This paper explores how interactive protocols enhance local differential privacy.
problem The power of interactive protocols in local differential privacy.
method Classification of locally private protocols by compositionality and transformation of fully interactive protocols into sequentially interactive ones.
result Fully interactive protocols can solve problems that sequentially interactive protocols cannot without a significant increase in sample complexity.
Proves exponential sample complexity separations in local differential privacy.
problem Sample complexity in locally private protocols.
method Connection between communication complexity and sample complexity, using specific lower bounds for two problems.
result Exponential separations between differentially private protocols.
Locally private Gaussian estimation tackles privacy in i.i.d. sample mean estimation.
problem Estimating the mean of an unknown Gaussian distribution while maintaining local differential privacy for each user.
method Adaptive two-round and nonadaptive one-round solutions for locally private Gaussian estimation.
result Upper bounds partially match with information-theoretic lower bounds, showing tightness up to logarithmic factors.
Improved distributed learning with reduced communication costs.
problem Efficient communication in resource-constrained environments for distributed learning.
method Proposed a cost-effective partial communication protocol.
result Communication cost is reduced to O(logT), improving significantly on full communication. New protocols show 1-bit mean estimation can be order-optimal without interaction.
problem Can 1-bit mean estimation be optimal without interaction?
method Adaptive and non-adaptive threshold and interval queries, with one adaptive transition.
result Arbitrary non-adaptive quantizers can match the adaptive rate, suggesting interaction is not necessary.
Proposes a credit scoring system for Aave accounts.
problem Scoring the creditworthiness of DeFi accounts.
method Tree-based binary classifier for predicting delinquency.
result Validated the credit scoring system for Aave accounts.
A study on the communication complexity of estimating correlations between variables.
problem Estimating the correlation between two sets of correlated random variables with limited communication.
method One-way interactive protocol exchanging k bits, optimizing over interaction protocol and estimator.
result Achieves optimal performance with communication complexity of 1/k, improving over naive schemes.
Interactive topic models with user-defined anchor words.
problem Building interpretable topic models.
method Protocol allowing users to interactively define anchor words for topic modeling.
result Validation of the usefulness of interactive topic models.
We propose a probabilistic formulation that enables sequential detection of multiple change points in a network setting. We present a class of sequential detection rules for certain functionals of change points (minimum among a subset), and prove their asymptotic optimality properties in terms of expected detection del…
A new method for identifying the best arm in multi-armed bandits with mediator feedback.
problem Sequential decision-making with unknown mediator policies.
method Best-arm identification under mediators' feedback (BAI-MF) with statistical lower bound and sequential decision-making strategy.
result The proposed algorithm matches the lower bound on sample complexity for identifying the best arm.
The paper introduces a protocol for including advice in robot interaction to improve performance.
problem Improving natural language interaction between humans and robots.
method Protocol for including advice in robot interaction, evaluating on blocks world task.
result Simple advice can lead to significant performance improvements in robot interaction.
R-SQAIR adds relational bias to sequential object attention models for better object interactions.
problem Traditional sequential multi-object attention models struggle with relational inferences.
method Proposes R-SQAIR, a relational extension of SQAIR with a parallel pairwise interaction module.
result Demonstrates gains in object relations and combinatorial generalization over sequential mechanisms.
New protocol evaluates synthetic data for temporal consistency.
problem Synthetic data generators can produce invalid timestamps and trajectories.
method Characterize datasets by four properties, then measure timestamp validity and dynamics.
result Temporal fidelity must be measured, not inferred from static data.
Protocol learns pure quantum states with minimal disturbance.
problem Efficiently learn quantum states with minimal disturbance.
method Sequential measurements with minimal disturbance.
result Achieves maximal precision with polylogarithmic regret.
New protocol reduces communication costs for heterogeneous bandits over complex networks.
problem Minimizing group regret in a multi-agent, heterogeneous bandit setting over complex networks.
method Flooding with Absorption (FwA) protocol for heterogeneous bandits over complex networks.
result FwA protocol significantly reduces communication costs compared to flooding while maintaining similar regret performance.
Survey of yield farming protocols in DeFi.
problem Understanding and evaluating yield farming mechanisms in DeFi.
method Analyzed smart contracts, performed simulations, reviewed literature.
result Characterized major yield aggregators and identified risks.
Paper develops PAC verification for hypothesis classes and statistical algorithms.
problem Verifying machine learning models interactively.
method Develops interactive proof for PAC verification, proves lower bounds, and introduces a generalization.
result Improved protocol for verifying unions of intervals and statistical query algorithms.
New model accounts for sequential dependence in LLM reliability.
problem Uncertainty in LLM reliability assessment due to sequential interactions.
method Extended Bayesian framework with Hidden Markov Model for sequential dependence.
result Ignoring sequential dependence leads to overconfident reliability estimates.
Deep model learns protein interfaces from high-order interactions.
problem Predicting protein interfaces from amino acid pairs.
method Graph neural networks and convolutional neural networks for 2D dense predictions.
result Our method consistently improves interface prediction performance.
Objective: Anemia is a frequent comorbidity in hemodialysis patients that can be successfully treated by administering erythropoiesis-stimulating agents (ESAs). ESAs dosing is currently based on clinical protocols that often do not account for the high inter- and intra-individual variability in the patient's response. …
New robust algorithms improve learning with feature feedback.
problem Interactive learning with discriminative feature feedback.
method Developed new robust interactive learning algorithms with improved mistake bounds.
result Achieved significantly lower mistake bounds in adversarial and stochastic settings.
Paper sets fundamental limits for distributed covariance estimation with constrained communication.
problem Estimating high-dimensional covariance matrices in a feature-split setting with limited communication.
method Developed a Conditional Strong Data Processing Inequality (C-SDPI) to establish minimax lower bounds and an optimal estimation protocol.
result Achieved nearly optimal estimation protocol with sample and communication requirements matching lower bounds up to logarithmic factors.
The paper proposes a new method to improve microcredit decisions by modeling sequential loan interactions.
problem Improving microcredit decision-making by addressing population bias and model generalization.
method The authors introduce a multi-stage interaction sequence (MSIS) method that models sequential loan interactions and uses a hierarchical attention module to leverage interaction information.
result The MSIS method effectively remedies population bias and improves model generalization on a real loan data set.
AriaNN enables private deep learning with minimal interaction and reduced key sizes.
problem Private deep learning with minimal interaction and reduced key sizes.
method Semi-honest 2-party computation protocol with function secret sharing, optimized primitives for neural network operations.
result Efficient private comparison for ReLU operations with reduced key size and improved performance.
SeqAttnGAN generates interactive images based on multi-turn text descriptions.
problem Interactive image editing with multi-turn textual commands.
method SeqAttnGAN uses a neural state tracker and GAN framework for sequential image generation and refinement.
result SeqAttnGAN outperforms state-of-the-art models on interactive image editing tasks.
Improves memory robustness in RNNs for sequential data.
problem Improving memory robustness in RNNs for sequential data processing.
method Utilized various training protocols, datasets, and architectures to analyze hidden state dynamics and propose a regularization technique.
result Manipulating hidden state speeds improves memory robustness over time.
Interactive machine learning improves learning efficiency with user input.
problem Expensive, time-consuming, or risky acquisition of labeled data and decision-making.
method Develops new algorithms for active learning, sequential decision making, and model selection under partial feedback.
result First efficient algorithms achieving exponential label savings and independent of action space size.
We describe theoretical bounds and a practical algorithm for teaching a model by demonstration in a sequential decision making environment. Unlike previous efforts that have optimized learners that watch a teacher demonstrate a static policy, we focus on the teacher as a decision maker who can dynamically choose differ…
A new method uses CPD to efficiently model feature interactions in non-sequential data.
problem Efficiently modeling feature interactions in non-sequential data with high computational and memory costs.
method Implicitly represent model parameters as a tensor, factorize into a compact Tensor Train (TT) format, and use Canonical Polyadic (CP) Decomposition for invariance to feature ordering.
result The proposed CP-based predictor outperforms other TN-based predictors on sparse data and matches neural network performance on dense non-sequential tasks.
Enhances quantum sensing by eliminating multiple oscillations in field amplitude estimation.
problem Multiple oscillations in field amplitude estimation due to inter-qubit interactions at high qubit densities.
method Adopting a quantum circuit learning framework to approximate a target function by optimizing gate parameters.
result Elimination of multiple oscillations, leading to enhanced dynamic range of quantum sensing.
SeqFM models dynamic and sequential features for better predictive analytics.
problem Inadequate handling of sequential dependencies in existing FM-based models.
method Introduces SeqFM, a novel model that incorporates multi-view self-attention to model static, dynamic, and their interactions.
result SeqFM outperforms existing models in ranking, classification, and regression tasks on six large-scale datasets.
New method for evaluating sequential recommendations with lower variance.
problem Evaluating good sequences of music, video, news, and e-commerce recommendations.
method Proposes a new counterfactual estimator for sequential reward interactions with lower variance and asymptotic unbiasedness.
result Our method outperforms existing methods in bias and data efficiency for sequential track recommendations.
We study the quantum synchronization between a pair of two-level systems inside two coupled cavities. By using a digital-analog decomposition of the master equation that rules the system dynamics, we show that this approach leads to quantum synchronization between both two-level systems. Moreover, we can identify in th…
New algorithm for sequential off-policy learning improves performance over batch methods.
problem Training policies from logged interaction data in a sequential setting.
method Combines Logarithmic Smoothing with online PAC-Bayesian tools.
result Improves performance and accelerates convergence in sequential off-policy learning.
Study shows no DNN avoids catastrophic forgetting in real-world tasks.
problem Catastrophic forgetting in Deep Neural Networks (DNNs).
method Empirical study on large-scale visual classification datasets, enforcing typical application constraints.
result No model avoids catastrophic forgetting across all datasets and tasks under application conditions.
New protocols improve privacy in counting and selection problems with multiple messages.
problem Improving privacy in counting and selection problems with multiple messages.
method Analyzed frequency estimation and selection problems in the shuffled model with multiple messages per user.
result Protocols with multiple messages achieve exponential improvements in error compared to single-message protocols.
A CNN integrates with Markov random fields for brain segmentation.
problem Combining CNNs and probabilistic models for robust brain segmentation.
method Backpropagation-based recurrent CNN for spatial interactions in Markov random fields.
result Model generalizes across different imaging protocols.
Machine learning employs dynamical algorithms that mimic the human capacity to learn, where the reinforcement learning ones are among the most similar to humans in this respect. On the other hand, adaptability is an essential aspect to perform any task efficiently in a changing environment, and it is fundamental for ma…
ComEx protocol reduces communication costs in cooperative bandits.
problem Minimizing communication costs in cooperative bandits while maintaining optimal performance.
method Developed ComEx protocol to reduce communication from Θ(T) to O(logT) messages. result Achieves state-of-the-art performance with significantly reduced communication cost.
A new dataset tracks user interactions and click responses in online marketplaces.
problem Lack of exposure data in recommender systems datasets.
method Proposes a novel dataset including slates and click responses, allowing more accurate likelihood models.
result Models using exposure data show more natural likelihood, reducing bias towards previously exposed items.
New model for personalized online advertising with multi-user interaction.
problem Realistic online advertising scenarios with multiple users interacting simultaneously.
method Introduces Multi-User Contextual Cascading Bandit (MCCB) model and proposes UCBBP and AUCBBP algorithms.
result Proves UCBBP and AUCBBP achieve optimal regret bounds for multi-user context.
Interactive machine comprehension models learn through seeking relevant information.
problem Existing MRC models struggle with real-world applications due to static datasets.
method Reframe MRC datasets as interactive, partially observable environments.
result Interactive models can seek relevant information through sequential decision making.
Variable selection for optimal treatment regime in a clinical trial or an observational study is getting more attention. Most existing variable selection techniques focused on selecting variables that are important for prediction, therefore some variables that are poor in prediction but are critical for decision-making…
The paper explores privacy-preserving methods for counting unique elements in distributed settings.
problem Counting unique elements in a distributed setting while maintaining privacy.
method Analyzes and proves lower bounds for differentially private protocols in various settings.
result Achieves optimal error bounds for multi-message shuffle protocols in estimating distinct elements.
LLM agents discover cryptocurrency factors under reproducible constraints.
problem Flexibility of LLM agents in empirical discovery leads to uncontrolled search.
method Sequential hypothesis search with fixed data splits and portfolio tests.
result Ridge-combined portfolio achieves 44.55% annualized return in out-of-sample period.
Estimates LRD in sequential data, improving RNNs.
problem Quantifying LRD in sequential data for better RNNs.
method Principled estimation procedure based on LRD theory for real-valued time series.
result Estimates LRD reliably in user behavior and Wikipedia article writing.
Interactive IL beats BC by state-wise annotation cost.
problem Behavior Cloning struggles with annotation cost in sequential decision making.
method Proved Stagger and Warm Stagger algorithms to outperform BC.
result Interactive and hybrid IL methods outperform BC with state-wise annotation.
TIMeSynC combines financial service interactions for intent prediction.
problem Aligning and learning from multi-domain, multi-resolution sequences for accurate intent prediction.
method An encoder-decoder transformer model addressing sequence alignment, temporal dynamics, and dynamic/static sequence combination.
result Significant improvement in intent prediction over existing methods.