Study improves self-normalized bounds for vector-valued processes beyond sub-Gaussianity.
problem Limited understanding of self-normalized concentration for vector-valued processes outside sub-Gaussian frameworks.
method Developed concentration inequalities for self-normalized processes with light tails (e.g., Bennett, Bernstein bounds) for vector-valued data.
result Provided new insights and bounds for self-normalized processes with non-sub-Gaussian distributions.
Calculation of the log-normalizer is a major computational obstacle in applications of log-linear models with large output spaces. The problem of fast normalizer computation has therefore attracted significant attention in the theoretical and applied machine learning literature. In this paper, we analyze a recently pro…
New bounds on self-normalized martingales improve online linear regression performance.
problem Improving regret bounds in online linear regression.
method Characterizing scale-invariant bounds on self-normalized martingales.
result For d = 1 d=1 d = 1 , O ( log T ) O(\log T) O ( log T ) doubly-uniform regret is possible; for d > 1 d>1 d > 1 , sublinear doubly-uniform regret is impossible. BR-SNIS reduces bias in self-normalized IS without increasing variance.
problem Bias in self-normalized IS.
method Iterated sampling-importance resampling (ISIR) to form a bias-reduced estimator.
result Significant reduction in bias without increasing variance.
Improved speech recognition with faster training and inference.
problem Training very deep CNNs for speech recognition is difficult.
method Proposed SNDCNN using SELU activations instead of RELU and shortcut connections/BN.
result Achieved similar or lower WER with faster training and inference.
The paper develops a method for self-normalized inference in adaptive experiments.
problem Adaptive experiments require a fixed horizon for ATE estimation, but propensities can change.
method The method uses self-normalized martingale limit theory to estimate ATE.
result The Studentized statistic is asymptotically N(0,1) at the prespecified horizon.
Tutorial on using concentration inequalities for linear system identification.
problem Learning state-space parameters of linear systems.
method Large-deviations and self-normalized martingales.
result Data-dependent and independent bounds on learning rate.
A new linear contextual bandit algorithm with improved regret bound.
problem Efficiently solving linear contextual bandit problems with reduced regret.
method Proposes a novel estimator embedded with exploration and a self-normalized bound.
result Regret bound matches lower bound of Ω ( d T ) Ω(\sqrt{dT}) Ω ( d T ) up to logarithmic factors. We improve bounds for stochastic processes, especially those with heavy tails.
problem Bounding the concentration of sub- ψ ψ ψ processes with heavy tails. method Variational approach to concentration, focusing on sub-Gaussian and other tail conditions.
result First dimension-free self-normalized empirical Bernstein inequality.
Self Normalizing Flows improve normalizing flows by reducing computational complexity.
problem Efficient gradient computation in normalizing flows, especially in Jacobian determinant terms.
method Introducing Self Normalizing Flows that replace expensive terms with learned approximate inverses.
result Models can be trained more quickly and perform better than functionally constrained counterparts.
A new method for evaluating and selecting policies in contextual bandits improves confidence intervals and policy quality.
problem Evaluating and selecting policies in contextual bandits with logged data.
method Self-normalized Importance Weighting (SN) estimator with Efron-Stein tail inequality and multiplicative bias control.
result The method provides tighter confidence intervals and better policy selection compared to competitors.
New activation function SERLU improves neural network performance.
problem Improving neural network performance and avoiding overfitting.
method Introducing a new activation function (SERLU) that breaks monotonicity while preserving self-normalizing property and developing shift-dropout for regularization.
result SERLU-based neural networks provide consistently promising results compared to other activation functions.
We study the regret minimization problem in the novel setting of generalized kernelized bandits (GKBs), where we optimize an unknown function f ∗ f^* f ∗ belonging to a reproducing kernel Hilbert space (RKHS) having access to samples generated by an exponential family (EF) reward model whose mean is a non-linear function $μ(…
Deep Learning has revolutionized vision via convolutional neural networks (CNNs) and natural language processing via recurrent neural networks (RNNs). However, success stories of Deep Learning with standard feed-forward neural networks (FNNs) are rare. FNNs that perform well are typically shallow and, therefore cannot …
Study tests adequacy of FARIMA models with uncorrelated but non-independent errors.
problem Testing adequacy of FARIMA models with specific error characteristics.
method Derive asymptotic distributions of residual autocovariances and autocorrelations, propose self-normalization approach.
result Asymptotic distributions of modified portmanteau statistics for weak FARIMA models.
The paper analyzes system identification with finite data.
problem Recovering system parameters and Kalman filter gain from noisy output measurements.
method Subspace identification algorithm, finite number of output samples, random matrix theory, self-normalized martingales, SVD robustness.
result Estimation errors decrease with a rate of 1/\sqrt{N}, valid even for marginally stable systems.
New algorithm reduces regret for logistic bandits without κ κ κ dependency.
problem Logistic bandits have poor frequentist regret guarantees due to large κ κ κ . method Optimistic algorithm based on self-normalized martingale tail-inequality.
result Achieves i l d e O ( T ) ilde{\mathcal{O}}(\sqrt{T}) i l d e O ( T ) regret with no κ κ κ dependency. New method improves feature importance assessment in random forests.
problem Improving feature importance measures for random forests.
method Hypothesis testing via self-normalized feature-residual correlation test (FACT).
result The method provides theoretically justified feature importance tests with controlled type I error and appealing power.
AMCI improves Monte Carlo integration by amortizing over both datasets and target functions.
problem Inefficiency in approximating expectations for known target functions using current approaches.
method Introduces AMCI, a method for amortizing Monte Carlo integration directly, producing three distinct amortized proposals.
result AMCI can theoretically produce arbitrarily small errors for any integrable target function using only a single sample from each proposal at runtime.
Paper controls false positives in high-dimensional models using a novel approach.
problem Controlling false positives in high-dimensional models with the Lasso.
method Recast SQRT-Lasso as a false positive control method, extend to all GLMs, use fast Lasso solvers.
result Shows novel false positive control using random weighted self-normalized sums in finite samples.
New algorithm reduces reinforcement learning regret for linear MDPs with unknown transitions.
problem Adversarial linear mixture MDPs with bandit feedback and unknown transition.
method Proposes a new algorithm with a least square estimator and self-normalized concentration.
result Achieves improved regret bound with high probability.
Finite-time queue peaks in stochastic networks have logarithmic scaling after geometric thresholds.
problem Queue peak laws in stochastic networks with geometric thresholds.
method Self-normalization mechanism
result Logarithmic scaling of queue peaks after geometric thresholds.
This paper develops dimension-agnostic inference methods for high-dimensional data.
problem Understanding how classical inference methods behave in high-dimensional settings.
method Using variational representations, sample splitting, and self-normalization to create a refined test statistic.
result The resulting statistic has a Gaussian limiting distribution regardless of how dimensionality scales with sample size.
Proposes SQUAD for better predictive uncertainty in deep latent models.
problem Intractable inference in deep latent variable models lead to overconfident predictions.
method Introduces Stochastic Quantized Activation Distributions (SQUAD) for flexible yet tractable latent variable distributions.
result The model provides competitive quality predictive uncertainty and learns non-linearities.
New framework for evaluating ad auctions using stochastic modeling.
problem Challenges in evaluating deterministic ad auctions.
method Repurposed bid landscape model to approximate propensity scores, enabling robust OPE estimators.
result Remarkable alignment with online A/B test results, achieving 92% MDA in CTR prediction.
The paper proposes a method for constructing confidence sets that adapt to the cardinality of the smallest component of a mean vector.
problem Forming confidence sets for the smallest component of an unknown mean vector.
method Sample splitting and self-normalization approach to test each component for being the smallest, maintaining validity regardless of d d d and n n n . result The proposed tests achieve the local minimax separation rate and robust to heavy-tailed distributions.
A tutorial on non-asymptotic system identification methods.
problem Identifying system parameters in linear models.
method Covering technique, Hanson-Wright Inequality, method of self-normalized martingales.
result Streamlined proofs of least-squares based estimator performance.
Refines online learning to rank algorithm with tighter bounds.
problem Online learning to rank in machine learning.
method Utilized method of mixtures and asymptotic expansions to refine inequalities.
result Improved algorithm and performance estimation.
This paper studies semiparametric contextual bandits, a generalization of the linear stochastic bandit problem where the reward for an action is modeled as a linear function of known action features confounded by an non-linear action-independent term. We design new algorithms that achieve O ~ ( d T ) \tilde{O}(d\sqrt{T}) O ~ ( d T ) regret …
A new aggregation strategy improves GNN performance and learning dynamics.
problem Improving expressivity and learning dynamics of GNNs.
method Proposes a variance-preserving aggregation function (VPA) for GNNs.
result VPA leads to increased predictive performance and improved learning dynamics.
The study reveals the efficiency of sampling from tilted distributions.
problem Sampling from a tilted distribution of an unknown underlying distribution.
method Self-normalized importance sampling to characterize accuracy.
result Polynomial vs super-polynomial sample complexity for bounded vs unbounded distributions.
Cost-aware SBI reduces expensive simulations in complex models.
problem High computational cost in simulating complex models.
method Combination of rejection and self-normalised importance sampling.
result Significant reduction in overall cost of inference.
New inequalities for matrix supermartingales converge under various conditions.
problem Convergence and maximal inequalities of supermartingales in positive semidefinite matrices.
method Developed new concentration inequalities for matrix supermartingales.
result New inequalities for matrix supermartingales under different tail conditions.
We develop a probabilistic framework for sequential random projection.
problem Challenges of sequential decision-making under uncertainty.
method Novel construction of a stopped process and method of mixtures.
result Achieved a non-asymptotic probability bound for random projection.
Quantile regression is an increasingly important empirical tool in economics and other sciences for analyzing the impact of a set of regressors on the conditional distribution of an outcome. Extremal quantile regression, or quantile regression applied to the tails, is of interest in many economic and financial applicat…
Learning the minimum/maximum mean among a finite set of distributions is a fundamental sub-task in planning, game tree search and reinforcement learning. We formalize this learning task as the problem of sequentially testing how the minimum mean among a finite set of distributions compares to a given threshold. We deve…
Bayesian optimization tackles uncertainty in context variables.
problem Sequential decision-making under context distributional uncertainty.
method Wasserstein Distributionally Robust Bayesian Optimization.
result Sublinear regret bounds matching state-of-the-art results.
New neural network approach mitigates vanishing/exploding gradients.
problem Vanishing and exploding gradients in neural networks.
method Gaussian-Poincaré normalized functions and orthogonal weight matrices.
result High-dimensional probability theory shows gradients disappear with high probability in wide neural networks.
New estimators improve OPE efficiency and stability.
problem Evaluate novel policies without exploration in RL.
method Empirical likelihood estimators that are more efficient and stable than existing methods.
result New estimators are more efficient and stable than IS, SNIS, and DR.
Paper proposes a federated learning method for quantile inference with local differential privacy.
problem Federated learning of quantile inference under local differential privacy constraints.
method Local stochastic gradient descent with randomized mechanism for privacy and efficiency.
result Asymptotic normality and functional central limit theorem for the proposed estimator.
Wavelet-based online learning adapts to noisy Besov spaces with high probability.
problem Minimizing integrated squared error in Besov spaces with noisy observations.
method Adaptive wavelet-based online learning algorithm that dynamically adjusts to gradient noise.
result Achieves minimax-optimal integrated squared error with high probability.
Improved sampling efficiency for inverse problems using variance-reduced diffusion methods.
problem Efficiently estimating noisy scores in inverse problems.
method Developed a nonparametric self-normalized importance sampling estimator and a state-dependent blending rule.
result Improved sample quality for fixed simulation budgets in synthetic targets and PDE-governed inverse problems.
New algorithm reduces best-in-class regret in contextual bandits.
problem Compete with the best policy in a class without model restrictions.
method Proposes an algorithm that updates policies by minimizing a pessimistic objective, including a clipped inverse-propensity estimate and variance penalty.
result Achieves fast best-in-class regret rates, including polylogarithmic rates in the parametric case.
New models learn from samplers to approximate EBMs.
problem Intractable sampling and density evaluation in EBMs.
method Maximize likelihood of sampler-induced distribution.
result EIMs provide exact samples and tractable log-likelihood.
New algorithms reduce regret in neural logistic bandits.
problem Learning unknown reward functions in neural networks.
method Introduced a Bernstein-type inequality for self-normalized vector-valued martingales.
result Regret bounds improved to O ~ ( d ~ κ T ) \widetilde{O}(\widetilde{d}\sqrt{κT}) O ( d κ T ) and O ~ ( d ~ T / κ ) \widetilde{O}(\widetilde{d}\sqrt{T/κ}) O ( d T / κ ) . New summary measures reveal geometric structure in weighted measures on manifolds.
problem Lack of geometric information in standard weight-only summaries.
method Heat-kernel entropy profiles, tracking nonuniformity across scales.
result Geometric effective sample size discounts nearby or duplicate particles.
New auto-encoder handles varying noise levels without retraining.
problem Auto-encoders degrade in noisy conditions.
method Formalized auto-encoders as transform learning, derived new architecture.
result Models generalize well to different noise levels.
This paper achieves first-order regret bounds in reinforcement learning with large state spaces.
problem Achieving first-order regret bounds in reinforcement learning with large state spaces.
method Developed a novel robust self-normalized concentration bound based on the robust Catoni mean estimator.
result Obtained regret bounds scaling as O ~ ( d 3 H 3 ⋅ V 1 ⋆ ⋅ K + d 3.5 H 3 log K ) \widetilde{\mathcal{O}}(\sqrt{d^3 H^3 \cdot V_1^\star \cdot K} + d^{3.5}H^3\log K ) O ( d 3 H 3 ⋅ V 1 ⋆ ⋅ K + d 3.5 H 3 log K ) .