Optimizes online learning with noisy gradient feedback for slowly changing minimizers.
problem Optimizing online learning performance with noisy gradient feedback for slowly changing minimizers.
method Introduces a path variation metric to analyze dynamic regret under true and noisy gradient feedback.
result Achieves optimal dynamic regret bounds under various feedback scenarios.
This letter improves sparse signal detection from one bit compressed sensing measurements.
problem Sparse signal detection from one bit compressed sensing measurements.
method Extended GLRT detector with optimal quantizer design and a double-detector scheme.
result The double-detector scheme outperforms existing methods in detection performance.
Study online learning with delays and capacity constraints, achieving optimal regret bounds.
problem Online learning with delays and capacity constraints.
method Novel scheduling and preemptive techniques, matching upper and lower bounds.
result Achieves optimal regret bounds across all capacity levels.
Dynamic assortment problem on two-sided platform with unknown parameters
problem Optimizing assortment display in an online platform with incomplete information and heterogeneous customers
method Data-driven algorithm that learns choice parameters while optimizing revenue
result Worst-case regret grows polylogarithmically over time
Capacity-Constrained Online Convex Optimization with Delayed Feedback
problem Online learning with delayed feedback under a hard capacity constraint
method Reduction to a delayed and weighted OCO problem using a scheduler
result First regret guarantees for capacity-constrained OCO under convex and strongly convex losses
A new pricing strategy minimizes regret by controlling strategic buyer behavior.
problem Designing a pricing policy for strategic buyers with limited seller information.
method Phased-structure policy with randomized isolation periods.
result Regret of T T T -period O ~ ( T ) \widetilde{\mathcal{O}}(\sqrt{T}) O ( T ) against a benchmark policy. Paper tackles online label shift in real-world applications.
problem Adapting to changing label distributions in online learning.
method Formulated an unbiased risk estimator and proposed online ensemble algorithms.
result Achieved optimal dynamic regret, indicating adaptability to label shift.
Dynamic pricing learns demand model from sparse product networks.
problem Minimizing revenue loss in a large network of products with unknown demand parameters.
method Combines optimism-in-the-face-of-uncertainty and PAC-Bayesian approaches.
result Achieves asymptotically optimal performance in terms of network size and time horizon.
Firm optimizes pricing for many products with varying features and customer choices.
problem Optimizing pricing for a large number of products with varying features and customer choices.
method Proposes a dynamic pricing policy, Regularized Maximum Likelihood Pricing (RMLP), leveraging the sparsity of the high-dimensional model.
result Achieves logarithmic regret in T T T for minimizing revenue loss against a clairvoyant policy. A new pricing strategy maximizes revenue in high-dimensional product spaces with varying customer preferences.
problem Maximizing revenue in a high-dimensional product space with heterogeneous price sensitivity.
method Proposes M3P, a pricing policy that achieves a specific regret bound under heterogeneous price sensitivity.
result Achieves a T T T -period regret of O ( log ( T d ) ( T + d log ( T ) ) ) O(\log(Td) (\sqrt{T} + d\log(T))) O ( log ( T d ) ( T + d log ( T ))) . Ad exchanges use CORP to set reserve prices against strategic buyers.
problem Setting optimal reserve prices in ad exchanges with strategic buyers.
method Proposes CORP policy to learn and set reserve prices robustly.
result Achieves sublinear regret in unknown noise distribution.
Paper proposes OPF policy for fair resource allocation with sublinear regret.
problem Fair resource allocation in an online setting against an unrestricted adversary.
method Online Proportional Fair (OPF) policy achieving approximate sublinear regret.
result OPF policy achieves c α c_α c α -approximate sublinear regret with c α ≤ 1.445 c_α \leq 1.445 c α ≤ 1.445 . New algorithm CROP achieves asymptotic optimality with bounded regret.
problem Optimistic algorithms fail to achieve asymptotic instance-dependent regret optimality.
method CRush Optimism with Pessimism (CROP) algorithm that eliminates optimistic hypotheses.
result CROP achieves constant-factor asymptotic optimality and bounded regret.
Firm optimizes prices for products with varying feature values to maximize revenue.
problem Maximizing revenue from products with changing feature values and unknown parameters.
method Projected Stochastic Gradient Descent (PSGD) for dynamic pricing.
result Regret bounds for PSGD pricing policy in two settings: antagonistic and stochastic feature models.
Broker uses multi-task dynamic pricing to learn competitive prices in credit markets.
problem Lack of data and infrequent trading in credit markets.
method Two-Stage Multi-Task (TSMT) algorithm that leverages shared structure across securities.
result TSMT algorithm achieves a regret bound of O ( T M d + M d ) O(\sqrt{T M d} + M d) O ( T M d + M d ) , outperforming baselines.