Enhances optimization algorithms to achieve faster convergence rates.
problem Minimizing smooth convex functions efficiently.
method Extends optimistic learning to achieve faster convergence rates.
result Achieves a rate of for optimization.
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48 results for “no-regret dynamics”
The paper tackles cooperative RL with function approximation, achieving near-optimal learning with limited communication.
problem Cooperative multi-agent reinforcement learning with function approximation.
method Careful message-passing and cooperative value iteration.
result Achieving near-optimal no-regret learning with limited communication in cooperative multi-agent settings.
No-regret optimization for time-varying functions using uncertainty injection.
problem Optimizing time-varying functions with no-regret in bandit feedback.
method W-SparQ-GP-UCB, incorporating uncertainty injection and additional queries.
result Achieves no-regret with a vanishing number of additional queries per iteration.
No-regret learning with strategic experts, incentivized.
problem Online learning with strategic experts who misreport beliefs.
method Building on wagering mechanisms, we provide algorithms for no-regret and incentive compatibility in both full and partial information settings.
result Our algorithms achieve no regret and incentive compatibility for myopic experts, with comparable regret to classic no-regret algorithms and diminishing regret for forward-looking agents.
This paper proposes a new portfolio allocation method using LLMs to outperform traditional strategies.
problem Persistent tradeoff between risk and return in portfolio management.
method Follow-the-leader approach with sentiment-based trade filtering and LLM-driven hedging.
result Empirical results show a 69% increase in annualized returns and 119% in Sharpe ratio compared to SPY buy-and-hold.
No-regret learning fails to converge to Nash equilibria in mixed strategies.
problem Limiting behavior of mixed strategies in repeated games.
method Study of optimal no-regret learning algorithms for 2x2 competitive games.
result Limiting mixed strategies cannot converge to Nash equilibria under mean-based and monotonic updates.
A new Python-C++ framework for agent-based simulation.
problem Understanding market dynamics and effects of delays.
method User-friendly Python API with efficient C++ implementation, message-driven architecture.
result Investigated the role of order processing delay in financial markets.
New insights link no-regret learning to online conformal prediction in adversarial settings.
problem Understanding the relationship between no-regret learning and online conformal prediction in adversarial environments.
method Analysis of existing algorithms and new connections between no-regret learning and conformal prediction.
result No-regret learning algorithms can provide group-conditional coverage guarantees in adversarial settings.
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.
Algorithm learns from changing zero-sum games with no regret.
problem Learning in time-varying zero-sum games.
method Developed a single parameter-free algorithm with three performance measures.
result Algorithm recovers best known results for fixed games and adapts to non-stationarity.
UC-SSP algorithm tackles exploration in stochastic shortest path problems without loop-free assumption.
problem Exploration in goal-oriented reinforcement learning problems under stochastic shortest path formulation.
method UC-SSP algorithm with a novel stopping rule to interrupt and switch policies.
result Regret bound of after episodes.
New framework uses PDE for no-regret generative modeling.
problem Developing efficient generative models for complex distributions.
method Iterative refinement of Brenier maps using mirror gradient descent.
result Converges to optimal Brenier map under various step-size schedules.
No-regret BO algorithm adapts hyperparameters to optimize unknown functions.
problem Misspecification of hyperparameters in BO leads to poor local optima.
method Adapts hyperparameters online to expand function class and converge to optimum.
result First provably no-regret BO algorithm with unknown hyperparameters.
No-regret algorithm for contextual RL with GLM mappings.
problem Learning near-optimal policies in episodic MDPs with contextual information.
method Proposes no-regret online RL algorithm using optimistic and randomized exploration methods.
result Improves previous bounds and provides a lower bound for the setting.
Paper explores rate-preserving reductions between Blackwell approachability and no-regret learning.
problem Tackles rate-preserving reductions between Blackwell approachability and no-regret learning.
method Studies fine-grained reductions and optimal rates of convergence.
result Shows that rate-preserving reductions do not always hold, but provides conditions for when they do.
Kernel-based function approximation improves reinforcement learning performance.
problem Average reward reinforcement learning in infinite horizon settings.
method Optimistic algorithm based on kernel ridge regression.
result No-regret performance guarantees and confidence intervals for kernel-based predictions.
New research shows no-regret learning is impossible in Markov games under certain assumptions.
problem Achieving no-regret learning in decentralized Markov games.
method Novel application of aggregation techniques from online learning to prove lower bounds.
result No polynomial-time algorithm exists for independent no-regret learning in general-sum Markov games.
Recent literature on online learning has focused on developing adaptive algorithms that take advantage of a regularity of the sequence of observations, yet retain worst-case performance guarantees. A complementary direction is to develop prediction methods that perform well against complex benchmarks. In this paper, we…
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.
Faster rates achieved for computing equilibria in convex-concave games.
problem Computing equilibria in convex-concave games efficiently.
method Optimistic prediction algorithms and no-regret techniques.
result Achieved rate for equilibrium computation.
Sequential prediction problems such as imitation learning, where future observations depend on previous predictions (actions), violate the common i.i.d. assumptions made in statistical learning. This leads to poor performance in theory and often in practice. Some recent approaches provide stronger guarantees in this se…
Online algorithms stabilize in feedback loops of performative prediction.
problem Feedback loops in algorithmic predictions influence data distributions.
method Martingale argument and randomization to avoid distributional assumptions.
result No-regret algorithms converge to performatively stable equilibria.
Paper analyzes GP-EI for Bayesian optimization with no regret and provides guidance on choosing incumbents.
problem Analyzing cumulative regret of GP-EI with different incumbents in noisy Bayesian optimization.
method Analyzes GP-EI with three incumbents (BPMI, BSPMI, BOI) in both SE and Matérn kernels, proving no-regret for BPMI and BSPMI.
result GP-EI with BPMI and BSPMI is a no-regret algorithm for both SE and Matérn kernels, providing theoretical guidance for choosing incumbents.
New algorithm for traffic routing in congested conditions.
problem Optimal routing in congested traffic networks.
method Congested Bandits model, UCB algorithm, iterative least squares planner.
result No-regret algorithms for congestion-aware routing.
New algorithms achieve no-regret learning even with adversarial transitions and losses.
problem No-regret learning impossible with adversarial transitions and losses.
method Developed algorithms for adversarial Markov Decision Processes with smooth regret increase.
result Achieved regret, with measuring adversarial transition function.
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 -approximate sublinear regret with .
Decentralized Optimistic Hyperpolicy Mirror Descent: Provably No-Regret Learning in Markov Gamescs.LG
DORIS algorithm achieves no-regret learning in Markov games with adversarial opponents.
problem Decentralized policy learning in Markov games with nonstationary opponents.
method DORIS algorithm using optimistic hyperpolicy mirror descent.
result Achieves -regret in general function approximation.
A method for safe online classification reduces test costs while maintaining low error rates.
problem Sequential testing for binary disease outcomes with unknown logistic model parameters.
method Joint estimation of logistic parameter and feature distribution with a conservative threshold.
result Achieves target error with high probability and requires minimal excess tests.
Robustly combines supervised and bandit feedback for contextual bandits.
problem Learning from mixed supervised and bandit data with potentially misaligned costs.
method Developed no-regret algorithms robust to misaligned cost distributions.
result Our approach is feasible and helpful in practice, as shown by empirical evaluations.
Online optimization algorithms help in making decisions with limited data.
problem Making decisions in real-time with limited data.
method Online convex optimization and no-regret learning algorithms.
result Asymptotically optimal performance in hindsight.
R2-B2 optimizes game interactions with recursive reasoning.
problem Optimizing interactions between boundedly rational agents with unknown payoff functions.
method Recursive Reasoning-Based Bayesian Optimization (R2-B2) for repeated games.
result R2-B2 achieves faster asymptotic convergence to no regret than non-recursive methods.
OMWU shows last iterate convergence in convex-concave games.
problem Optimizing in constrained min-max optimization landscapes.
method OMWU (Optimistic Multiplicative-Weights Update) in the no-regret online learning framework.
result OMWU exhibits last iterate convergence for convex-concave games, generalizing previous results.
Beam search policies learned via imitation learning.
problem Beam search policies are not explicitly learned by models during training.
method Developed a meta-algorithm for learning beam search policies using imitation learning.
result Showed no-regret guarantees for learning beam search policies.
Paper establishes no-regret property for practical EGO optimization.
problem No theoretical bounds on cumulative regret for practical EGO.
method Introduced practical EGO with a positive nugget, analyzed its regret bounds.
result Practical EGO is a no-regret algorithm with sublinear regret bounds.
New approach considers a buyer with no-regret learning to optimize seller's revenue.
problem Optimizing revenue for a seller selling to a buyer with no-regret learning.
method Analyzes different learning algorithms for the buyer and corresponding optimal auctions for the seller.
result Seller can achieve optimal revenue by setting decreasing reserves over time, surpassing truthful auctions.
No-regret incentive-compatible online learning under exact truthfulness with non-myopic expertscs.LG
A new mechanism reduces expert belief regret in online forecasting.
problem Minimizing expert belief regret in strategic forecasting.
method Developed a no-regret mechanism for non-myopic experts using online I-ELF.
result Achieved regret for full-information setting.
This paper tackles no-regret learning for fair multi-agent social welfare optimization.
problem Maximizing social welfare in a fair manner for multiple agents.
method Developed algorithms for stochastic and adversarial multi-agent settings, proving regret bounds and tightness.
result Achieved no-regret learning for fair multi-agent social welfare optimization in various settings.
Recent work has demonstrated that problems-- particularly imitation learning and structured prediction-- where a learner's predictions influence the input-distribution it is tested on can be naturally addressed by an interactive approach and analyzed using no-regret online learning. These approaches to imitation learni…
Motivated by the scarcity of accurate payoff feedback in practical applications of game theory, we examine a class of learning dynamics where players adjust their choices based on past payoff observations that are subject to noise and random disturbances. First, in the single-player case (corresponding to an agent tryi…
Online learning with one-sided feedback aims to maximize accuracy while ensuring fairness.
problem Maximizing accuracy in online learning with limited feedback and ensuring fairness.
method Extending the framework of Bechavod et al. (2020) to incorporate dynamic panels of auditors, reducing the problem to a contextual combinatorial semi-bandit, and leveraging Exp2 and Context-Semi-Bandit-FTPL algorithms.
result Multi-criteria no regret guarantees for accuracy and fairness are provided.
Proper Calibeatingecon.TH
New concept of proper-calibeating extends classic calibrated forecasts to proper scoring rules.
problem Defining and extending calibrated forecasts to proper scoring rules.
method Extending the concepts of calibrated and calibeating forecasts to proper scoring rules and proving their properties.
result Proper-calibration always implies calibration, but proper-calibeating does not necessarily imply calibeating.
Many prediction domains, such as ad placement, recommendation, trajectory prediction, and document summarization, require predicting a set or list of options. Such lists are often evaluated using submodular reward functions that measure both quality and diversity. We propose a simple, efficient, and provably near-optim…
We consider a family of learning strategies for online optimization problems that evolve in continuous time and we show that they lead to no regret. From a more traditional, discrete-time viewpoint, this continuous-time approach allows us to derive the no-regret properties of a large class of discrete-time algorithms i…
The paper tackles robust policy learning from multiple data sources.
problem Learning a policy that generalizes across diverse settings from multiple heterogeneous data sources.
method Proposes a minimax regret optimization objective and a policy learning algorithm combining doubly robust offline policy evaluation and no-regret learning.
result Achieves minimal worst-case mixture regret up to a moderated vanishing rate of the total data across all sources.
Optimistic Thompson Sampling reduces regret in unknown multi-player games.
problem Navigating uncertainty in unknown multi-player games with strategic decision-making.
method Introduces Thompson Sampling algorithms that exploit opponents' actions and reward structures.
result Achieves over tenfold improvements in experimental budgets with logarithmic regret bound.
This work shows neural networks can solve non-convex constraints problems.
problem Training neural networks under non-convex constraints.
method Project stochastic gradient descent with no-regret analysis of online learning.
result Overparameterized neural networks achieve near-optimal and near-feasible solutions.
Study agnostic feature-based dynamic pricing models with linear policies and noisy valuations.
problem Tackles dynamic pricing with unknown noise and no assumptions on data.
method Studies two agnostic models: linear policy and linear noisy valuation, presenting algorithms and regret bounds.
result Demonstrates no-regret learning is possible under weak assumptions, but noisy feedback is not significantly more useful than bandit feedback.
PDCA algorithm learns policies for RL with constraints using a primal-dual approach.
problem Offline constrained reinforcement learning with general function approximation.
method Primal-Dual-Critic Algorithm (PDCA) using a primal-dual approach.
result PDCA finds a near saddle point of the Lagrangian, nearly optimal for constrained RL.