Optimistic estimate predicts best fitting performance of nonlinear models.
problem Evaluating the potential of nonlinear models in fitting.
method Proposes an optimistic estimate to quantify the smallest sample size for fitting nonlinear models.
result Predicts specific subsets of targets that can be fitted at overparameterization.
Proposes Optimistic Pessimistically Initialised Q-Learning (OPIQ) for better exploration in RL.
problem Pessimistic initialisation of Q-values in deep RL leads to poor exploration performance.
method Augments pessimistically initialised Q-values with count-based bonuses to ensure optimism.
result OPIQ outperforms non-optimistic DQN variants in hard exploration tasks.
Accelerates policy optimization in RL with optimistic and adaptive updates.
problem Improving policy optimization methods in reinforcement learning.
method Integrates foresight into policy improvement step via optimistic and adaptive updates.
result Designs an optimistic policy gradient algorithm, adaptive via meta-gradient learning.
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.
Optimistic algorithm reduces regret and constraint violations in online convex optimization with adversarial constraints.
problem Online convex optimization with adversarial constraints.
method Improved algorithm using accurate predictions of loss and constraint functions.
result Improved bounds on regret and cumulative constraint violations.
Optimistic method adapted for faster convex-concave min-max problems.
problem Solving convex-concave min-max optimization problems efficiently.
method Adaptive, line search-free second-order methods combining optimistic updates and second-order information.
result Achieves optimal convergence rate without line search or backtracking.
The paper analyzes bias in sample means of multi-armed bandits.
problem Analyzing bias in sample means of multi-armed bandits.
method Decoupling three sources of bias: sampling, stopping, choosing; using optimism to capture monotonic behaviors.
result Optimistic sampling induces negative bias, while optimistic stopping and choosing induce positive bias.
Optimistic bounds for multi-output learning using self-bounding Lipschitz condition.
problem Learning vector-valued functions from supervised data.
method Introducing self-bounding Lipschitz condition and proving optimistic bounds using local Rademacher complexity and Srebro's inequality.
result Minimax optimal generalization bounds for multi-output learning, up to logarithmic factors.
New AMSGrad variant accelerates nonconvex optimization.
problem Training deep neural networks efficiently.
method Adds prior knowledge to AMSGrad, leveraging sequential predictability and optimistic online learning.
result Accelerates convergence and increases sample efficiency.
Randomized exploration methods are more statistically efficient than optimistic methods in reinforcement learning.
problem Comparing and contrasting optimistic and randomized exploration methods in reinforcement learning.
method Analytic examples to compare optimistic and randomized approaches.
result Randomized approaches are more statistically efficient than optimistic approaches.
Bayesian classifier improves robustness with optimistic score ratio.
problem Limited information on class-conditional distribution.
method Optimistic score ratio for robust binary classification.
result Bayesian classifier using optimistic score ratio is robust and computationally tractable.
We show that the optimistic limits of the colored Jones polynomials of the hyperbolic knots coincide with the optimistic limits of the Kashaev invariants modulo 4 π 2 4π^2 4 π 2 .
Paper proposes Adaptive DDPG for better stock portfolio allocation.
problem Challenges in finding optimal stock portfolio allocation in dynamic stock markets.
method Adaptive Deep Deterministic Reinforcement Learning (Adaptive DDPG) incorporating optimistic or pessimistic reinforcement learning.
result Adaptive DDPG outperforms traditional and baseline strategies in investment return and Sharpe ratio.
The optimistic limit is the mathematical formulation of the classical limit which is a physical method to expect the actual limit by using saddle point method of certain potential function. The original optimistic limit of the Kashaev invariant was formulated by Yokota, and a modified formulation was suggested by the a…
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 O ( 1 / T 2 ) O(1/T^2) O ( 1/ T 2 ) rate for equilibrium computation. New algorithm reduces regret in sequential decision-making problems.
problem Balancing exploration and exploitation in online sequential decision problems.
method Variational Bayesian optimistic sampling (VBOS) for optimizing policies.
result VBOS achieves i l d e O ( A T ) ilde O(\sqrt{AT}) i l d e O ( A T ) Bayesian regret for stochastic multi-armed bandits. Combines online learning algorithms to achieve better performance.
problem Improving online learning algorithms with varying guarantees.
method Adding iterates of two parameter-free algorithms to create a new algorithm with improved regret.
result Generates efficient algorithms that adapt to multiple norms and maintain dimension-free guarantees.
Paper proves convergence of optimistic policy iteration for stochastic shortest path problems.
problem Optimizing policies in stochastic shortest path problems.
method Analyzes optimistic policy iteration algorithm with Monte Carlo and TD(λ) methods.
result Proves convergence of the algorithm under specific conditions.
Optimistic NPG improves policy optimization in online RL with efficient sample complexity.
problem Limited theoretical understanding of policy optimization, especially in online RL.
method Combines natural policy gradient with optimistic policy evaluation.
result Achieves optimal dimension dependence sample complexity for learning near-optimal policies.
Optimistic likelihoods improve classification accuracy by considering nearby distributions.
problem Evaluating likelihoods of nominal distributions estimated from data, which can be inaccurate.
method Use ambiguity sets and geodesic/standard convex optimization to compute optimistic likelihoods.
result Optimistic likelihoods lead to better classification performance.
Characterizes concept classes for optimistic online learning.
problem Understanding minimal assumptions for online learnability.
method Investigates two questions about concept classes' learnability.
result Characterizes all concept classes for optimistically universal online learnability.
Study shows over-sampling biases prediction results on imbalanced datasets.
problem Over-optimistic prediction results on imbalanced data.
method Applying over-sampling before partitioning training and testing sets.
result Over-sampling causes biased results and reduces predictive performance.
Optimistic algorithms achieve logarithmic regret bounds for MDPs without diameter dependence.
problem Achieving logarithmic regret bounds for episodic MDPs without relying on diameter-like quantities.
method Novel 'clipped' regret decomposition applied to optimistic algorithms.
result Smooth interpolation between gap-dependent and minimax rates of convergence.
Improved CFR achieves faster convergence to Nash equilibrium.
problem Theoretical slow convergence rate of CFR algorithms.
method Combining predictive and stable regret minimizers within CFR.
result Achieves O ( T − 3 / 4 ) O(T^{-3/4}) O ( T − 3/4 ) convergence rate, faster than O ( T − 1 / 2 ) O(T^{-1/2}) O ( T − 1/2 ) . New algorithms reduce forecasting errors by leveraging optimistic learning and hinting.
problem Real-time climate forecasting with delayed feedback.
method Developed optimistic online learning algorithms that reduce regret under delayed feedback.
result Optimistic learning algorithms achieve optimal regret guarantees under delayed feedback.
New method minimizes regret in AMDP with high probability.
problem Pessimistic regret analysis in AMDP.
method Developed a new policy search method achieving optimistic regret.
result Achieved sublinear optimistic regret with high probability.
Omega method mitigates noise in stochastic game optimization.
problem Noise sensitivity and convergence issues in stochastic game optimization.
method Omega method incorporates EMA of historic gradients in its update rule.
result Omega method outperforms optimistic gradient method in stochastic games.
Unified framework for model- and value-optimistic reinforcement learning.
problem Designing and analyzing algorithms for episodic reinforcement learning.
method Lagrangian duality for a unified view of optimism.
result Unified algorithms with both efficient implementation and probabilistic analysis.
Efficiently solves exploration-exploitation in LQR using Lagrangian relaxation.
problem Exploration-exploitation dilemma in linear quadratic regulator (LQR) setting.
method Relax optimistic optimization into a constrained extended LQR problem, then solve using Riccati equations.
result Computes ε ε ε -optimistic controller efficiently with O ( log ( 1 / ε ) ) O\big(\log(1/ε)\big) O ( log ( 1/ ε ) ) Riccati equations. New algorithm achieves data-dependent regret bounds in MDPs with unknown transitions.
problem Achieving best-of-both-worlds guarantees with data-dependent regret bounds in MDPs with unknown transitions.
method Optimistic follow-the-regularized-leader algorithm with new optimistic Q-function estimators and transition bonus.
result First-order, second-order, and path-length bounds with polylog(T) regret in the stochastic regime.
Proposes H-UCRL for efficient model-based RL with sublinear regret.
problem Greedy policy exploration in model-based RL ignores epistemic uncertainty.
method Reparameterizes plausible models, hallucinates control, augments input space, solves with greedy planners.
result H-UCRL achieves provably sublinear regret for Gaussian Process models.
New method for optimistic planning in MDPs using regularization.
problem Optimistic planning in infinite-horizon discounted MDPs.
method Regularized dynamic programming for approximate value iteration.
result Achieves near-optimal statistical guarantees in learning policies.
Improved path-length regret bounds for adaptive and oblivious adversaries.
problem Adaptive and oblivious adversaries in multi-armed bandit and linear bandit problems.
method Developed two new algorithms based on optimistic mirror descent framework with novel techniques.
result Strictly improved path-length bounds for adaptive adversary and better results for oblivious adversary.
Optimistic Hedge achieves optimal regret bounds in two-player zero-sum games.
problem Achieving optimal regret bounds for optimistic Hedge in two-player zero-sum games.
method Refined regret analysis and optimization problem formulation.
result Optimistic Hedge achieves O ( log m log n ) O(\sqrt{\log m \log n}) O ( log m log n ) regret bounds, matching upper and lower bounds. OMD improves training GANs by avoiding limit cycling.
problem Limit cycling behavior in training GANs.
method Optimistic Mirror Decent (OMD) for training Wasserstein GANs.
result OMD addresses limit cycling in WGANs and converges to an equilibrium.
We present a powerful general framework for designing data-dependent optimization algorithms, building upon and unifying recent techniques in adaptive regularization, optimistic gradient predictions, and problem-dependent randomization. We first present a series of new regret guarantees that hold at any time and under …
Optimistic PPO variant solves linear MDPs with improved regret bound.
problem Understanding theoretical limits of PPO in linear MDPs.
method Proposes an optimistic variant of PPO for episodic adversarial linear MDPs with full-information feedback.
result Establishes a i l d e O ( d 3 / 4 H 2 K 3 / 4 ) ilde{\mathcal{O}}(d^{3/4}H^2K^{3/4}) i l d e O ( d 3/4 H 2 K 3/4 ) regret bound. New validation method prevents privacy breaches and biases in federated learning.
problem Privacy breaches and data leakage in federated learning.
method Stratified cross-validation for unbiased and privacy-preserving federated learning.
result Stratified cross-validation prevents data leakage without demanding deduplication algorithms.
This paper develops a model of reference-dependent assessment of subjective beliefs in which loss-averse people optimally choose the expectation as the reference point to balance the current felicity from the optimistic anticipation and the future disappointment from the realisation. The choice of over-optimism or over…
Study optimizes linear regression analysis for high-dimensional settings.
problem Understanding high-dimensional linear regression with interpolation and regularization.
method Localized uniform convergence analysis of optimistic rates for linear regression.
result Recover guarantees for ridge and LASSO regression under random designs.
Paper proposes an optimistic likelihood approximation for nonparametric likelihoods.
problem Computational intractability of evaluating likelihood functions in Bayesian statistics.
method Non-parametric approximation using distributionally robust optimization.
result Optimistic likelihood can be solved as a convex optimization problem with analytical expressions.
Novel approach to universal online learning for bounded losses, closing open problems.
problem Characterizing processes for universal online learning under non-i.i.d. conditions.
method Characterization of processes admitting strong and weak universal learning, introduction of optimistically universal learning rule.
result Introduction of a novel 1NN algorithm that is optimistically universal for bounded losses.
Solves open problem on universally consistent online learning with unbounded losses.
problem Open problem on universally consistent online learning with unbounded losses.
method Constructs random measurable partitions of the instance space.
result Simple memorization rule is optimistically universal for any unbounded loss.
New framework uses tempered optimism to handle imperfect experts in online learning.
problem Challenges of implicit optimism in practical online learning environments.
method Introduces tempered optimism as a framework for online non-convex learning, modifies existing algorithms.
result Demonstrates tempered optimism as a fruitful paradigm for online non-convex learning.
Paper uses machine learning and SIR models to predict COVID-19 cases.
problem Predicting the spread of COVID-19 cases for control measures.
method Machine learning and SIR models (deterministic and stochastic) with numerical approximations.
result Predictions help in finding concrete actions to control the pandemic.
Unified framework for model-based RL with sample complexity guarantees.
problem Designing efficient posterior sampling methods for model-based RL.
method Optimistic posterior sampling, Hellinger distance reduction, data likelihood measurement.
result Unified algorithms with state-of-the-art sample complexity guarantees.
Optimistic pricing algorithm handles online dynamic pricing with censored demand.
problem Online dynamic pricing with censoring of potential demand.
method Optimistic estimates of derivatives for pricing algorithm.
result Achieves i l d e O ( T ) ilde{O}(\sqrt{T}) i l d e O ( T ) optimal regret against adversarial inventory series. Yokota suggested an optimistic limit method of the Kashaev invariants of hyperbolic knots and showed it determines the complex volumes of the knots. His method is very effective and gives almost combinatorial method of calculating the complex volumes. However, to describe the triangulation of the knot complement, he re…