Optimal adaptive experiment for choosing best treatment with binary outcomes.
problem Choosing the best treatment from binary options in an adaptive experiment.
method Adaptive experiment with two phases: treatment allocation and choice. Neyman allocation method used.
result Neyman allocation is minimax and Bayes optimal, matching lower bounds for regret.
Adaptive allocation with constraints using Thompson sampling.
problem Choosing allocations repeatedly with unknown returns and constraints.
method Thompson sampling approach with finite-sample regret bound.
result Prior-independent bound on expected regret for exponential allocations.
Portfolio allocation is crucial for investment companies. However, getting the best strategy in a complex and dynamic stock market is challenging. In this paper, we propose a novel Adaptive Deep Deterministic Reinforcement Learning scheme (Adaptive DDPG) for the portfolio allocation task, which incorporates optimistic …
Develops adaptive framework for estimating survival effects with censoring.
problem Estimating causal effects in survival data with censoring.
method Derives semiparametric efficiency bound, proposes efficiency-optimal allocation policy, and develops Adaptive Survival Estimator (ASE).
result ASE achieves asymptotic normality via martingale central limit theorem and demonstrates efficiency gains over uniform randomization.
Fairly allocate items with noisy queries, reducing envy.
problem Fairly allocate indivisible goods with unknown valuations.
method Use Gaussian noisy queries to find an envy-free allocation.
result The optimal number of queries scales as \( \frac{m^{2.5}}{Δ^2} \) for large negative-envy.
Optimal asset allocation strategy outperforms stochastic benchmark.
problem Achieving higher terminal wealth than a stochastic benchmark.
method Data-driven Neural Network optimization framework for dynamic asset allocation.
result Optimal adaptive strategy outperforms benchmark with higher median and right-skewed terminal wealth.
Adaptive compute allocation improves model performance by prioritizing harder queries.
problem Inefficiency in allocating test-time compute uniformly across all queries.
method Formulated as a bandit learning problem, proposed adaptive algorithms that estimate query difficulty and allocate compute accordingly.
result Achieved up to 15.29% relative performance improvement on various benchmarks.
Improves content allocation in educational platforms with sparse data.
problem Imbalanced content allocation and delayed convergence in adaptive strategies.
method Introduces WAPTS, an algorithm that refines Thompson Sampling for data-sparse environments.
result Demonstrates earlier and more reliable identification of promising treatments.
POCA optimizes hyperparameters with adaptive allocation for faster convergence.
problem Optimizing hyperparameters for machine learning models.
method Adaptive allocation of computational budget using Bayesian sampling.
result POCA finds strong configurations faster than its competitors.
Adaptive RL optimizes testing resource allocation for dynamic software environments.
problem Optimizing resource allocation for evolving software testing environments.
method Integrates Q-learning with hybrid reward design for sequential decision-making.
result Consistently outperforms static and optimization-based baselines in simulation studies.
ATA optimizes task allocation in distributed machine learning.
problem Greedy task allocation leads to inefficiencies in distributed machine learning.
method Adaptive Task Allocation (ATA) adapts to unknown computation time distributions.
result ATA identifies optimal task allocation without prior knowledge of computation times.
Meta-DRL improves resource allocation in O-RAN networks.
problem Dynamic resource allocation in O-RAN networks.
method Meta Deep Reinforcement Learning (Meta-DRL) inspired by MAML.
result 19.8% improvement in network management performance.
Adaptive AI delegation framework for dynamic decision authority allocation.
problem Dynamic allocation of decision authority to AI-generated recommendations under evolving evidence quality and uncertainty.
method Formulated as a Governance-Aware POMDP, using Bayesian inference for informational state estimation and sequential optimization for authority allocation.
result Sequential Bayesian governance provides the strongest general-purpose policy across AI-quality regimes, adapting to evolving evidence.
DeepAries optimizes rebalancing intervals and asset allocations for better portfolio performance.
problem Fixed rebalancing intervals lead to unnecessary transactions and poor risk-adjusted returns.
method Adaptive deep reinforcement learning with Transformer state encoder and PPO.
result DeepAries outperforms traditional strategies in risk-adjusted returns, transaction costs, and drawdowns.
SEEDA optimizes dose allocation in clinical trials to balance efficacy and safety.
problem Complex relationships between efficacy and toxicity in new drug trials.
method Adaptive clinical trial methodology that maximizes cumulative efficacy while ensuring safety constraints.
result SEEDA outperforms existing methods in finding optimal doses with higher success rates and fewer patients.
Unified framework for response-adaptive targeting in multi-treatment experiments
problem Improving ethical and statistical efficiency in multi-treatment clinical trials
method Response-adaptive targeting strategies
result Unified framework for α-Rebalancing Targeting Strategies (αRTS) Existing approaches to resource allocation for nowadays stochastic networks are challenged to meet fast convergence and tolerable delay requirements. The present paper leverages online learning advances to facilitate stochastic resource allocation tasks. By recognizing the central role of Lagrange multipliers, the unde…
This paper tackles federated incremental learning with dynamic memory allocation for improved model performance in non-IID data.
problem Catastrophic forgetting in federated healthcare systems with non-IID data.
method Dynamic memory allocation strategy based on data replay mechanism.
result Significant performance improvements in medical image datasets compared to baseline models.
Network agents solve adaptive regression problems with compressed signals.
problem Solving regression problems in networks with communication constraints.
method ACTC diffusion strategy with randomized differential compression.
result Optimized resource allocation improves performance.
Proposes a new sampling policy for ranking and selection problems.
problem Improving ranking and selection in adaptive sampling policies.
method Annealed entropic allocation, using soft-min weights and saddlepoint corrections.
result Consistently competitive performance in various settings.
Bayesian approach improves AdaLoRA's performance and efficiency.
problem Improving the efficiency and performance of adaptive low-rank adaptation.
method Utilized Bayesian metrics and the Improved Variational Online Newton (IVON) optimizer for adaptive parameter budget allocation.
result Bayesian counterpart outperforms sensitivity-based importance metric and is faster than AdaLoRA.
Generative profiling improves real-time task timing for varied resource contexts.
problem Inaccurate task timing analysis for complex hardware architectures.
method Nonparametric, conditional multi-marginal Schrödinger Bridge (MSB) formulation for synthesizing context-dependent timing profiles.
result Maximum likelihood accurate execution profiles for unseen resource contexts.
Proposes a flexible feature allocation model for sparse factor analysis.
problem Sparse data and rigid assumptions in traditional exploratory tools.
method Adaptive latent feature sharing with control over feature sparsity.
result Derives a novel adaptive Factor analysis (aFA) and aPPCA for flexible dimensionality reduction.
Paper introduces Market-adaptive Ratio for better portfolio management.
problem Traditional risk-adjusted ratios fail to account for bull and bear markets.
method Integrates ρ parameter and uses reinforcement learning to adjust portfolio allocations dynamically. result Market-adaptive Ratio outperforms traditional ratios in bull and bear markets.
Unified formula for arbitrary liquidity operations in weighted AMMs
problem Decentralized resource allocation in intelligent transportation systems
method Weighted invariant adapted from Balancer-type AMMs
result Unified formula for four resource allocation operations
New algorithms allocate sampling budget to estimate group means without exploration.
problem Allocate sampling budget to estimate means of multiple groups.
method Design exploration-free non-adaptive and adaptive algorithms.
result Prove tighter regret bounds for multi-group mean estimation.
Adaptive batching improves Gaussian process surrogates for noisy level set estimation.
problem Learning the level set of noisy simulator responses.
method Developed four novel adaptive batching schemes for Gaussian process metamodels.
result Adaptive batching brings significant computational speed-ups with minimal loss of modeling fidelity.
FLoE adapts LLMs by selectively deploying LoRA adapters based on layer importance and task requirements.
problem Uniform LoRA deployment across all layers leads to inefficient and redundant parameter allocation.
method FLoE uses Fisher information to dynamically identify task-critical layers and optimizes LoRA ranks.
result FLoE achieves significant efficiency-accuracy trade-offs, especially in resource-constrained environments.
Optimizes budgeted evaluations of LLMs by allocating queries to judges efficiently.
problem Evaluating LLMs with heterogeneous judges and varying costs and reliability.
method Formalizes and analyzes budgeted heteroskedastic multi-judge estimation, proposing EST-IVWE for practical implementation.
result EST-IVWE matches the oracle IVWE rate up to lower-order terms in the budget and is instance-optimal.
Adaptive strategies reduce pension fund costs and risks.
problem Managing longevity and volatility risks in pension funds.
method Modular simulation framework with customizable metrics.
result Substantial reduction in pension plan costs and default risk.
A new test improves statistical inference in bandit algorithms without sacrificing adaptiveness.
problem Challenges in statistical inference for adaptive randomised experiments in bandits.
method An allocation probability test for Thompson Sampling without trading-off regret or requiring large sample sizes.
result Improves statistical inference in small samples, showing advantages in mental health experiments.
New study shows non-adaptive trials can be outperformed by adaptive designs in treatment selection.
problem Determining the best allocation of resources in clinical trials.
method Analysis of batched arm elimination designs and comparison with completely randomized trials.
result Simple adaptive designs universally and strictly dominate non-adaptive completely randomized trials for at least three treatment arms.
New algorithm tackles resource allocation in multi-armed bandits to balance speed and throughput.
problem Balancing speed and throughput in stochastic multi-armed bandits with limited resources.
method Proposes an algorithm that trades off between information accumulation and throughput.
result Upper bounds the time taken to find the best arm with a given target success probability.
New method improves treatment effect estimation in adaptive experiments with noncompliance.
problem Estimating average treatment effect in adaptive experiments with binary instrumental variable.
method AMRIV estimator that balances outcome noise and compliance variability.
result AMRIV achieves semiparametric efficiency bound and is robust to noncompliance.
Paper proposes ClipSMT algorithm for better ATE estimation.
problem Adaptive estimation of Average Treatment Effect (ATE).
method ClipSMT algorithm for improved Neyman regret.
result Achieves exponential improvements in Neyman regret.
We present an online approach to portfolio selection. The motivation is within the context of algorithmic trading, which demands fast and recursive updates of portfolio allocations, as new data arrives. In particular, we look at two online algorithms: Robust-Exponentially Weighted Least Squares (R-EWRLS) and a regulari…
Best-of-∞ improves LLM performance by efficiently allocating inference-time computation.
problem Achieving optimal performance in test-time LLM ensembling with infinite budget.
method Adaptive generation scheme and weighted ensembles of LLMs, formulated as mixed-integer linear program.
result Optimal ensemble weighting improves performance over individual models.
For large scale on-line inference problems the update strategy is critical for performance. We derive an adaptive scan Gibbs sampler that optimizes the update frequency by selecting an optimum mini-batch size. We demonstrate performance of our adaptive batch-size Gibbs sampler by comparing it against the collapsed Gibb…
Algorithm allocates perishable resources online to minimize envy and inefficiency.
problem Online allocation of perishable resources to minimize envy and inefficiency.
method Algorithm uses predictions of perishing order and desired envy bound to adaptively allocate resources.
result Algorithm achieves optimal envy-efficiency trade-off as derived from strong lower bounds.
It is challenging to develop stochastic gradient based scalable inference for deep discrete latent variable models (LVMs), due to the difficulties in not only computing the gradients, but also adapting the step sizes to different latent factors and hidden layers. For the Poisson gamma belief network (PGBN), a recently …
Algorithm identifies best policy in MDPs with adaptive sampling.
problem Best policy identification in discounted MDPs with limited samples.
method Derive lower and upper bounds on sample complexity, design KLB-TS algorithm.
result KLB-TS algorithm achieves nearly-optimal sample allocation.
The paper tackles resource allocation for arms with unknown and random rewards, achieving optimal regret bounds.
problem Allocating resources on arms with unknown and random rewards.
method Developed two algorithms with optimal regret bounds for b∈[0,1], demonstrating a phase transition at b=1/2. result Achieved optimal gap-dependent and gap-independent regret bounds for b∈[0,1]. Robo-advisors use MPC to create dynamic investment strategies.
problem Static allocation methods limit robo-advisors' effectiveness.
method Combines MPC with Hidden Markov Model and Black-Litterman for dynamic asset allocation.
result MPC-based strategies outperform static approaches in dynamic and risk-budgeting criteria.
Optimal algorithm for identifying best-arm with minimal regret.
problem Identifying the best arm in two treatments with limited budget.
method Neyman allocation based on outcome standard deviations.
result Neyman allocation is minimax optimal for simple regret.
New algorithms improve best-arm identification with varying rewards.
problem Identifying the best arm with varying reward variances in fixed budget.
method Proposed two algorithms: SHVar for known variances, SHAdaVar for unknown variances; uses non-uniform budget allocation.
result Bounding misidentification probabilities for both algorithms.
The paper tackles budget allocation for multiple campaigns using a novel combinatorial bandit approach.
problem Maximizing cumulative returns with limited budgets across various ad lines.
method Formulated as a multi-task combinatorial bandit problem, integrates Bayesian hierarchical models, and uses Thompson sampling.
result Demonstrates robustness and adaptability in maximizing overall cumulative returns.
This study uses HMM and RL to dynamically allocate equities, Treasuries, and gold based on market regimes.
problem Developing a dynamic portfolio allocation strategy for different market conditions.
method Characterizes market regimes using Markov switching models and HMM, then applies RL for allocation decisions.
result RL-based allocation outperforms passive strategies, providing lower drawdowns and higher Sharpe ratios.
Performance of machine learning algorithms depends critically on identifying a good set of hyperparameters. While recent approaches use Bayesian optimization to adaptively select configurations, we focus on speeding up random search through adaptive resource allocation and early-stopping. We formulate hyperparameter op…