A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New algorithm reduces performance loss in IRL with mismatched transition dynamics.
Paper identifies objective mismatch in MBRL, affecting control task performance.
KalmanNet uses neural networks to improve state estimation in systems with unknown dynamics.
RFC enhances humanoid control to imitate complex human motions.
We study the question of how to imitate tasks across domains with discrepancies such as embodiment, viewpoint, and dynamics mismatch. Many prior works require paired, aligned demonstrations and an additional RL step that requires environment interactions. However, paired, aligned demonstrations are seldom obtainable an…
New state-only IL algorithm tackles MDP transition mismatch.
ITF improves DSR but inflates curvature, while marginal likelihood reduces it, affecting QoIs.
Study online RL with mismatched dynamics, achieving sublinear regret.
Dynamic Vocabulary Pruning stabilizes LLM training by removing low-probability tokens.
New algorithm solves Schrödinger bridge problem with mismatched channels.
We introduce a toy probabilistic model to analyze job-matching processes in recent Japanese labor markets for university graduates by means of statistical physics. We show that the aggregation probability of each company is rewritten by means of non-linear map under several conditions. Mathematical treatment of the map…
This paper considers the classification of linear subspaces with mismatched classifiers. In particular, we assume a model where one observes signals in the presence of isotropic Gaussian noise and the distribution of the signals conditioned on a given class is Gaussian with a zero mean and a low-rank covariance matrix.…
Framework identifies discrepancies in physics models, improving sensor accuracy.
The paper addresses score-mismatched diffusion models and zero-shot conditional samplers.
Neuromorphic Computing is a nascent research field in which models and devices are designed to process information by emulating biological neural systems. Thanks to their superior energy efficiency, analog neuromorphic systems are highly promising for embedded, wearable, and implantable systems. However, optimizing neu…
CycleFQI tackles offline reinforcement learning for cyclic MDPs, mitigating state distribution mismatch.
BinaryDuo improves BNNs by coupling binary activations, outperforming state-of-the-art models.
Mixup technique improved, reducing manifold mismatch for better calibration.
Thompson Sampling shows polynomial regret for combinatorial semi-bandits with subgaussian rewards.
New algorithm minimizes worst-case regret in uncertain, time-varying dynamics.
New method addresses error bounds for PnP-ULA under mismatched models.
Paper addresses linear regression with partially mismatched data using local search with theoretical guarantees.
PACMAN provides bounds for classification tasks considering accuracy vs. negative log-loss mismatch.
Current approaches for Knowledge Distillation (KD) either directly use training data or sample from the training data distribution. In this paper, we demonstrate effectiveness of 'mismatched' unlabeled stimulus to perform KD for image classification networks. For illustration, we consider scenarios where this is a comp…
Study on LMMSE estimation with model mismatch, quantifying MSE trade-offs.
Global hydrological and land surface models are increasingly used for tracking terrestrial total water storage (TWS) dynamics, but the utility of existing models is hampered by conceptual and/or data uncertainties related to various underrepresented and unrepresented processes, such as groundwater storage. The gravity …
Supervised learning based on a deep neural network recently has achieved substantial improvement on speech enhancement. Denoising networks learn mapping from noisy speech to clean one directly, or to a spectrum mask which is the ratio between clean and noisy spectra. In either case, the network is optimized by minimizi…
Recently, there has been significant interest in linear regression in the situation where predictors and responses are not observed in matching pairs corresponding to the same statistical unit as a consequence of separate data collection and uncertainty in data integration. Mismatched pairs can considerably impact the …
Ensemble models improve prediction calibration for mismatched distributions.
Framework optimizes battery storage for markets by separating long-term degradation from short-term market dynamics.
The dictionary-aided sparse regression (SR) approach has recently emerged as a promising alternative to hyperspectral unmixing (HU) in remote sensing. By using an available spectral library as a dictionary, the SR approach identifies the underlying materials in a given hyperspectral image by selecting a small subset of…
Empirical study compares finite- and infinite-width BNNs, revealing performance differences under model mismatch.
We characterize the performance of sequential information guided sensing, Info-Greedy Sensing, when there is a mismatch between the true signal model and the assumed model, which may be a sample estimate. In particular, we consider a setup where the signal is low-rank Gaussian and the measurements are taken in the dire…
We introduce a novel non-parametric methodology to test for the dynamical time evolution of the lag-lead structure between two arbitrary time series. The method consists in constructing a distance matrix based on the matching of all sample data pairs between the two time series. Then, the lag-lead structure is searched…
The performance of automatic speech recognition (ASR) systems can be significantly compromised by previously unseen conditions, which is typically due to a mismatch between training and testing distributions. In this paper, we address robustness by studying domain invariant features, such that domain information become…
Posterior sampling estimator achieves near-optimal recovery guarantees for signals from any prior distribution.
Many sleep studies suffer from the problem of insufficient data to fully utilize deep neural networks as different labs use different recordings set ups, leading to the need of training automated algorithms on rather small databases, whereas large annotated databases are around but cannot be directly included into thes…
In this study, we consider classification problems based on neural networks in data-imbalanced environment. Learning from an imbalanced data set is one of the most important and practical problems in the field of machine learning. A weighted loss function based on cost-sensitive approach is a well-known effective metho…
Most of the existing studies on voice conversion (VC) are conducted in acoustically matched conditions between source and target signal. However, the robustness of VC methods in presence of mismatch remains unknown. In this paper, we report a comparative analysis of different VC techniques under mismatched conditions. …
Paper proposes a new framework to improve policy optimization by aligning real and simulated data distributions.
SRRM improves recursive transport surrogates in the small-discrepancy regime.
Probabilistic models analyze data by relying on a set of assumptions. Data that exhibit deviations from these assumptions can undermine inference and prediction quality. Robust models offer protection against mismatch between a model's assumptions and reality. We propose a way to systematically detect and mitigate mism…
We study the problem of off-policy policy optimization in Markov decision processes, and develop a novel off-policy policy gradient method. Prior off-policy policy gradient approaches have generally ignored the mismatch between the distribution of states visited under the behavior policy used to collect data, and what …
We study the estimation capacity of the generalized Lasso, i.e., least squares minimization combined with a (convex) structural constraint. While Lasso-type estimators were originally designed for noisy linear regression problems, it has recently turned out that they are in fact robust against various types of model un…
Study addresses covariate mismatch in federated learning, improving model accuracy.
A new method improves data generation quality by correcting score mismatches.
A new RL method handles uncertainty and constraints in real-time optimization.
Dynamic pricing aims to match power supply and demand in an energy transition.