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.
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.
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.
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.
Generating labeled training datasets has become a major bottleneck in Machine Learning (ML) pipelines. Active ML aims to address this issue by designing learning algorithms that automatically and adaptively select the most informative examples for labeling so that human time is not wasted labeling irrelevant, redundant…
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…
Generative adversarial nets (GANs) are a promising technique for modeling a distribution from samples. It is however well known that GAN training suffers from instability due to the nature of its maximin formulation. In this paper, we explore ways to tackle the instability problem by dualizing the discriminator. We sta…
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.
Decision maker's preferences are often captured by some choice functions which are used to rank prospects. In this paper, we consider ambiguity in choice functions over a multi-attribute prospect space. Our main result is a robust preference model where the optimal decision is based on the worst-case choice function fr…
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.
We present heuristics for solving the maximin problem induced by the generative adversarial privacy setting for linear and convolutional neural network (CNN) adversaries. In the linear adversary setting, we present a greedy algorithm for approximating the optimal solution for the privatizer, which performs better as th…
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.
We study the problem of alleviating the instability issue in the GAN training procedure via new architecture design. The discrepancy between the minimax and maximin objective values could serve as a proxy for the difficulties that the alternating gradient descent encounters in the optimization of GANs. In this work, we…
Minimax optimization has found extensive applications in modern machine learning, in settings such as generative adversarial networks (GANs), adversarial training and multi-agent reinforcement learning. As most of these applications involve continuous nonconvex-nonconcave formulations, a very basic question arises---"w…
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.
In a pathbreaking paper, Cover and Ordentlich (1998) solved a max-min portfolio game between a trader (who picks an entire trading algorithm, θ(⋅)) and "nature," who picks the matrix X of gross-returns of all stocks in all periods. Their (zero-sum) game has the payoff kernel Wθ(X)/D(X), where Wθ(X) is the…
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.
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.
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.
We deconstruct the performance of GANs into three components: 1. Formulation: we propose a perturbation view of the population target of GANs. Building on this interpretation, we show that GANs can be viewed as a generalization of the robust statistics framework, and propose a novel GAN architecture, termed as Cascade …
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.
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.
The leverage effect refers to the generally negative correlation between the return of an asset and the changes in its volatility. There is broad agreement in the literature that the effect should be present for theoretical reasons, and it has been consistently found in empirical work. However, a few papers have pointe…
New method for interpreting non-linear models using forward marginal effects.
problem Interpreting non-linear models' feature effects is challenging.
method Introducing forward marginal effects and partitioning feature space for better interpretation.
result Improved interpretation of non-linear prediction functions.
The Kalinin effectivity is studied and applied to compactifications and Hilbert squares.
problem Understanding Kalinin effectivity in compactifications and its applications.
method Definition, construction methods, and analysis of Kalinin effectivity in various compactifications.
result Wonderful compactifications of hyperplane arrangements and configuration spaces are Kalinin effective.
New method estimates treatment effects in network data, accounting for spillover effects.
problem Treatment effect estimation in networks with spillover effects.
method Augmented inverse probability weighting (AIPW) with cross-fitting and machine learning.
result Semiparametric treatment effect estimator converges at parametric rate and follows Gaussian distribution.
Causalfe estimates treatment effects in panel data with fixed effects.
problem Spurious heterogeneity in treatment effect estimates due to fixed effects in panel data.
method CFFE approach with node-level residualization during tree construction.
result Validates the estimator's performance through simulation studies.
The paper clarifies the distinction between CATE and ITE under ignorability assumptions.
problem Confusion between CATE and ITE hinders personalized effect estimation.
method Clarifies the distinction between CATE and ITE under ignorability assumptions.
result CATE and ITE are not necessarily the same under ignorability assumptions.
GADGET framework decomposes global feature effects using recursive partitioning.
problem Misleading global feature effects when feature interactions are present.
method Generalized additive decomposition of global effects (GADGET) based on recursive partitioning.
result Minimizes interaction-related heterogeneity of local feature effects.
A new RL framework evaluates dynamic mediation effects over time.
problem Dynamic mediation effects in sequentially assigned treatments.
method Reinforcement Learning framework for decomposition and estimation of causal effects.
result Superior performance demonstrated through numerical studies and real data analysis.
New memory effect discovered in gravitational wave behavior.
problem Understanding gravitational wave behavior in spacetimes with angular momentum.
method Mathematical analysis of Minkowski spacetime and Kerr black holes.
result Angular momentum memory effect observed at future null infinity.
Effective Yau-Tian-Donaldson conjecture for spherical varieties.
problem Finding effective K-stability criteria for spherical varieties.
method Formulated an effective variant of the Yau-Tian-Donaldson conjecture and reviewed effective K-stability criteria for spherical varieties.
result Effective K-stability criteria can be computed given combinatorial data.
A new method estimates treatment effects in mixed groups, improving accuracy.
problem Estimating treatment effects in mixed groups with heterogeneous responses.
method PCM (pre-cluster and merge) approach for nonparametric estimation.
result Asymptotic consistency and significant improvement in accuracy over existing methods.
Study estimates heterogeneous principal causal effects with binary treatments and intermediate variables.
problem Estimating subgroup effects within strata defined by potential values of an intermediate variable.
method Proposes a framework for estimating and forming confidence intervals for heterogeneous principal causal effects under principal ignorability assumption. Develops several estimators with varying robustness properties.
result Established large-sample theory and analyzed bias contributions of each approach.
Study develops method for estimating causal effects in continuous variables.
problem Lack of methods for estimating causal effects in continuous variables.
method Develops a method independent of data generating models for continuous variable interventions.
result Preserves identifiability of data and applies to any generating models.
ICA accurately estimates treatment effects even with confounders.
problem Estimating treatment effects in the presence of confounding variables.
method Uses Independent Component Analysis (ICA) to identify latent sources and estimate mixing coefficients.
result Linear ICA can consistently estimate multiple treatment effects, even with Gaussian confounders, and is more sample-efficient than Orthogonal Machine Learning (OML).
Method improves treatment effect estimation in randomized experiments.
problem Estimating distributional treatment effects in randomized experiments.
method Distributional regression framework with machine learning for variance reduction.
result The proposed method reduces variance of distributional treatment effect estimators.
New method discovers context effects in choice data.
problem Identifying context effects from choice data is challenging.
method Automatic discovery of context effects from observed choices.
result Automatic discovery of context effects from observed choices.
Dropout introduces both explicit and implicit regularization effects.
problem Understanding the full impact of dropout regularization.
method Disentangled explicit and implicit regularization effects through experiments and analytic simplifications.
result Explicit and implicit regularization effects of dropout are distinct and can be characterized analytically.