Maximin UCB algorithm optimizes energy harvesting for sensor networks.
problem Optimizing energy harvesting for sensor nodes in varying environments.
method Modeling as Maximin Multi-Armed Bandits and proposing Maximin UCB algorithm.
result Maximin UCB algorithm achieves performance guarantees similar to UCB1.
The paper develops methods to identify stable associations across multiple studies.
problem Identifying stable associations across multiple studies with possible distributional shifts.
method Modeling heterogeneous multi-source data with multiple high-dimensional regressions and devising a novel sampling method for valid confidence intervals of maximin effects.
result Significant maximin effects indicate stable associations that can be generalized to target populations.
A method for selecting pseudo-labeled data in semi-supervised learning using generalized Bayes and soft revision.
problem Selecting pseudo-labeled data for semi-supervised learning with robustness to uncertainty.
method Using credal sets and the Gamma-Maximin method with soft revision to update priors and select pseudo-labeled data.
result The Gamma-Maximin method with soft revision can achieve promising results, especially in scenarios with low labeled data proportions.
We study an original problem of pure exploration in a strategic bandit model motivated by Monte Carlo Tree Search. It consists in identifying the best action in a game, when the player may sample random outcomes of sequentially chosen pairs of actions. We propose two strategies for the fixed-confidence setting: Maximin…
We study optimal investment problem for a diffusion market consisting of a finite number of risky assets (for example, bonds, stocks and options). Risky assets evolution is described by Ito's equation, and the number of risky assets can be larger than the number of driving Brownian motions. We assume that the risk-free…
Maximizes robustness in Bayesian experimental design under model uncertainty.
problem Brittleness of Bayesian experimental design under model misspecification.
method Formulates as a max--min game, uses Sibson's α-MI, and adopts PAC-Bayes framework.
result Establishes robust belief update and conditional information gain measure.
This paper tackles GAN instability by dualizing the discriminator.
problem GAN training instability due to the maximin formulation.
method Dualizing the discriminator to reformulate the saddle point objective into a maximization problem.
result The dualing GAN approach removes instability for linear discriminators and provides an alternative for nonlinear discriminators.
Optimizes binary regression models with gradient ascent-descent methods.
problem Regression problems with binary weights in quantized learning and digital communication.
method Maximin optimization using gradient ascent-descent methods.
result The approach is optimal in linear regression with low noise and robust regression with few outliers.
Annealed Entropic Allocation improves ranking and selection by mitigating hard switching and improving finite-budget discrimination.
problem Sequential budget allocation in ranking and selection
method Annealed weighted soft-min framework
result Surrogate converges uniformly to the hard minimum, soft-min weights concentrate on active challengers, and target allocation map is continuous.
SurvLIME-KS improves survival model explanations robustly.
problem Improving explanations of unreliable survival models.
method SurvLIME-KS combines Cox proportional hazards model and Kolmogorov-Smirnov bounds for robust optimization.
result SurvLIME-KS minimizes average distance and maximizes distance in approximating cumulative hazard functions.
We construct a new map from a convex function to a distribution on its domain, with the property that this distribution is a multi-scale exploration of the function. We use this map to solve a decade-old open problem in adversarial bandit convex optimization by showing that the minimax regret for this problem is $\tild…
New approach for active learning in overparameterized models.
problem Efficiently labeling datasets in machine learning.
method MaxiMin Active Learning for nonparametric or overparameterized models.
result Automatically identifies decision boundaries and data clusters.
A new approach to group fairness treats it as a bargaining problem.
problem Fairness in deploying predictors across subpopulations.
method Interpreting fairness as a bargaining problem and proposing relative improvement.
result Relative improvement provides axiomatic justification and finite-sample convergence guarantees.
Study generalizes property elicitation to imprecise probabilities.
problem Minimizing risk over imprecise probability distributions.
method Maximin risk minimization over a set of imprecise probabilities.
result Conditions for elicitability of IP-properties.
Heuristics for solving privacy setting problems in neural networks.
problem Maximin problem in generative adversarial privacy setting.
method Greedy algorithm for linear adversaries and alternately optimizing for CNN adversaries.
result The greedy algorithm performs better as the number of instances increases.
Proposes a new sampling policy for ranking and selection problems.
problem Improving ranking and selection in adaptive sampling policies.
method Annealed entropic allocation, using soft-min weights and saddlepoint corrections.
result Consistently competitive performance in various settings.
Stackelberg GAN improves GAN stability by reducing minimax gap.
problem Stability issues in GAN training procedure.
method New multi-generator architecture and application of Shapley-Folkman lemma.
result Minimax gap shrinks to ε with rate O(1/ε) as the number of generators increases.
Paper defines local optimality for sequential nonconvex-nonconcave games.
problem Defining local optimality in sequential nonconvex-nonconcave minimax optimization.
method Proposes local minimax definition and connects to gradient descent ascent.
result Gradient descent ascent stable limit points are local minimax points.
This paper tackles sample-efficient reinforcement learning for partially observable Markov games.
problem Learning in partially observable Markov games with incomplete information.
method A simple algorithm combining optimism and Maximum Likelihood Estimation (MLE) for self-play, and a variant of optimistic MLE for adversarial opponents.
result The proposed algorithms achieve approximate Nash, correlated, and coarse correlated equilibria in polynomial samples for weakly revealing POMGs.
Paper solves a max-min game for complex performance benchmarks.
problem Max-min portfolio game with complex performance benchmarks.
method Solves a max-min game for complex performance benchmarks using the axiom of choice.
result Exact maximin strategy found for arbitrary performance benchmarks.
Unified algorithm for efficient pure exploration using dual variables.
problem Efficiently achieving a specific goal through adaptive experimentation.
method Introducing dual variables to derive optimal allocation conditions, leading to Information-Directed Selection.
result Top-two Thompson sampling attains asymptotic optimality for Gaussian best-arm identification.
New algorithm identifies optimal subtrees in fixed-budget tree search.
problem Identifying optimal subtrees in fixed-budget Monte Carlo Tree Search.
method ε-agnostic algorithm for max-min action identification.
result Misidentification probability decays exponentially with sample size.
Improves pre-trial risk assessments by making them safer without changing existing rules.
problem Improving pre-trial risk assessments while maintaining deterministic rules.
method Developed a maximin robust optimization approach to find a safer policy.
result Can safely improve certain components of the risk assessment instrument.
Paper develops a robust preference model for multi-attribute choices.
problem Ambiguity in multi-attribute choice functions.
method Pairwise comparisons for preference elicitation, robust optimization model based on worst-case choice function.
result Developed tractable formulations for robust preference optimization.
A new approach for efficient batch multiobjective optimization using Thompson sampling.
problem Inefficient batch multiobjective optimization due to expensive oracles and hard inner optimization.
method Proposes a Thompson sampling approach (qextttPOTS) that chooses Pareto optimal candidates sequentially. result Empirically superior performance compared to classical evolutionary approaches and MOBO.
Novel MOBO method for risk measures under input uncertainty.
problem Efficiently identifying Pareto front for black-box functions with input uncertainty.
method Assumes Gaussian process model and constructs bounding boxes for risk measures.
result The method can return an arbitrary-accurate solution with high probability.
DQ4FairIM uses RL to maximize influence while ensuring fairness across all groups.
problem Fairness in influence maximization in social networks.
method Fairness-aware deep RL method using Structure2Vec network embedding.
result DQ4FairIM achieves higher fairness than fairness-agnostic and fairness-aware baselines.
Algorithm identifies correct hypothesis from alternatives in bandit problems.
problem Efficiently identifying the correct hypothesis from a finite set of alternatives in structured stochastic multi-armed bandits.
method Frank-Wolfe Self-Play (FWSP) reformulates the game as a saddle-point problem, using a differential-inclusion argument to prove convergence.
result Convergence of the game value for best-arm identification in linear bandits, with uniform global convergence to the optimal value.
This research deconstructs GANs into formulation, generalization, and optimization components.
problem Improving the performance and stability of GANs.
method Proposes a perturbation view of GANs, introduces Cascade GANs, and develops principles for GAN generalization and optimization.
result Demonstrates a fundamental trade-off in GAN approximation and statistical errors, and proposes a new GAN architecture with zero minimax duality gap.
Bayesian optimization agent learns user preferences from pairwise comparisons.
problem Learning user preferences from unknown and infinite choices.
method Sequential Bayesian optimization with pairwise comparisons.
result Optimal agent strategy minimizes remaining system uncertainty.
Investment strategy in uncertain markets improved by learning and risk-ambiguity preferences.
problem Investment in financial markets with unknown drift coefficients.
method Optimization under KMM approach, considering risk and ambiguity preferences.
result Optimal investment strategy can be adjusted based on prior drift distribution.