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.
Paper defines and solves a problem in representation learning to ensure fairness with high confidence.
New method reduces variance in stochastic optimization with high confidence.
Recent work has shown that state-of-the-art classifiers are quite brittle, in the sense that a small adversarial change of an originally with high confidence correctly classified input leads to a wrong classification again with high confidence. This raises concerns that such classifiers are vulnerable to attacks and ca…
New method predicts sets under unknown covariate shift with high confidence.
New method provides reliable high-confidence prediction intervals for high-impact events.
Standard results in stochastic convex optimization bound the number of samples that an algorithm needs to generate a point with small function value in expectation. More nuanced high probability guarantees are rare, and typically either rely on "light-tail" noise assumptions or exhibit worse sample complexity. In this …
Efficient method for high confidence level inference using parallel stochastic optimization.
New method for predicting paths of unpredictable objects with high confidence.
SNPL learns safe policies for multi-objective interventions with high confidence.
Develops a method to find costly high-confidence errors in black box models.
Optimal query allocation improves extractive QA efficiency with LLMs.
In the field of reinforcement learning there has been recent progress towards safety and high-confidence bounds on policy performance. However, to our knowledge, no practical methods exist for determining high-confidence policy performance bounds in the inverse reinforcement learning setting---where the true reward fun…
Estimating the value function for a fixed policy is a fundamental problem in reinforcement learning. Policy evaluation algorithms---to estimate value functions---continue to be developed, to improve convergence rates, improve stability and handle variability, particularly for off-policy learning. To understand the prop…
We address the problem of computing reliable policies in reinforcement learning problems with limited data. In particular, we compute policies that achieve good returns with high confidence when deployed. This objective, known as the \emph{percentile criterion}, can be optimized using Robust MDPs~(RMDPs). RMDPs general…
Classifiers used in the wild, in particular for safety-critical systems, should not only have good generalization properties but also should know when they don't know, in particular make low confidence predictions far away from the training data. We show that ReLU type neural networks which yield a piecewise linear cla…
Proposes a recursive MPC scheme with probabilistic safety guarantees for uncertain dynamic systems.
Deep neural networks are easily fooled high confidence predictions for adversarial samples
This paper proposes a distributionally robust approach to logistic regression. We use the Wasserstein distance to construct a ball in the space of probability distributions centered at the uniform distribution on the training samples. If the radius of this ball is chosen judiciously, we can guarantee that it contains t…
CADRO optimizes DRO by reducing conservatism through cost-aware ambiguity sets.
Study non-asymptotic BPI guarantees for online RL.
Mutual teaching improves graph models with less labeled data.
Bayesian inverse reinforcement learning (IRL) methods are ideal for safe imitation learning, as they allow a learning agent to reason about reward uncertainty and the safety of a learned policy. However, Bayesian IRL is computationally intractable for high-dimensional problems because each sample from the posterior req…
A key impediment to reinforcement learning (RL) in real applications with limited, batch data is defining a reward function that reflects what we implicitly know about reasonable behaviour for a task and allows for robust off-policy evaluation. In this work, we develop a method to identify an admissible set of reward f…
We introduce and analyse two algorithms for exploration-exploitation in discrete and continuous Markov Decision Processes (MDPs) based on exploration bonuses. SCAL is a variant of SCAL (Fruit et al., 2018) that performs efficient exploration-exploitation in any unknown weakly-communicating MDP for which an upper bo…
Efficient algorithms for -means clustering frequently converge to suboptimal partitions, and given a partition, it is difficult to detect -means optimality. In this paper, we develop an a posteriori certifier of approximate optimality for -means clustering. The certifier is a sub-linear Monte Carlo algorithm b…
Pathology reports contain useful information such as the main involved organ, diagnosis, etc. These information can be identified from the free text reports and used for large-scale statistical analysis or serve as annotation for other modalities such as pathology slides images. However, manual classification for a hug…
It has been suggested that adversarial examples cause deep learning models to make incorrect predictions with high confidence. In this work, we take the opposite stance: an overly confident model is more likely to be vulnerable to adversarial examples. This work is one of the most proactive approaches taken to date, as…
Training a neural network for a classification task typically assumes that the data to train are given from the beginning. However, in the real world, additional data accumulate gradually and the model requires additional training without accessing the old training data. This usually leads to the catastrophic forgettin…
Unified framework for best arm identification and dueling bandits regret minimization.
Smoothed analysis is a powerful paradigm in overcoming worst-case intractability in unsupervised learning and high-dimensional data analysis. While polynomial time smoothed analysis guarantees have been obtained for worst-case intractable problems like tensor decompositions and learning mixtures of Gaussians, such guar…
GAAVI offers anytime-valid tests for CMF global null and contrasts.
BALLET filters a high-confidence region of interest for Bayesian optimization.
Bayes-TrEx finds in-distribution examples for model inspection.
Hybrid Bayesian-conformal framework improves uncertainty quantification in healthcare predictions.
One of the main drawbacks of deep neural networks, like many other classifiers, is their vulnerability to adversarial attacks. An important reason for their vulnerability is assigning high confidence to regions with few or even no feature points. By feature points, we mean a nonlinear transformation of the input space …
New algorithms detect and react to multiple change points in online learning.
Measuring mutual information from finite data is difficult. Recent work has considered variational methods maximizing a lower bound. In this paper, we prove that serious statistical limitations are inherent to any method of measuring mutual information. More specifically, we show that any distribution-free high-confide…
The study analyzes group testing algorithms for identifying defective items with high confidence.
New sampling bounds improve uniform coverage verification in machine learning.
Tree-Query uses LLMs to discover causal relationships in a transparent, interpretable manner.
Sampling random points can reveal submanifold topology.
Researchers show how to secretly train models with hidden data, detect usage with high confidence.
For an autonomous agent, executing a poor policy may be costly or even dangerous. For such agents, it is desirable to determine confidence interval lower bounds on the performance of any given policy without executing said policy. Current methods for exact high confidence off-policy evaluation that use importance sampl…
Bayesian REX learns Atari games from demonstrations efficiently.
ICP provides interval predictions with high confidence coverage.
New method defends against patch attacks with high-certainty guarantees.
Trained DNN models are increasingly adopted as integral parts of software systems, but they often perform deficiently in the field. A particularly damaging problem is that DNN models often give false predictions with high confidence, due to the unavoidable slight divergences between operation data and training data. To…
Dash selects dynamic pseudo labels from unlabeled data for semi-supervised learning.