TWM doesn't reduce delta in PDLPs, proving impossibility.
problem TWM in PDLPs doesn't uniformly reduce portfolio delta.
method Proved TWM's condition is self-contradictory and showed impossibility.
result No TWM can uniformly reduce portfolio delta.
Adaptive sampling method improves efficiency in complex target distributions.
problem Efficiency of importance sampling in complex target distributions, especially multimodal distributions in high-dimensional spaces.
method Proposes an adaptive scheme combining global sampling with delayed weighting to promote efficient exploration of target distributions.
result The proposed algorithm is geometrically convergent under mild assumptions and demonstrates improved efficiency in various numerical experiments.
Optimal weights improve particle-based approximations of discrete distributions.
problem Improving particle-based approximations of discrete distributions.
method Proving optimality of weights and showing how to compute them efficiently.
result Optimal weights can be computed from existing particle-based methods without extra costs.
Matrix completion is a modern missing data problem where both the missing structure and the underlying parameter are high dimensional. Although missing structure is a key component to any missing data problems, existing matrix completion methods often assume a simple uniform missing mechanism. In this work, we study ma…
Long-term prediction of multivariate time series is still an important but challenging problem. The key to solve this problem is to capture the spatial correlations at the same time, the spatio-temporal relationships at different times and the long-term dependence of the temporal relationships between different series.…
New estimators improve efficiency in two-phase designs with coarsened data.
problem Efficient estimation in two-phase designs with incomplete data.
method Developed new estimators within the TMLE framework.
result New estimators are asymptotically equivalent and more efficient.
MPTE uses Transformer attention to estimate mixed-frequency factor models.
problem Estimating factor models in panel datasets with mixed frequencies and nonlinear signals.
method Mixed-Panels-Transformer Encoder (MPTE) with attention mechanisms.
result MPTE achieves competitive performance in nonlinear forecasting environments.
Conformal Bayes under label shift: post-hoc calibration vs. in-training adaptation
problem Bayesian prediction sets under label shift
method Post-hoc calibration vs. In-training adaptation
result Both strategies achieve valid coverage equally in an unbiased training regime
Single-head attention approximates any function under various norms.
problem Universal approximation of functions using attention mechanisms.
method Interpreting attention as partitioning and summing linear transformations.
result Single-head attention can approximate any continuous function under L∞-norm and Lebesgue integrable functions under Lp-norm. Two approaches improve conformal Bayes for label shift, one post-hoc and one in-training.
problem Improving prediction sets for target domain under label shift.
method Two complementary approaches: post-hoc calibration and in-training adaptation.
result In-training adaptation achieves up to 43% width reduction at unchanged coverage.
Statistical mechanics explains learning in large neural networks near interpolation.
problem Understanding the learning dynamics of large neural networks near interpolation.
method Statistical physics analysis of a two-layer network with generic weight distribution and activation function.
result Learning transitions and feature learning emerge as the number of data increases.
New method uses weighted SDEs to improve sampling from complex distributions.
problem Sampling from highly non-log-concave distributions.
method Introduces weighted stochastic differential equations to augment diffusion-based samplers.
result Demonstrates improved exploration of nonconvex or multimodal landscapes.
Paper proposes mechanism learning to reverse causal inference in ML.
problem Machine learning models learn associational, not causal, relationships.
method Causally weighted Gaussian mixture models (CW-GMMs).
result CW-GMMs can deconfound observational data for reverse causal inference.
SNF combines stochastic and deterministic steps to sample complex distributions.
problem Sampling complex probability distributions efficiently.
method Stochastic Normalizing Flows (SNF) - sequence of invertible functions and stochastic blocks.
result SNFs improve efficiency and representational power over pure MCMC/LD.
Recurrent neural networks have been widely used to generate millions of de novo molecules in a known chemical space. These deep generative models are typically setup with LSTM or GRU units and trained with canonical SMILEs. In this study, we introduce a new robust architecture, Generative Examination Networks GEN, base…
A new method aligns source and target distributions by tuning their weights.
problem Domain adaptation on unlabeled target datasets using labeled source datasets.
method Weighted Joint Distribution Optimal Transport (WJDOT) method that finds alignment between source and target distributions and re-weighting of source distributions.
result Achieves state-of-the-art performance on simulated and real-life datasets.
A new meta-learning framework that assigns weights to source tasks based on target samples.
problem Learning initialization for target tasks with limited labeled examples.
method A general framework that assigns weights to the loss of different source tasks, which can depend on the target samples. Provides upper bounds and develops a learning algorithm based on minimizing the error bound with respect to an empirical IPM.
result Empirically, the weighted meta-learning algorithm finds better initializations than uniformly-weighted meta-learning algorithms.
Corrects distribution shift in target shift scenarios using importance weighting.
problem Analyzes importance weighting for correcting distribution shift under target shift.
method Analyzed importance-weighted kernel ridge regression under target shift.
result Shows that importance weighting corrects the train-test mismatch without altering input-space complexity.
Recent years have seen an emerging class of structured financial products based on options linked to dynamic asset allocation strategies. One of the most chosen approach is the so-called target volatility mechanism. It shifts between risky and riskless assets to control the volatility of the overall portfolio. Even if …
A new method corrects bias in causal inference by balancing covariate distributions.
problem Bias in causal effect estimates due to covariate imbalance.
method Causal Optimal Transport, a nonparametric approach targeting distributional balance.
result Causal Optimal Transport outperforms other methods in misspecified models.
T-BFA targets and misleads specific DNN inputs to a chosen output.
problem Targeted attack on DNN weight parameters to hijack function.
method Identifies critical weight bits, ranks them by class dependence, and flips them to mislead inputs.
result Successfully misclassifies images from 'Hen' to 'Goose' class with 100% success rate, maintaining 59.35% validation accuracy.
The paper reveals three mechanisms for weak-to-strong generalization.
problem Understanding the mechanisms behind weak-to-strong generalization in imperfect labeling scenarios.
method Theoretical analysis of simple models including ridge regression and weighted ridge regression, and a nonlinear multi-index setting.
result A student model can compensate for a teacher's under-regularization and achieve lower test error.
Adapts conformal prediction for missing data, ensuring valid coverage.
problem Uncertainty quantification with missing covariates.
method Proposes a reweighted conformal prediction procedure for handling missing values.
result Guaranteed Marginal Coverage and Mask-Conditional Validity for general missing data mechanisms.
AI agents in experimental markets exhibit behavioral patterns that aggregate into market dynamics.
problem Understanding AI trading behavior and its impact on market dynamics.
method Experimental asset markets populated by AI agents trained on Large Language Models (LLMs).
result AI agents' behavior leads to market dynamics similar to human traders, including bubbles.
EWS-GCN improves credit scoring by analyzing money transfer connections.
problem Improving credit scoring in transactional banking data.
method Edge Weight-Shared Graph Convolutional Network (EWS-GCN) combining graph and recurrent neural networks.
result EWS-GCN outperforms state-of-the-art models in credit scoring.
Develops a weighting framework to generalize ITRs from source to target populations.
problem Challenges in generalizing ITRs from a source population to a target population with differing characteristics.
method A robust sample weighting framework using a reproducing kernel Hilbert space to balance covariates and improve ITR learning methods.
result Improves ITR estimation for the target population compared to other weighting methods.
New mechanism protects neural network weights from privacy attacks during self-supervised learning.
problem Privacy risks during fine-tuning stage of self-supervised learning.
method Proposes a novel differential privacy mechanism using additive logistic noise.
result Reduces membership inference attack accuracy to 50% while maintaining below 5% performance loss.
A new type of End-to-End system for text-dependent speaker verification is presented in this paper. Previously, using the phonetically discriminative/speaker discriminative DNNs as feature extractors for speaker verification has shown promising results. The extracted frame-level (DNN bottleneck, posterior or d-vector) …
This paper presents a new family of backpropagation-free neural architectures, Gated Linear Networks (GLNs). What distinguishes GLNs from contemporary neural networks is the distributed and local nature of their credit assignment mechanism; each neuron directly predicts the target, forgoing the ability to learn feature…
RoGAT enhances GAT robustness against adversarial attacks.
problem Vulnerability of GAT to adversarial attacks.
method Dynamic adjustment of edge weights and features, with an extra attention score.
result RoGAT outperforms other defensive methods in robustness tests.
This paper analyzes how periodic and soft target updates stabilize linear Q-learning.
problem Theoretical explanation of stabilization mechanisms for linear Q-learning.
method Exact analysis using switched linear system dynamics and the joint spectral radius.
result Periodic and soft target updates can guarantee convergence to the exact projected Q-Bellman solution under specific conditions.
A new method normalizes flow mixtures for better inference across different data types.
problem Inference failure across diverse posterior geometries in normalizing flows.
method Introduces a two-stage framework with a stable global weighting mechanism based on sEMA.
result Achieves consistent NLL improvements and stable weight trajectories over baselines.
HiGraphDTI learns drug and target representations from molecular graphs to predict DTIs.
problem Inaccurate drug-target interaction prediction due to insufficient chemical information extraction.
method Hierarchical graph representation learning to extract chemical information from atoms, motifs, and molecules.
result HiGraphDTI outperforms state-of-the-art methods in DTI prediction and interaction interpretation.
PDLPs reduce borrowing costs for perpetual futures traders.
problem High capital costs for market makers and traders in decentralized settings.
method Formalize PDLPs and target weight mechanisms, describe pool arbitrage and expected payoffs.
result PDLPs are easy to delta hedge, improving capital efficiency.
Paper tackles hypothesis transfer learning for black-box models.
problem Difficult to build universal machine learning models across different institutions.
method Dynamic Knowledge Distillation (dkdHTL) with instance-wise weighting.
result Empirical results show the effectiveness of dkdHTL.
TSC improves causal effect estimation in panel data.
problem Estimating causal effects in panel data with a single treated unit.
method Targeted synthetic control method that refines initial weights through a one-dimensional targeted update.
result TSC consistently improves estimation accuracy over state-of-the-art SCM baselines.
New nonparametric estimators improve causal effect estimation.
problem Estimation of causal effects with selection bias.
method Undersmoothing of the highly adaptive lasso for estimating the weighting mechanism.
result Asymptotic efficiency and convergence to nonparametric efficiency bound.
Framework models supervised learning in non-stationary data.
problem Non-stationary data in supervised learning.
method Statistical physics methods applied to LVQ and neural networks.
result LVQ and ReLU have different sensitivity to concept drift.
Few-shot learning improves time-series forecasting with limited data.
problem Limited data in target tasks degrade forecasting performance.
method A few-shot learning method using recurrent neural networks with attention.
result The model forecasts future values effectively with minimal data.
This research integrates attention into XAI frameworks for better model explanations.
problem Improving the interpretability of transformer models.
method Developed two novel explanation methods: Shapley value decomposition and token-level directional derivatives.
result Attention weights can be meaningfully incorporated into XAI frameworks, enhancing transformer explainability.
Algorithm approximates target distribution using weight queries.
problem Approximating a distribution without sampling.
method Interactive algorithm using weight queries and UCB approach.
result Approximation bound on total variation distance.
Dynamic-weight AMMs outperform traditional CEX rebalancing in tokenized funds, especially on L2s.
problem Improving asset allocation efficiency in decentralized finance (DeFi) protocols.
method Block-level arbitrage analysis and long-term performance benchmarks on two live pools.
result Dynamic-weight AMMs can achieve performance comparable to or better than traditional CEX rebalancing, especially on Layer 2 (L2) networks.
OKDDip uses diverse peers to improve online knowledge distillation.
problem Early saturation in group-based distillation.
method Two-level distillation with multiple auxiliary peers and a group leader, using attention-based aggregation weights.
result OKDDip consistently gives better performance than state-of-the-art approaches.
Proposes a new ridge estimator for smooth covariates with adaptive centering.
problem Estimating coefficients and center function for smooth covariates in linear models.
method SACR framework with convex formulation, roughness penalty, and adaptive centering.
result Improves prediction and variable selection for smooth covariates.
Current algorithms for deep learning probably cannot run in the brain because they rely on weight transport, where forward-path neurons transmit their synaptic weights to a feedback path, in a way that is likely impossible biologically. An algorithm called feedback alignment achieves deep learning without weight transp…
Deep generative networks can simulate from a complex target distribution, by minimizing a loss with respect to samples from that distribution. However, often we do not have direct access to our target distribution - our data may be subject to sample selection bias, or may be from a different but related distribution. W…
Diffusion models accurately recover mixture weights from generated samples despite score function insensitivity.
problem Score-based generative models often fail to learn correct relative mode amplitudes (mixture weights) from generated samples.
method Relate diffusion score matching (DSM) loss to mixture weight estimation error, define diffusion score sensitivity index (DSSI), and prove its governing role in mixture weight recovery.
result Generated samples can accurately recover mixture weights from the DSM loss, even when the target score is insensitive to mixture weights.
Model predicts activist fund targets with 78.2% accuracy.
problem Predicting activist fund targets to mitigate risks and inform investments.
method Evaluated 123 model configurations using machine learning techniques.
result Best model achieved AUC-ROC of 0.782.