Paper improves sparse linear bandits by accounting for noise variance.
problem Sparse linear bandits with unknown noise variance.
method Develops a general framework to convert variance-aware algorithms to sparse linear bandits.
result Achieves $\widetilde{\mathcal O}\left(\sqrt{d\sum_{t=1}^T σ_t^2} + 1
ight)$ regret, interpolating between worst-case and benign settings.
Optimizes ranking from click feedback in a bandit setting.
problem Learning to rank from Bernoulli click feedback in a bandit setting.
method Variance-aware confidence sets derived from Bernstein and Chernoff bounds for optimal algorithms.
result Optimal algorithms for the case of small mean rewards, improving on previous suboptimal results.
UCB-V algorithm improves on UCB for MAB problems with variance estimates.
problem Optimizing arm selection in MAB problems with variance information.
method Asymptotic and high probability analysis of UCB-V algorithm.
result UCB-V can exhibit instability in arm-pulling rates but achieves refined regret bounds.
VGE provides a practical approach to uncertainty estimation in ensemble models.
problem Uncertainty estimation in ensemble models using additive decomposition breaks down.
method Variance-Gated Ensembles (VGE) introduces a differentiable framework with a signal-to-noise gate.
result VGE provides a Variance-Gated Margin Uncertainty (VGMU) score and Variance-Gated Normalization (VGN) layer.
Neural- σ 2 σ^2 σ 2 -LinearUCB improves regret in neural contextual bandits.
problem Balancing exploration and exploitation in neural contextual bandits.
method Proposes a variance-aware neural UCB algorithm using neural representations and an upper bound of reward noise variance.
result Oracle and practical versions of Neural- σ 2 σ^2 σ 2 -LinearUCB achieve better regret guarantees and performance. New algorithm resists corruption in linear contextual bandits.
problem Adversarial corruption in linear contextual bandits.
method Variance-aware algorithm with multi-level partition and adaptive confidence sets.
result Regret bound of i l d e O ( C 2 d ∑ t = 1 T σ t 2 + C 2 R d T ) ilde{O}(C^2d\sqrt{\sum_{t = 1}^T σ_t^2} + C^2R\sqrt{dT}) i l d e O ( C 2 d ∑ t = 1 T σ t 2 + C 2 R d T ) . FGTSVA improves Thompson Sampling for contextual bandits with optimal variance-aware regret.
problem Optimizing regret bounds for Thompson Sampling in contextual bandits.
method Developed FGTSVA, a variance-aware Thompson Sampling algorithm for contextual bandits with a new decoupling coefficient.
result Achieved optimal regret bound of i l d e O ( d c ⋅ log ∣ F ∣ ∑ t = 1 T σ t 2 + d c ) ilde{O}(\sqrt{\mathrm{dc}\cdot\log|\mathcal{F}|\sum_{t=1}^Tσ_t^2}+\mathrm{dc}) i l d e O ( dc ⋅ log ∣ F ∣ ∑ t = 1 T σ t 2 + dc ) . VA-OPE improves OPE by incorporating variance information, achieving tighter error bounds.
problem Estimating value function of a target policy from offline data collected by a behavior policy.
method Proposes VA-OPE, an algorithm that reweights Bellman residual using estimated variance of the value function.
result Achieves a tighter error bound than the best-known result.
Faster convergence of kernel mean embeddings using variance information.
problem Speeding up the convergence rate of kernel mean embeddings.
method Leveraging variance information in reproducing kernel Hilbert space and estimating variance from data.
result Efficiently estimate variance information from data to achieve distribution-agnostic convergence bounds.
Improved regret bound for MNL MDPs with variance-aware approach.
problem Optimal reinforcement learning for MNL MDPs with structured variance.
method Introducing a problem-dependent constant measuring average variance, proposing an algorithm with improved regret bound.
result Minimax optimal regret bound of O ( d H 2 σ ˉ T T ) O(dH^2\barσ_T\sqrt{T}) O ( d H 2 σ ˉ T T ) for structured MDPs. CASP improves portfolio optimization by considering asset covariance.
problem Infeasibility in cardinality-constrained portfolio optimization.
method CASP uses volatility-normalized selection and covariance-aware projection.
result CASP-Basic delivers lower portfolio variance than standard Euclidean repair.
A new method resolves non-identifiability in reward modeling using anchor labels.
problem Non-identifiability in reward modeling from pairwise preferences alone.
method Anchor-guided Variance-aware Reward Modeling (AVRM) framework.
result AVRM resolves non-identifiability and improves reward modeling performance.
New method discovers mean and variance causal graphs from heteroscedastic data.
problem Understanding causal relationships in data with varying variance.
method Bayesian, moment-driven approach inferring separate mean and variance causal graphs.
result Accurately recovers mean and variance structures from heteroscedastic data.
New algorithms reduce regret in online MDPs by adapting to data and variance.
problem Adapting to both adversarial and stochastic environments in online MDPs.
method Develops algorithms based on global optimization and policy optimization, using optimistic follow-the-regularized-leader with log-barrier regularization.
result Achieves refined data-dependent and variance-dependent regret bounds.
This paper addresses dueling bandits with contextual information, improving regret bounds by accounting for variance.
problem Minimizing cumulative regret in dueling bandits with contextual information.
method Proposes a new SupLinUCB-type algorithm for contextual dueling bandits with variance-aware regret bound.
result Achieves a variance-aware regret bound of i l d e O ( d ∑ t = 1 T σ t 2 + d ) ilde O\big(d\sqrt{\sum_{t=1}^Tσ_t^2} + d\big) i l d e O ( d ∑ t = 1 T σ t 2 + d ) . Enhanced portfolio selection using sentiment data and LSTM.
problem Improving portfolio selection through sentiment analysis and price prediction.
method Semantic Attention Model for sentiment prediction, LSTM for price prediction, mean-variance strategy for portfolio optimization.
result Sentiment-aware portfolio strategies outperform non-sentiment aware models on average.
Proposes a new framework for investing that adapts to market regimes.
problem Adapting to dynamic market regimes for better investment performance.
method Wasserstein Hidden Markov Model (HMM) with transaction-cost-aware optimization.
result Significantly higher risk-adjusted performance compared to benchmarks.
Heuristic algorithm for portfolio optimization reduces solve times to milliseconds.
problem Mean-variance portfolio optimization with various constraints.
method Alternating Direction Method of Multipliers (ADMM).
result Achieves performance bounds and solves problems in milliseconds.
A new game-theoretic approach balances downside risk with expected reward.
problem Traditional game theory views risk only from the upside perspective, ignoring downside risk.
method Introduces downside risk aware equilibria (DRAE) based on lower partial moments.
result Successfully finds equilibria that balance downside risk with expected reward.
Despite the simplicity and intuitive interpretation of Minimum Mean Squared Error (MMSE) estimators, their effectiveness in certain scenarios is questionable. Indeed, minimizing squared errors on average does not provide any form of stability, as the volatility of the estimation error is left unconstrained. When this v…
Paper introduces geometry-aware normalizing flows for improved causal inference.
problem Disparity between sample and population distributions in causal inference.
method Integrates continuous normalizing flows with parametric submodels, employing Wasserstein gradient flows and optimal transport.
result Significantly reduces parameter estimation bias and variance in finite-sample settings.
New algorithm reduces MDP regret by accounting for state suboptimality gaps and variance.
problem Reducing regret in episodic MDPs with varying state suboptimality gaps.
method Introduced MVP algorithm with variance-aware gap-dependent regret bound.
result Achieved a variance-aware gap-dependent regret bound for MDPs.
RegimeFolio optimizes portfolios by adapting to changing market regimes.
problem Non-stationary markets with shifting volatility regimes.
method Explicitly models volatility regimes with sector-specific ensemble forecasting and adaptive mean-variance allocation.
result Significant improvement in return and robustness compared to conventional methods.
We propose and investigate new complementary methodologies for estimating predictive variance networks in regression neural networks. We derive a locally aware mini-batching scheme that result in sparse robust gradients, and show how to make unbiased weight updates to a variance network. Further, we formulate a heurist…
QTAML models quantum tunneling errors for AI robustness.
problem Quantum tunneling errors in AI inference.
method Derives weight-error distribution using WKB approximation, introduces TAC algorithm.
result TAC achieves 95% clean accuracy with 3.4-33.6x less ECC overhead.
We propose a novel visual context-aware filter generation module which incorporates contextual information present in images into Convolutional Neural Networks (CNNs). In contrast to traditional CNNs, we do not employ the same set of learned convolution filters for all input image instances. Our proposed input-conditio…
Proposes a novel graph self-training method with EM regularization for semi-supervised node classification.
problem Handles noisy graph structures and feature spaces in semi-supervised node classification.
method Introduces an Expectation-Maximization (EM) regularization scheme for uncertainty-aware pseudo-label generation and model retraining.
result Significantly outperforms strong baselines by up to 2.5% in accuracy.
Attention mechanism is effective in both focusing the deep learning models on relevant features and interpreting them. However, attentions may be unreliable since the networks that generate them are often trained in a weakly-supervised manner. To overcome this limitation, we introduce the notion of input-dependent unce…
Optimizes survey design for private mean estimation with reduced variance.
problem Minimizing variance in private mean estimation with privacy constraints.
method Formulates optimal survey design as an optimization problem, determining optimal subsampling sizes to minimize variance.
result Identifies the first privacy-aware stratified sampling scheme that minimizes variance under different privacy mechanisms.
CARV reduces compute cost for downstream pipelines using diffusion models.
problem High variance in Monte Carlo estimators from diffusion models limits compute efficiency.
method CARV uses hierarchical MC estimation with amortized upstream computation and stratified-inverse-CDF.
result CARV delivers 2-3x effective compute multipliers without changing the objective.
New algorithms reduce dueling bandits' regret with neural networks and efficient exploration.
problem Optimizing dueling bandits with neural networks for better performance.
method Combines shallow exploration strategies with neural networks for utility approximation, using iterative self-improvement and spectral analysis to reduce network width.
result Achieves sublinear regret of O ~ ( d ∑ t = 1 T σ t 2 + d T ) \widetilde{\mathcal{O}}(d\sqrt{\sum_{t=1}^{T} σ_t^2} + \sqrt{dT}) O ( d ∑ t = 1 T σ t 2 + d T ) . This work improves cost-aware Bayesian optimization by introducing Pareto-efficient acquisition functions.
problem Cost variability in hyperparameter evaluations affects the efficiency of Bayesian optimization.
method Reformulated cost-aware Bayesian optimization as Pareto efficiency, proposing a novel Pareto-efficient expected improvement.
result Pareto-efficient acquisition functions significantly outperform previous solutions, providing finer control over cost-accuracy trade-offs.
VA-LUCB identifies best arm with variance constraint, achieving optimal sample complexity.
problem Identifying the best arm with variance constraint under fixed confidence.
method Parameter-free algorithm VA-LUCB, analyzing sample complexity and proving lower bounds.
result Optimal sample complexity up to a logarithmic factor in H V A H_{VA} H V A , demonstrated by experiments. Unified framework for risk-aware policy learning in contextual bandits.
problem Optimizing decision rules in high-stakes domains with adverse outcomes.
method Distributional framework for Lipschitz-continuous risk functionals, with novel empirical concentration inequalities.
result Data-dependent suboptimality bounds with an i l d e O ( 1 / n ) ilde{\mathcal{O}}(1/\sqrt{n}) i l d e O ( 1/ n ) rate, matching risk-neutral offline policy optimization. Risk-aware linear bandits optimize against adverse outcomes.
problem Optimizing decisions under risk in sequential decision-making problems.
method Proposed an optimistic UCB algorithm for contextual bandits with convex loss minimization.
result Regret guarantees similar to generalized linear bandits with convex optimization at each round.
Two algorithms tackle heavy-tailed rewards in reinforcement learning with linear function approximation.
problem Online sequential decision-making with heavy-tailed rewards.
method AdaOFUL and VARA algorithms for linear stochastic bandits and MDPs, using modified adaptive Huber regression.
result Achieved state-of-the-art and variance-aware regret bounds for heavy-tailed rewards.
This paper extends the MAB problem to consider risk-reward tradeoffs.
problem Maximizing reward while accounting for risk in multi-armed bandit problems.
method Introduced the Risk Aware Lower Confidence Bound (RALCB) algorithm to solve the mean-variance MAB problem.
result The RALCB algorithm performs better than the algorithm in Sani et al. (2012) in both independent and dependent scenarios.
EVODiff optimizes DM inference by reducing conditional entropy, improving image generation.
problem Slow and inaccurate inference in diffusion models.
method Entropy-aware variance optimization for efficient inference.
result Significant improvement in image generation quality and efficiency.
RAVEN-UCB addresses non-stationary MAB problems with tighter regret bounds.
problem Non-stationary environments in multi-armed bandits.
method Combines variance-aware adaptation with three innovations: confidence bounds, adaptive control, and recursive updates.
result Achieves tighter regret bounds than UCB1 and UCB-V.
Efficient RL for linear MDPs with unknown transitions.
problem Long planning horizons and unknown state transitions in linear mixture MDPs.
method Horizon-free algorithm using weighted least squares with variance and uncertainty awareness.
result Achieves optimal regret up to logarithmic factors.
This paper improves uncertainty quantification in ELM models.
problem Uncertainty in ELM predictions due to data assumptions and randomness.
method Analytical derivations and variance estimates under various conditions.
result Improved understanding and estimation of ELM variability.
DGSAM improves domain generalization by minimizing individual sharpness.
problem Improving domain generalization models that perform well on unseen target domains.
method Shifts DG paradigm toward minimizing individual sharpness across source domains.
result DGSAM reduces performance variance across domains with less computational overhead.
Unified framework for causal inference with reliable uncertainty quantification.
problem Causal inference under unobserved confounding with unreliable uncertainty quantification.
method Deconditional Gaussian Process (DGP) framework for uncertainty-aware causal learning.
result Strong predictive performance and informative uncertainty quantification.
New methods incorporate alpha signals into portfolio construction, improving performance.
problem Signal-blindness in existing portfolio construction methods.
method Introduces three methods: HRP- μ \mu μ , HRP- Σ μ \Sigma\mu Σ μ , and CRISP. result CRISP at intermediate γ \gamma γ consistently outperforms other methods. New measure EC assesses node contributions in nonlinear, time-varying systems.
problem Existing node contribution measures assume linear, time-invariant dynamics, failing for complex, real-world systems.
method Defined 'emergent contribution (EC)' as a dynamical leverage measure from Jacobians of differentiable models.
result EC diverges from average controllability under persistent regime switching and sign reversal, identifying limits of local linearization.
Proposes ENVAR for causal discovery in structural VAR models with equal noise variance.
problem Challenges in causal discovery from multivariate time series with contemporaneous effects.
method Introduces observational equivalence and the observational alignment discrepancy for structural VAR models with equal noise variance.
result Shows that multiple structural VAR parameterizations can induce the same stationary observed process law.
PEMC uses ML to enhance Monte Carlo simulations, reducing variance and runtime.
problem Computational inefficiency in Monte Carlo simulations for complex tasks.
method Prediction-Enhanced Monte Carlo (PEMC) framework that uses ML surrogates as predictors.
result PEMC provides unbiased evaluations with reduced variance and runtime compared to standard Monte Carlo.
Sparse Principal Component Analysis (sPCA) is a popular matrix factorization approach based on Principal Component Analysis (PCA) that combines variance maximization and sparsity with the ultimate goal of improving data interpretation. When moving from PCA to sPCA, there are a number of implications that the practition…