Data Shared Adaptive Bootstrap Aggregated Lasso (AdaBag) Lasso.
result Demonstrates improved performance over PCA-LD in IMDb data.
KineticSim accelerates financial market simulations 3406x over CPU.
problem Simulating financial markets at scale with multi-agent models is bottlenecked by sequential processing and GPU kernel overhead.
method Formalized and implemented a reusable parallel design pattern for iterative multi-agent reductions in thread-block shared memory.
result Achieved a peak throughput of over 54.7 billion agent-events per second, delivering 3406x speedup over CPU.
KineticSim: A lightweight, high-performance execution engine for real-time market simulators
problem Simulating financial markets at scale with multi-agent models
method Reusable parallel design pattern: persistent, state-carrying clearing for iterative multi-agent reductions
result Reduces per-step critical-path depth from Theta(L+A) to Theta(log L + ceil(A/L))
The paper studies how to improve language model inference using particle filtering.
problem Understanding the accuracy-cost tradeoffs of inference-time methods for large language models.
method Introduces particle filtering algorithms like Sequential Monte Carlo (SMC) to study language model inference.
result Identifies criteria enabling non-asymptotic guarantees for SMC and fundamental limits faced by all particle filtering methods.
New method detects and locates changes in spatio-temporal point processes.
problem Detecting and localizing changes in spatio-temporal data.
method Score-based, likelihood-free approach estimating change time and region.
result The method provides theoretical guarantees on detection and localization accuracy.
Pareto optimal centralized risk sharing with multiple agents
problem Centralized risk sharing with endogenous prices
method Inclusive and fair Pareto optimality
result Equivalence between inclusive and fair Pareto optimality and balanced sequential optimization
Paper tackles robust decision-making from multiple sites with shared structure.
problem Learning robust sequential decisions from heterogeneous multi-site datasets.
method Group-Robust MDPs with d-rectangular uncertainty sets, feature-wise worst-case aggregation, and cluster-level pooling.
result Proves suboptimality bound for robust planning policy under robust partial coverage assumption.
Improved sales forecasting at various levels using ensemble methods.
problem Enhancing sales forecasting accuracy at different levels of e-commerce data.
method Hierarchical robust aggregation of sales forecasts using exponential smoothing and Holt's linear trend method.
result Better forecasts at subsubfamily, subfamily, and family levels compared to individual techniques.
Annealed Entropic Allocation improves ranking and selection by mitigating hard switching and improving finite-budget discrimination.
problem Sequential budget allocation in ranking and selection
method Annealed weighted soft-min framework
result Surrogate converges uniformly to the hard minimum, soft-min weights concentrate on active challengers, and target allocation map is continuous.
This work tackles large action spaces in RL by binarizing actions.
problem Large action spaces in reinforcement learning cause significant challenges.
method Sequentializing actions and binarizing the action space.
result Binarizing the action space can significantly improve RL algorithms and reduce state space size.
OPERA blends multiple OPE estimators to evaluate new policies offline.
problem Lack of reliable offline policy evaluation methods for new policies.
method Adaptive blending of multiple OPE estimators without explicit selection.
result Consistent and reliable policy evaluation framework for offline RL.
Improved bounds for unbounded losses using transductive priors.
problem Sequential regression and classification with unbounded losses.
method Exponential weights algorithm with transductive priors.
result Statistical bounds independent of design vectors and optimal solution norm.