Efficiently estimates conversion probability in online display advertising.
problem Estimating conversion probability with delays and large data sets.
method Compromise estimator combining logistic regression and joint model.
result Computational efficiency with less bias than previous methods.
Study proposes a time-aware model to predict user conversion intent.
problem Weak predictive signals from users not suitable for conversion prediction.
method Time-aware approach to model user activities and capture conversion intent signals.
result Approach outperforms other models on real-world datasets.
New attribution model boosts ad bidding efficiency.
problem Inefficiency of standard bidding policies in ad exchanges.
method Developed and applied an attribution model within the bidder.
result Average bid increased after incorporating attribution model.
A method corrects feedback shift in predicting conversion rates with delayed feedback.
problem Delayed feedback leads to mislabeling of positive instances in training data.
method Uses importance weight approach to correct feedback shift.
result Proposed method outperforms existing methods in offline and online experiments.
New method constructs confidence sets for GLMs via game theory.
problem Developing reliable confidence intervals for GLM parameters.
method Reduction to sequential prediction games with low regret.
result Online-to-confidence-set conversions provide new types of intervals.
Estimates conversion probabilities from click sequences with privacy constraints.
problem Training models in advertising with limited direct click-conversion links.
method Formalizes learning from attribution sets, constructs unbiased estimator, applies Empirical Risk Minimization.
result Empirical Risk Minimization achieves generalization guarantees and robustness against prior errors.
Dual learning algorithm addresses delayed conversions in CVR prediction.
problem Challenges in predicting conversion rate due to delayed feedback.
method Proposes two unbiased estimators and a dual learning algorithm.
result Demonstrates practical value of the proposed approach through empirical evaluations.
Study uses time series analysis to predict player churn and conversion in games.
problem Predicting player churn and conversion in free-to-play games.
method State Space time series approach with Autoregressive Integrated Moving Average and Unobserved Components models.
result Unobserved Components approach fails to detect marketing campaigns and predicts abandonment poorly.
Study predicts ad conversions based on URL embeddings.
problem Predicting user conversion likelihood from visited URLs.
method Introduced URL embedding models and evaluated conversion prediction models.
result Demonstrated effectiveness of URL embedding models for conversion prediction.
Adaptive policies solve a linear program to maximize rewards while minimizing costs in sales with discounts.
problem Maximizing rewards in sales with discounts while considering costs.
method Solves a linear program based on upper-confidence estimates of conversion probabilities.
result Achieves a regret bound of the typical order (OPT/ B B B ) T \sqrt{T} T , where B is the total budget allowed. Paper proposes RL for efficient MCI diagnosis from dialogue data.
problem Efficiently diagnose MCI from conversational data with minimal interaction.
method Reinforcement learning framework trained on clinical trial transcripts.
result Significantly outperforms supervised learning approaches with minimal conversation turns.
Enhanced transformer converts whispered speech to natural speech.
problem Machine recognition of whispered speech is challenging.
method Proposes an enhanced transformer architecture trained end-to-end using supervised learning.
result Similar formant distributions of converted speech to groundtruth.
In this paper, we propose a dictionary update method for Nonnegative Matrix Factorization (NMF) with high dimensional data in a spectral conversion (SC) task. Voice conversion has been widely studied due to its potential applications such as personalized speech synthesis and speech enhancement. Exemplar-based NMF (ENMF…
This paper uses Gaussian Process and converse Lyapunov function to estimate power system ROA.
problem Estimating the region of attraction (ROA) for power systems with conservative and limited analytical methods.
method Combining converse Lyapunov theorem and Gaussian Process to estimate ROA without needing an analytic Lyapunov function.
result The approach can significantly enlarge the estimated ROA compared to analytical methods.
I prove that if markets are weak-form efficient, meaning current prices fully reflect all information available in past prices, then P = NP, meaning every computational problem whose solution can be verified in polynomial time can also be solved in polynomial time. I also prove the converse by showing how we can "progr…
A new calibration metric bridges testability and actionability.
problem Combining testability and actionable insights for forecast probabilities.
method Cutoff Calibration Error (CCE) that assesses calibration over intervals of forecasted probabilities.
result Cutoff Calibration Error is both testable and actionable.
DHEN improves CVR prediction for ads with multitask learning and auxiliary loss.
problem Predicting conversion rates in ad-recommendation systems.
method DHEN integrates multiple feature-crossing modules and uses a multitask learning framework, ablation studies, and self-supervised auxiliary loss.
result DHEN achieves state-of-the-art performance in CVR prediction.
Wavelet-based online learning adapts to noisy Besov spaces with high probability.
problem Minimizing integrated squared error in Besov spaces with noisy observations.
method Adaptive wavelet-based online learning algorithm that dynamically adjusts to gradient noise.
result Achieves minimax-optimal integrated squared error with high probability.
The support recovery problem consists of determining a sparse subset of a set of variables that is relevant in generating a set of observations, and arises in a diverse range of settings such as compressive sensing, and subset selection in regression, and group testing. In this paper, we take a unified approach to supp…
This paper introduces a Bayesian framework for optimizing online experiments to maximize profit.
problem Statistical flaws and reliance on proxy metrics in A/B tests compromise their effectiveness.
method Hierarchical Bayesian model for estimating conversion probability and monetary value, decision-theoretic stopping rule.
result The framework ensures experiments conclude when no variant offers a significant profit improvement, conserving resources.
Paper improves conversion prediction models for online advertising.
problem Predicting different types of conversions in online advertising.
method Multi-Task Learning with MT-FwFM.
result Improved AUC by 0.74% and 0.84% on two conversion types, and overall AUC by 0.50%.
Study examines cash conversion cycle in manufacturing firms, finding negative relationships with profitability and size.
problem Understanding cash conversion cycle in manufacturing firms and its impact on profitability and size.
method Empirical study of 30 manufacturing firms in Dhaka Stock Exchanges, categorizing them into six industries, analyzing industry averages and relationships with size and profitability.
result Negative relationship between cash conversion cycle and profitability, especially ROE; negative relationship with firm size in terms of net sales.
Framework converts singer identity and vocal technique from non-parallel corpora.
problem Converts singer identity and vocal technique from non-parallel corpora.
method Uses variational autoencoders with separate encoders for singer identity and vocal technique.
result Successfully disentangles and converts singer identity and vocal technique.
FasterVoiceGrad speeds up VC by 6-7x with novel distillation.
problem Slow iterative sampling in diffusion-based VC models.
method Adversarial diffusion conversion distillation (ADCD) to create a faster one-step model.
result 6.6-6.9 and 1.8x faster on GPU and CPU, respectively.
Markov networks (MNs) are a powerful way to compactly represent a joint probability distribution, but most MN structure learning methods are very slow, due to the high cost of evaluating candidates structures. Dependency networks (DNs) represent a probability distribution as a set of conditional probability distributio…
Conversational UCB accelerates bandit learning with user feedback.
problem Slow learning speed in traditional contextual bandit algorithms.
method Generalized contextual bandit to conversational contextual bandit, leveraging both behavioral and conversational feedbacks.
result ConUCB achieves a smaller regret upper bound, indicating faster learning speed.
Algorithm recovers spike direction from modulo-reduced measurements in high dimensions.
problem Recovering spike direction from modulo-reduced measurements in high-dimensional space.
method Developed an algorithm for estimating the spike direction using modulo-reduced measurements.
result Algorithm accurately estimates the spike direction with n = p o l y ( k ) n=\mathrm{poly}(k) n = poly ( k ) measurements when Δ ≳ log k Δ\gtrsim \sqrt{\log k} Δ ≳ log k . A new model predicts conversion rates by analyzing post-click actions.
problem Challenges in predicting conversion rates due to sample selection bias and data sparsity.
method Post-click behavior decomposition and multi-task learning.
result The model effectively addresses sample selection bias and data sparsity issues.
Automated system extracts medication regimens from medical conversations.
problem Extract relevant medication information from medical conversations.
method QA task approach, combined QA and Information Extraction, data augmentation, public embeddings, pretraining.
result Improved accuracy in extracting dosage and frequency from 54.28 and 37.13 to 89.57 and 45.94.
The paper shows a phase transition for the existence of MLE in high-dimensional logistic regression.
problem The existence of the maximum likelihood estimate in high-dimensional logistic regression models.
method Established a phase transition boundary curve h e x t M L E h_{ ext{MLE}} h e x t M L E parameterized by scalars measuring the magnitude of regression coefficients. result The existence of the MLE in high-dimensional logistic regression models undergoes a sharp phase transition.
Improved speech recognition for medical conversations.
problem Transcribing accurate doctor-patient conversations.
method Collected a large dataset, designed realistic tasks, explored alignment and model types (CTC and LAS).
result LAS models were more resilient to noisy data, while CTC required more data cleanup.
FedConPE improves conversational recommender systems efficiency and privacy.
problem Efficiently eliciting user preferences in interactive systems with heterogeneous clients.
method Phase elimination-based federated conversational bandit algorithm with adaptive key term construction.
result Minimizes uncertainty across all dimensions in feature space and offers improved efficiency and privacy.
Solution for voice conversion with limited data using hierarchical seq2seq and attention models.
problem Voice conversion between speakers with limited parallel audio pairs.
method Hierarchical sequence to sequence model with attention-based decoder, trained on single speaker dataset.
result Improved voice conversion quality using mel spectrograms and wavenet vocoder.
Proposes a multi-state model for evaluating life insurance conversion options.
problem Evaluating the value of conversion options in life insurance contracts.
method Age-indexed semi-Markov chains to model duration, time non-homogeneity, and ageing effects.
result Validates the model's ability to accurately evaluate conversion option values.
Improved voice conversion with semi-supervised learning.
problem Voice conversion with limited parallel data.
method Amortized variational inference with parallel and non-parallel utterances.
result Semi-supervised training improves voice conversion performance.
Improved SAR in asynchronous conversations using neural models and unlabeled data.
problem Lack of labeled data for SAR in asynchronous conversations.
method Hierarchical LSTM-CRF model, semi-supervised learning with word embeddings, adversarial training.
result Adversarial training improves SAR performance by leveraging labeled data from synchronous domains.
CycleGAN-VC3 improves CycleGAN-VCs for mel-spectrogram conversion.
problem Ambiguity in CycleGAN-VC/VC2 effectiveness for mel-spectrogram conversion.
method Proposes CycleGAN-VC3 with time-frequency adaptive normalization (TFAN).
result CycleGAN-VC3 outperforms or matches CycleGAN-VC2 for mel-spectrogram conversion.
ConvS2S-VC converts voice characteristics and pitch contour using a fully convolutional seq2seq model.
problem Voice conversion with preservation of pitch contour and duration.
method Fully convolutional seq2seq architecture with conditional batch normalization.
result ConvS2S-VC outperforms baseline methods in sound quality and speaker similarity.
A new algorithm for conversational recommendation systems using dueling bandits in GLMs.
problem Limited user feedback in existing conversational bandit methods.
method Integrates dueling bandits with relative feedback in generalized linear models.
result Theoretical and empirical validation of ConDuel's efficacy.
The paper extracts structured data from physician-patient conversations, reducing clerical burden.
problem Mining insights from physician-patient conversations for electronic health record documentation.
method Created a dataset of transcripts and summaries, extracted noteworthy utterances, and improved model performance.
result Extracting noteworthy utterances significantly boosts model performance for recognizing diagnoses and RoS abnormalities.
Schedule-free SGD is optimal for nonconvex optimization problems.
problem Nonconvex optimization in neural networks.
method Developed a general framework for online-to-nonconvex conversion, which converts schedule-free SGD into an effective nonconvex optimization algorithm.
result Schedule-free SGD achieves optimal iteration complexity for nonsmooth, nonconvex optimization problems.
Improved autoencoder for F0-consistent voice conversion.
problem Non-parallel many-to-many voice conversion with prosodic information leakage.
method Conditional autoencoder with information-constraining bottlenecks.
result Controlled F0 contour and improved speech quality.
Bi-Lipschitz flows approximate a wide range of distributions.
problem Characterizing the expressivity of bi-Lipschitz normalizing flows.
method Linking score regularity to transport map bi-Lipschitzness via probability flow ODE.
result Gaussian pullbacks induced by bi-Lipschitz variance-preserving transport maps are L 1 L^1 L 1 -dense among all probability densities. Improved conversion rate prediction in online advertising using self-supervised pre-training.
problem Data sparsity and calibration issues in predicting conversions given clicks.
method Self-supervised pre-training on all conversion events to enrich CVR prediction model without compromising calibration.
result Improvements in offline training and online A/B tests, with full deployment to Yahoo native advertising system.
A fast voice conversion method using diffusion models.
problem One-shot many-to-many voice conversion.
method Diffusion probabilistic modeling with Fast Maximum Likelihood Sampling Scheme.
result Superior quality compared to state-of-the-art approaches.
Proposes a new voice conversion model that preserves pitch patterns.
problem Preserving pitch patterns while changing speaker identity.
method Variational-autoencoder-based model with an auxiliary network.
result Ensures the conversion result correctly reflects specified F0/timbre information.
Study optimizes tree-based models for better alignment of predicted scores and actual probabilities.
problem Traditional calibration metrics fail to align predicted scores with actual probabilities when score distributions deviate from the underlying data.
method Optimizes tree-based models (Random Forest, XGBoost) using Kullback-Leibler (KL) divergence to minimize the difference between predicted and true probability distributions.
result Optimized tree-based models yield superior alignment between predicted scores and actual probabilities without significant performance loss.
New method detects business-relevant outliers in e-commerce conversion rates.
problem Identifying outliers in e-commerce conversion rate data.
method A novel unsupervised fluid IQR method that adjusts sensitivity based on platform activity.
result Fluid IQR method outperforms existing methods in business-relevance and robustness.