We consider an online learning process to forecast a sequence of outcomes for nonconvex models. A typical measure to evaluate online learning algorithms is regret but such standard definition of regret is intractable for nonconvex models even in offline settings. Hence, gradient based definition of regrets are common f…
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48 results for “local regret”
We introduce a new local regret framework for non-convex models in dynamic environments.
problem Challenges in online forecasting for non-convex models with frequent updates and concept drift.
method We propose a novel local regret framework and a time-smoothed gradient update rule.
result Our approach yields more stable, robust, and computationally efficient forecasting compared to state-of-the-art methods.
This paper achieves optimal regret bounds for locally private linear contextual bandit.
problem Designing locally private linear contextual bandit algorithms with optimal regret bounds.
method New algorithmic and analytical ideas, including mean absolute deviation analysis and layered principal component regression.
result Achieves an regret upper bound for locally private linear contextual bandit.
Greedy algorithm achieves sublinear regret for various distributions.
problem Efficient performance of greedy algorithms in linear contextual bandit problems.
method Introduced Local Anti-Concentration (LAC) condition to ensure sublinear regret.
result Greedy algorithm achieves cumulative expected regret.
We consider regret minimization in repeated games with non-convex loss functions. Minimizing the standard notion of regret is computationally intractable. Thus, we define a natural notion of regret which permits efficient optimization and generalizes offline guarantees for convergence to an approximate local optimum. W…
New algorithms reduce matching market regret to log(T) with improved stability.
problem Minimizing regret in two-sided matching markets with bandit feedback.
method Phase-based algorithm with local arm deletion to improve stability.
result Achieves Θ(log(T)) regret for markets with uniqueness consistency.
Paper extends meta-learning framework to non-convex settings with improved performance.
problem Learning from past tasks for faster future tasks in a sequential setting.
method Generalized online meta-learning framework to non-convex settings, introduced local regret as performance measure.
result The framework achieves logarithmic local regret and robustness to hyperparameter initialization.
The paper studies MAB problems with LDP to balance privacy and service quality.
problem Balancing privacy and service quality in multi-armed bandit systems.
method Investigates regret minimization for MAB with LDP guarantee, proving lower bounds and proposing matching upper bounds algorithms.
result Regret upper bounds match lower bounds up to constant factors for MAB algorithms with LDP guarantee.
Paper proposes algorithms to minimize both dynamic and adaptive regret simultaneously.
problem Traditional regret minimization algorithms are suboptimal for changing environments.
method Developed novel online algorithms to minimize dynamic and adaptive regret simultaneously.
result Proposed algorithms minimize dynamic and adaptive regret over any interval.
The paper sets bounds on how much regret is unavoidable in adaptive LQR with unknown B-matrix.
problem Understanding the limits of adaptive LQR with unknown B-matrix.
method Local asymptotic minimax regret lower bounds using van Trees' inequality and Bellman error representation.
result Logarithmic regret is impossible if the parametrization induces an uninformative optimal policy.
This guide simplifies high-probability regret bounds in empirical risk minimization.
problem High-probability regret bounds in empirical risk minimization.
method Modular presentation, three-step recipe, localized Rademacher complexity, local maximal inequalities, metric-entropy integrals.
result Recover familiar rates for various function classes and derive regret bounds for nuisance components.
Unified analysis of kernel-based and locally adaptive bandit optimization methods.
problem Performance of bandit optimization algorithms in RKHS functions.
method Investigates the relationship between kernel regularity and algorithmic performance, characterizing spectral properties of various kernels.
result Unified framework for analyzing kernel-based and locally adaptive bandit algorithms, deriving explicit regret bounds.
Regret minimization is a powerful tool for solving large-scale problems; it was recently used in breakthrough results for large-scale extensive-form game solving. This was achieved by composing simplex regret minimizers into an overall regret-minimization framework for extensive-form game strategy spaces. In this paper…
We take initial steps in studying PAC-MDP algorithms with limited adaptivity, that is, algorithms that change its exploration policy as infrequently as possible during regret minimization. This is motivated by the difficulty of running fully adaptive algorithms in real-world applications (such as medical domains), and …
A new algorithm reduces frequentist regret in multi-agent bandit problems with sparse hypergraphs.
problem Deriving a frequentist regret bound for Thompson sampling in multi-agent settings with sparse hypergraphs.
method Proposed -exploring Multi-Agent Thompson Sampling (-MATS) algorithm that combines exploration and exploitation strategies.
result Achieves a worst-case frequentist regret bound sublinear in time horizon and local arm size, optimal up to constants and logarithms for sparse hypergraphs.
Improved guarantees for misspecified kernelized bandit optimization.
problem Misspecification in kernelized bandit optimization.
method Localization and domain splitting techniques.
result Logarithmic or polylogarithmic growth of misspecification amplification.
The paper tackles personalized policy learning from diverse data sources in a federated setting.
problem Learning personalized decision policies from observational bandit feedback across multiple heterogeneous data sources.
method Introduces a novel regret analysis for distinguishing global and local regret, and presents a federated policy learning algorithm using local policies trained with doubly robust offline policy evaluation strategies.
result Establishes finite-sample upper bounds on global and local regret, characterizing them by source heterogeneity and distribution shift.
New algorithms adaptively compete against complex environments with local regularities.
problem Efficiently competing against complex, locally regular comparator functions in nonparametric settings.
method Locally-adaptive online algorithms using hierarchical -nets and tree experts.
result Proved regret bounds scaling with different types of local regularities, delivering better performance for simple profiles.
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.
We study a variant of the stochastic multi-armed bandit (MAB) problem in which the rewards are corrupted. In this framework, motivated by privacy preservation in online recommender systems, the goal is to maximize the sum of the (unobserved) rewards, based on the observation of transformation of these rewards through a…
This paper considers distributed online optimization with time-varying coupled inequality constraints. The global objective function is composed of local convex cost and regularization functions and the coupled constraint function is the sum of local convex functions. A distributed online primal-dual dynamic mirror des…
Locally private algorithm improves online federated learning with correlated noise.
problem Privacy-preserving online federated learning with non-IID data.
method Locally differentially private algorithm using temporally correlated noise.
result Established dynamic regret bound for nonconvex loss functions.
New bounds for online portfolio selection without smoothness assumptions.
problem Online portfolio selection with non-Lipschitz, non-smooth losses.
method Data-dependent bounds using novel smoothness characterizations and FTRL with self-concordant regularizers.
result Achieves logarithmic regrets when data is 'easy' and sublinear worst-case regrets.
Paper analyzes nonconvex bandit problems with improved adaptive methods.
problem Continuous armed bandit problems for nonconvex cost functions.
method Simple and adaptive bin splitting methods.
result Adaptive method achieves locally minimax optimal expected cumulative regret.
New algorithm reduces control error in systems with changing dynamics.
problem Online control of systems with time-varying linear dynamics.
method Introduces adaptive regret metric and a novel meta-algorithm.
result First adaptive regret bound for online convex optimization with memory.
We provide a simple and efficient algorithm for adversarial -action -outcome non-degenerate locally observable partial monitoring game for which the -round minimax regret is bounded by , matching the best known information-theoretic upper bound. The same algorithm also achieves…
New algorithm bounds MAB regret for unknown scale and magnitude of losses.
problem Adversarial Multi Armed Bandits with unknown scale and magnitude of losses.
method Design a bandit Follow The Regularized Leader (FTRL) algorithm with adaptive learning rate.
result First MAB bounds that adapt to and norms of losses.
Paper tackles LDP bandits learning with improved results and sub-linear regret.
problem Contextual bandits learning with LDP privacy constraints.
method Simple black-box reduction frameworks for context-free bandits, extended to GLB.
result First result for BCO with multi-point feedback under LDP, sub-linear regret for GLB.
Study local exploration on dynamic graphs with time-varying edges.
problem Learning optimal actions in a network with changing connections.
method Local explore-then-commit algorithms under a structural condition ensuring intrinsic walk stability.
result Sublinear expected regret for reward-aware strategies.
A new pricing controller handles resource constraints to infer target prices effectively.
problem Resource constraints prevent fixed-price inference, leading to support exclusion.
method Formalizes support-exclusion failure, designs a target-aware controller, and uses a realized information clock.
result The controller can certify feasible target bands and log continuous local densities, leading to polynomial rates of inference.
We study a nonparametric contextual bandit problem where the expected reward functions belong to a Hölder class with smoothness parameter . We show how this interpolates between two extremes that were previously studied in isolation: non-differentiable bandits (), where rate-optimal regret is achieved by run…
Unified framework for combinatorial and rounding algorithms in experimental design.
problem Designing and analyzing combinatorial and rounding algorithms for experimental design problems.
method Local search framework for combinatorial algorithms and regret minimization framework for rounding algorithms.
result Unified approach to match and improve all known results in D/A/E-design and obtain new results in unknown settings.
Algorithm achieves optimal pricing with minimal exploration for dynamic markets.
problem Optimal pricing in dynamic markets with contextual information.
method Localized exploration-then-commit (LetC) algorithm with pure exploration, refinement, and exploitation stages.
result Achieves minimax optimal, dimension-free regret bound.
New Thompson sampling algorithm for stochastic partial monitoring achieves logarithmic regret.
problem Limited feedback in sequential learning problems.
method Developed a novel Thompson-sampling-based algorithm to sample from the posterior distribution exactly.
result Achieved logarithmic regret bound of O(log T) for a linearized variant of the problem.
UCB-Advantage learns MDPs with regret.
problem Model-free reinforcement learning in finite-horizon MDPs.
method Reference-Advantage decomposition for low regret.
result Achieves regret, matching best known bounds.
A collaborative algorithm reduces regret in federated linear contextual bandits.
problem Optimizing decision-making in federated learning with heterogeneous data.
method Fed-PE algorithm, leveraging geometric structure of rewards, multi-client G-optimal design.
result Achieves near-optimal regrets with logarithmic communication costs.
New algorithm reduces regret in multi-agent bandits over undirected graphs.
problem Minimize regret in a multi-agent bandit setting with malicious agents.
method Proposed a new algorithm for undirected graphs, considering the number of malicious neighbors.
result The new algorithm achieves nearly linear regret improvement over existing methods.
Paper addresses privacy in combinatorial semi-bandits with improved bounds.
problem Privacy-preserving learning in combinatorial semi-bandits with additional dimension dependence.
method Proposes novel algorithms and proves optimal regret bounds for LDP and DP settings.
result Achieves nearly optimal regret bounds for LDP and DP settings, matching non-private rates.
Dynamic regret minimization is shown equivalent to static regret minimization for linear losses.
problem Dynamic regret minimization in online convex optimization.
method Equivalence between dynamic and static regret minimization for linear losses.
result Dynamic regret minimization is equivalent to static regret minimization for linear losses.
A new algorithm LONR learns without terminal states or perfect recall.
problem Learning in settings without terminal states or perfect recall.
method Local No-Regret Learning (LONR) using Q-learning-like updates.
result LONR achieves last iterate convergence in challenging settings.
Order-Optimal Regret in Distributed Kernel Bandits using Uniform Sampling with Shared Randomnesscs.LG
Algorithm reduces regret in distributed kernel bandits with shared randomness.
problem Minimizing regret in collaborative function maximization.
method Uniform exploration at local agents and shared randomness with central server.
result Achieves optimal regret order with sublinear communication cost.
A Domain-Shrinking based Bayesian Optimization Algorithm with Order-Optimal Regret Performancestat.ML
Bayesian optimization algorithm reduces regret with efficient region pruning.
problem Sequential optimization of unknown functions in high-dimensional spaces.
method Gaussian process-based, domain shrinking through tree-based region pruning.
result Order-optimal regret performance with reduced computational complexity.
New algorithm reduces online learning regret by exploiting historical invariances.
problem Stochastic non-stationary linear bandits with changing reward models.
method ISD-linUCB algorithm that learns invariances in reward model.
result Significant regret improvements in fast-changing environments with historical data.
New method for online meta-learning reduces dynamic regret in changing environments.
problem Learning new tasks quickly from limited data in dynamic settings.
method Established dynamic regret analysis using generalized adaptive gradient methods.
result Logarithmic local dynamic regret with dependence on total iterations and learner parameters.
Agents collaborate to minimize regret while keeping costs under a threshold.
problem Collaborative multi-agent stochastic linear bandits with cost constraints.
method Safe distributed upper confidence bound algorithm (MA-OPLB) with accelerated consensus.
result Regret bound of order $ \mathcal{O}\left(\frac{d}{τ-c_0}\frac{\log(NT)^2}{\sqrt{N}}\sqrt{\frac{T}{\log(1/|λ_2|)}}
ight)$.
The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learningcs.LG
Introduces LoCA regret to evaluate model-based RL methods.
problem Lack of consistent metrics to evaluate model-based RL methods.
method Inspired by neuroscience, introduces LoCA regret to measure model-based behavior.
result LoCA regret can identify model-based behavior and assess how close methods are to optimal model-based behavior.
Paper proposes no-regret algorithms for private GP bandit optimization.
problem Private Gaussian process bandit optimization.
method Combines uniform kernel approximator with random perturbations for differentially private GP bandit algorithms.
result Provable no-regret algorithms for stationary kernel functions in two DP settings.
New algorithms reduce private bandit regret to nearly non-private levels.
problem Differentially private adversarial bandits and expert advice.
method Conversion of non-private algorithms to private, new algorithms for bandits and expert advice.
result Improved regret bounds for private bandits, sublinear for small ε.