The paper improves dropout's utility by reducing interactions in deep neural networks.
problem Over-fitting problem in deep learning.
method Game-theoretic interactions analysis and interaction loss.
result Interaction loss improves dropout's utility and boosts DNN performance.
A new method extends Bayesian optimization to more models and utilities.
problem Extending Bayesian optimization to a broader class of models and utilities.
method Likelihood-free Bayesian Optimization (LFBO) which directly models the acquisition function without separate inference.
result LFBO outperforms state-of-the-art black-box optimization methods on real-world problems.
Improved privacy and utility in machine learning with adaptive differential privacy.
problem Enhancing privacy in machine learning models while maintaining utility.
method Adaptive differentially private (ADP) learning method that optimally adapts noise to stepsize.
result ADP method significantly improves utility compared to standard differentially private methods.
Study uses reinforcement learning to optimize portfolios under recursive utility.
problem Improving portfolio allocation using risk-sensitive objectives.
method Approximated certainty equivalent via Monte Carlo, trained actor-critic algorithms (PPO, A2C).
result Recursive-utility agent outperforms discounted baseline in Sharpe ratio, max drawdown, and cumulative return.
Global optimization in Bayesian inference yields little additional benefit.
problem Improving psychometric parameter estimation using global optimization strategies.
method Experimental simulations comparing myopic and global strategies in multiple models.
result Global optimization strategies provide negligible additional utility improvement beyond the immediate next steps.
Study shows cooperation can improve everyone's market efficiency.
problem Understanding when collective cooperation improves market efficiency.
method General semimartingale framework, deriving necessary and sufficient conditions.
result Strict improvement in each agent's indirect utility when cooperation is beneficial.
Examines optimal risk sharing with realistic risk attitudes, finding risk seeking in certain subdomains.
problem Optimal risk sharing with empirically realistic risk attitudes.
method Allows for risk-seeking agents, generalizes expected utility, and uses counter-monotonic improvement theorem.
result First empirical results on optimal risk sharing with realistic risk attitudes.
Bayesian decision theory outlines a rigorous framework for making optimal decisions based on maximizing expected utility over a model posterior. However, practitioners often do not have access to the full posterior and resort to approximate inference strategies. In such cases, taking the eventual decision-making task i…
DIFF2 improves differential privacy in nonconvex optimization with better utility bounds.
problem Improving differential privacy in nonconvex optimization with better utility bounds.
method DIFF2 constructs a differential private global gradient estimator using gradient differences.
result DIFF2 achieves a utility of \(\widetilde O(d^{2/3}/(n\varepsilon_{\mathrm{DP}})^{4/3})\), significantly better than \(\widetilde O(\sqrt{d}/(n\varepsilon_{\mathrm{DP}}))\).
DPLTM uses private winners to improve privacy and utility.
problem Balancing privacy and utility in machine learning models.
method DPLTM applies differential privacy to the lottery ticket method.
result DPLTM converges faster and uses less privacy budget.
AGI modifies utility function to cooperate, conflicting with orthogonality thesis.
problem AGI modifying utility function to improve cooperation, conflicting with orthogonality thesis.
method Observing and influencing interactions, converging to similar utility functions.
result AGIs in competitive environments may optimize for favorable influence.
Algorithm improves recommendation subset selection in the presence of biases.
problem Maximizing submodular functions for recommendation in the presence of social biases.
method Algorithm for submodular maximization with fairness constraints.
result Algorithm provably outputs subsets with near-optimal utility and proportional representation.
New private learning algorithms improve utility in tasks with public features.
problem Private learning with public features in recommendation and ad prediction.
method Developed algorithms that protect only certain sufficient statistics, improving utility for linear regression and private recommendation benchmarks.
result Achieved state-of-the-art performance on private recommendation benchmarks.
Paper improves privacy-utility trade-off in federated learning.
problem Repeated parameter sharing in federated learning leaks private data.
method Proposes a new representation federated learning objective with differential privacy guarantees.
result Algorithm \DPFEDREP\ converges to a global optimal solution with a linear rate and privacy budget-dependent radius.
Improved vector quantization using Gaussian mixtures for better codebook utilization.
problem Training instability and information loss in discrete vector quantization.
method Generalized vector quantization with Gaussian mixture model and aggregated categorical posterior evidence lower bound.
result GM-VQ improves codebook utilization and reduces information loss without heuristics.
DP-RandP improves privacy-utility tradeoff in DP-SGD by learning priors from random processes.
problem Improving the performance of differentially private stochastic gradient descent (DP-SGD) on private data.
method A three-phase approach that learns priors from images generated by random processes and transfers these priors to private data.
result New state-of-the-art accuracy on CIFAR10, CIFAR100, MedMNIST, and ImageNet for various privacy budgets.
Paper improves privacy and utility of SGD with bounded domain and smooth losses.
problem Lack of tight privacy bounds and practical assumptions in DPSGD.
method Rigorous privacy characterization for DPSGD with general L-smooth and non-convex loss functions, tracking privacy loss over iterations.
result Privacy loss converges without convexity assumption for bounded domain, improving utility.
Improved privacy analysis for stochastic gradient descent.
problem Analyzing privacy leakage in noisy stochastic gradient descent.
method Modeling Rényi divergence dynamics with Langevin diffusions, proving exponential privacy loss convergence for smooth and strongly convex objectives.
result Privacy loss converges exponentially fast for smooth and strongly convex objectives under constant step size.
Intersectional constraints improve selection outcomes by reducing inequality.
problem Persistent inequality and reduced utility in selection processes due to implicit bias.
method Introducing intersectional constraints to mitigate the adverse effects of implicit bias in selection processes.
result Intersectional constraints can recover almost all the utility achievable in the absence of implicit bias, offering a significant advantage over non-intersectional constraints.
Advocates focusing on utility functions to avoid unfair outcomes.
problem Unfair outcomes from fairness criteria in optimizing policies.
method Defines value of information fairness and proposes modifying utility functions.
result Value of information fairness leads to better answers than existing fairness notions.
Off-policy reinforcement learning with eligibility traces is challenging because of the discrepancy between target policy and behavior policy. One common approach is to measure the difference between two policies in a probabilistic way, such as importance sampling and tree-backup. However, existing off-policy learning …
SMOTE-DP enhances synthetic data privacy without sacrificing utility.
problem Balancing privacy and utility in synthetic data generation.
method Integrating SMOTE with differential privacy mechanisms.
result SMOTE-DP produces synthetic data that is both private and useful.
New DP mechanisms improve ML privacy-utility-computational tradeoffs.
problem Improving privacy in machine learning with multiple passes over data.
method Formalized DP for adaptive streams, extended matrix factorization techniques, Fourier-transform-based mechanism.
result Substantial improvements in privacy-utility-computational tradeoffs over previous methods.
Loss-calibrated EP improves Bayesian decision-making by focusing on utility-sensitive posterior approximations.
problem Bayesian decision-making under asymmetric utility functions.
method Loss-calibrated expectation propagation (Loss-EP) that tilts the posterior towards higher utility decisions.
result Loss-EP can capture useful information for decision-making under asymmetric penalties.
Paper relaxes differential privacy for correlated features, improving privacy-utility trade-off.
problem Standard differential privacy ignores feature correlation, leading to suboptimal privacy-utility balance.
method Introduces CorrDP framework that accounts for feature correlation, using total variation distance for quantification.
result CorrDP algorithms outperform standard DP in synthetic and real-world datasets with insensitive features.
The paper proposes using density ratio estimation to evaluate synthetic data quality.
problem Improving the quality and utility of synthetic data for analysis.
method Density ratio estimation to measure synthetic data quality.
result Density ratio estimation yields more accurate global utility estimates than existing methods.
New method generates private synthetic data with optimal utility for smooth queries.
problem Achieving strong utility guarantees for meaningful downstream analysis of sensitive datasets.
method Proposes a polynomial-time algorithm for generating (ε,δ)-differentially private synthetic data with minimax optimal error rates for smooth queries. result Achieves a minimax error rate of Ok,d(n−min{1,dk}) for k-smooth queries, up to a log(n) factor. FairDTD improves fairness in GNNs by distilling dual teacher knowledge, balancing utility and bias.
problem Bias in GNN predictions due to sensitive attributes.
method Dual-Teacher Distillation with a causal graph model, feature and structure teachers, and graph-level distillation.
result Achieves optimal fairness while preserving high model utility.
New algorithm optimizes privacy and utility in multi-task learning with skewed data.
problem Privacy constraints in multi-task learning with uneven data distribution.
method Adaptive reweighting of privacy budget allocation among tasks.
result Significant improvement in utility with state-of-the-art performance on benchmarks.
The implementation of smart building technology in the form of smart infrastructure applications has great potential to improve sustainability and energy efficiency by leveraging humans-in-the-loop strategy. However, human preference in regard to living conditions is usually unknown and heterogeneous in its manifestati…
CARDS improves decoding efficiency and alignment quality for LLMs.
problem Efficiency bottlenecks in decoding-time alignment for LLMs.
method Cascade Reward Sampling (CARDS) with segment-level rejection sampling and uncertainty-based segmentation.
result Significant improvement in decoding efficiency and alignment quality.
Enhances crypto-asset AMM with deep learning for better liquidity and efficiency.
problem Reduced slippage and improved liquidity in decentralized finance.
method Deep reinforcement learning for predicting market equilibrium and optimizing liquidity.
result Improved capital efficiency and reduced slippage for crypto-asset traders.
FCA improves fair clustering by optimizing utility and fairness.
problem Balancing fairness and utility in clustering.
method FCA alternates between aligning data and optimizing cluster centers in an aligned space.
result FCA achieves a superior trade-off between fairness and utility.
Theoretical limits on verifying self-improving systems without risking unbounded utility.
problem Formalizing and proving the limits of safety verification for self-improving systems.
method Developed dual conditions and used Holder's inequality, NP counting method, and Lipschitz bounds to establish impossibility and ceiling results.
result A classifier-based safety gate cannot simultaneously permit unbounded beneficial self-modification and bounded cumulative risk.
Paper solves portfolio problem using improved stochastic methods.
problem Finite horizon consumption-investment problem under stochastic factor framework.
method Proves existence of classical solution for semilinear equation using gradient estimates.
result Proves existence of classical solution and provides all necessary estimates.
New method calibrates noise for attack risk, improving ML model accuracy.
problem Improving accuracy of privacy-preserving ML models while maintaining privacy.
method Directly calibrates noise scale to a desired attack risk level, bypassing the standard ε-calibration. result Significantly decreases noise scale, leading to increased utility at the same risk level.
A new method calculates optimal decisions from classifier outputs, improving predictions in drug discovery.
problem Finding optimal decisions from classifier outputs in fields like medicine.
method Develops a transducer that calculates probabilities from classifier outputs, enabling expected-utility maximization.
result Improves prediction accuracy in drug discovery problems, sometimes close to theoretical maximum.
Enhances DP linear regression using public data moments.
problem Limited utility of traditional DP methods in linear regression.
method Transform private data using public second-moment matrix for a better OLSE.
result Improved accuracy and robustness of OLSE in DP linear regression.
In this paper, we consider efficient differentially private empirical risk minimization from the viewpoint of optimization algorithms. For strongly convex and smooth objectives, we prove that gradient descent with output perturbation not only achieves nearly optimal utility, but also significantly improves the running …
This paper improves privacy accounting in decentralized FL using f-Differential Privacy.
problem Challenges in accurately quantifying privacy budget in decentralized FL.
method Develops two new f-DP-based accounting methods for decentralized FL.
result Yields tighter (ε,δ) bounds and improved utility compared to existing methods.
VES-Gamma adapts EI using information-theoretic principles.
problem Optimizing black-box functions using Bayesian optimization.
method Variational Entropy Search (VES) and VES-Gamma algorithm.
result VES-Gamma improves EI by incorporating information-theoretic concepts.
In this paper, we diagnose deep neural networks for 3D point cloud processing to explore utilities of different intermediate-layer network architectures. We propose a number of hypotheses on the effects of specific intermediate-layer network architectures on the representation capacity of DNNs. In order to prove the hy…
Improves causal graph learning on dependent binary data.
problem Challenges in learning causal graphical models from dependent binary data.
method Decorrelation-based approach using latent utility model and EM-like algorithm.
result Significant improvement in accuracy of causal graph learning.
In the field of multi-objective optimization algorithms, multi-objective Bayesian Global Optimization (MOBGO) is an important branch, in addition to evolutionary multi-objective optimization algorithms (EMOAs). MOBGO utilizes Gaussian Process models learned from previous objective function evaluations to decide the nex…
Novel privacy model for decentralized data analysis.
problem Achieving good privacy-utility trade-off in federated learning.
method Introducing network Differential Privacy (network DP) for decentralized algorithms.
result Privacy-utility trade-offs of network DP algorithms significantly improve upon LDP and trusted curator model.
Adversaries with multiple antennas can fool deep learning modulators more effectively.
problem Improving evasion attacks on deep learning-based modulation classifiers.
method Utilizing multiple antennas to enhance adversarial attacks on deep learning classifiers.
result Adversarial attacks with multiple antennas significantly improve classifier accuracy.
Negative user preference is an important context that is not sufficiently utilized by many existing recommender systems. This context is especially useful in scenarios where the cost of negative items is high for the users. In this work, we describe a new recommender algorithm that explicitly models negative user prefe…
We present a fast and scalable algorithm to induce non-monotonic logic programs from statistical learning models. We reduce the problem of search for best clauses to instances of the High-Utility Itemset Mining (HUIM) problem. In the HUIM problem, feature values and their importance are treated as transactions and util…