arXiv research

A new method for student-initiated action advice using novelty detection.

Advice-efficient prediction with expert advice (in analogy to label-efficient prediction) is a variant of prediction with expert advice game, where on each round of the game we are allowed to ask for advice of a limited number $M$ out of $N$ experts. This setting is especially interesting when asking for advice of ever…

The paper tackles AI advice giving by considering adherence levels and defer options.

Improved learning of multivariate Gaussians with imperfect advice.

New algorithm learns causal structure with advice, improving efficiency.

A new method for learning to defer decisions with expert advice improves over standard methods.

New algorithm uses imperfect advice to improve online bipartite matching performance.

Investigates fast prediction rates with limited expert advice.

Training deep reinforcement learning agents complex behaviors in 3D virtual environments requires significant computational resources. This is especially true in environments with high degrees of aliasing, where many states share nearly identical visual features. Minecraft is an exemplar of such an environment. We hypo…

New algorithms improve on consistency and robustness in convex function chasing with black-box advice.

Over the last few years, there has been growing interest in learning models for physically grounded language understanding tasks, such as the popular blocks world domain. These works typically view this problem as a single-step process, in which a human operator gives an instruction and an automated agent is evaluated …

Study finds optimal regret bound for multi-armed bandit problem with expert advice.

Conventional learning with expert advice methods assumes a learner is always receiving the outcome (e.g., class labels) of every incoming training instance at the end of each trial. In real applications, acquiring the outcome from oracle can be costly or time consuming. In this paper, we address a new problem of active…

LIMEADE improves AI advice for opaque models, enhancing accuracy and user satisfaction.

Improved regret bounds for bandits with expert advice.

Improved algorithm reduces regret in corrupted expert advice setting.

Improved regret bounds for bandits with fixed expert advice using information theory.

Sparse reward is one of the most challenging problems in reinforcement learning (RL). Hindsight Experience Replay (HER) attempts to address this issue by converting a failed experience to a successful one by relabeling the goals. Despite its effectiveness, HER has limited applicability because it lacks a compact and un…

We provide the first algorithm for online bandit linear optimization whose regret after T rounds is of order sqrt{Td ln N} on any finite class X of N actions in d dimensions, and of order d*sqrt{T} (up to log factors) when X is infinite. These bounds are not improvable in general. The basic idea utilizes tools from con…

Recently, deep models have been successfully applied in several applications, especially with low-level representations. However, sparse, noisy samples and structured domains (with multiple objects and interactions) are some of the open challenges in most deep models. Column Networks, a deep architecture, can succinctl…

Proposes a novel method for detecting novelty in multi-modal data.

Study shows LLM-advisors match human performance in eliciting preferences but struggle with conflicting needs and trust.

Improves reward bounds for prediction with expert advice using abstention.

Deep learning detects novel changes in time series data.

Novelty detection is the unsupervised problem of identifying anomalies in test data which significantly differ from the training set. Novelty detection is one of the classic challenges in Machine Learning and a core component of several research areas such as fraud detection, intrusion detection, medical diagnosis, dat…

Adapts model-based advice to stabilize black-box policies for nonlinear control.

WSB's investment advice significantly outperformed the S&P500 over 3 years, but not consistently.

Recently, deep models have had considerable success in several tasks, especially with low-level representations. However, effective learning from sparse noisy samples is a major challenge in most deep models, especially in domains with structured representations. Inspired by the proven success of human guided machine l…

We prove non-asymptotic lower bounds on the expectation of the maximum of $d$ independent Gaussian variables and the expectation of the maximum of $d$ independent symmetric random walks. Both lower bounds recover the optimal leading constant in the limit. A simple application of the lower bound for random walks is an (…

Sharp bounds found on expert error in binary advice aggregation.

This paper introduces CENIE to quantify environment novelty for better UED.

Optimal algorithm reduces regret in adversarial bandit problem with multiple plays.

Many currently deployed Reinforcement Learning agents work in an environment shared with humans, be them co-workers, users or clients. It is desirable that these agents adjust to people's preferences, learn faster thanks to their help, and act safely around them. We argue that most current approaches that learn from hu…

Since datasets with annotation for novelty at the document and/or word level are not easily available, we present a simulation framework that allows us to create different textual datasets in which we control the way novelty occurs. We also present a benchmark of existing methods for novelty detection in textual data s…

Generalized algorithm for translation and scale-invariant prediction.

Generatability in metric spaces studied with novel novelty parameters.

In the framework of prediction with expert advice, we consider a recently introduced kind of regret bounds: the bounds that depend on the effective instead of nominal number of experts. In contrast to the Normal- Hedge bound, which mainly depends on the effective number of experts but also weakly depends on the nominal…

As more and more people shift their movie watching online, competition between movie viewing websites are getting more and more intense. Therefore, it has become incredibly important to accurately predict a given user's watching list to maximize the chances of keeping the user on the platform. Recent studies have sugge…

New algorithms improve prediction with expert advice under local differential privacy.

This work introduces a novel method to evaluate generative model novelty.

In machine learning, novelty detection is the task of identifying novel unseen data. During training, only samples from the normal class are available. Test samples are classified as normal or abnormal by assignment of a novelty score. Here we propose novelty detection methods based on training variational autoencoders…

A new framework uses deep RL to aggregate expert advice for better portfolio management.

Survey of algorithms to correct past mistakes in prediction.

Paper studies continuous prediction with experts' advice using differential equations.

CSI detects novelty by contrasting shifted instances, outperforming existing methods.

BERT learns claim descriptions to identify patent novelty.

The leaderboard in machine learning competitions is a tool to show the performance of various participants and to compare them. However, the leaderboard quickly becomes no longer accurate, due to hack or overfitting. This article gives two pieces of advice to prevent easy hack or overfitting. By following these advice,…

We consider an original problem that arises from the issue of security analysis of a power system and that we name optimal discovery with probabilistic expert advice. We address it with an algorithm based on the optimistic paradigm and on the Good-Turing missing mass estimator. We prove two different regret bounds on t…