Sebastian Tschiatschek

research

∙ 05/26/2023

Applying Interdisciplinary Frameworks to Understand Algorithmic Decision-Making

We argue that explanations for "algorithmic decision-making" (ADM) syste...

0 Timothée Schmude, et al. ∙

research

∙ 05/25/2023

Learning Safety Constraints from Demonstrations with Unknown Rewards

We propose Convex Constraint Learning for Reinforcement Learning (CoCoRL...

0 David Lindner, et al. ∙

research

∙ 03/28/2023

Adaptive Scaffolding in Block-Based Programming via Synthesizing New Tasks as Pop Quizzes

Block-based programming environments are increasingly used to introduce ...

0 Ahana Ghosh, et al. ∙

research

∙ 02/16/2023

On the Impact of Explanations on Understanding of Algorithmic Decision-Making

Ethical principles for algorithms are gaining importance as more and mor...

0 Timothée Schmude, et al. ∙

research

∙ 06/10/2022

Interactively Learning Preference Constraints in Linear Bandits

We study sequential decision-making with known rewards and unknown const...

0 David Lindner, et al. ∙

research

∙ 05/04/2022

Equity and Fairness of Bayesian Knowledge Tracing

We consider the equity and fairness of curricula derived from Knowledge ...

1 Sebastian Tschiatschek, et al. ∙

research

∙ 10/18/2021

MDP Abstraction with Successor Features

Abstraction plays an important role for generalisation of knowledge and ...

17 Dongge Han, et al. ∙

research

∙ 02/24/2021

Information Directed Reward Learning for Reinforcement Learning

For many reinforcement learning (RL) applications, specifying a reward i...

0 David Lindner, et al. ∙

research

∙ 11/02/2020

Reinforcement Learning with Efficient Active Feature Acquisition

Solving real-life sequential decision making problems under partial obse...

0 Haiyan Yin, et al. ∙

research

∙ 06/25/2020

Replication-Robust Payoff-Allocation with Applications in Machine Learning Marketplaces

The ever-increasing take-up of machine learning techniques requires ever...

15 Dongge Han, et al. ∙

research

∙ 06/21/2020

VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data

Deep generative models often perform poorly in real-world applications d...

14 Chao Ma, et al. ∙

research

∙ 03/12/2020

Large-Scale Educational Question Analysis with Partial Variational Auto-encoders

Online education platforms enable teachers to share a large number of ed...

0 Zichao Wang, et al. ∙

research

∙ 01/07/2020

Resource-Efficient Neural Networks for Embedded Systems

While machine learning is traditionally a resource intensive task, embed...

21 Wolfgang Roth, et al. ∙

research

∙ 11/08/2019

Collaborative Machine Learning Markets with Data-Replication-Robust Payments

We study the problem of collaborative machine learning markets where mul...

0 Olga Ohrimenko, et al. ∙

research

∙ 10/28/2019

Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck

The ability for policies to generalize to new environments is key to the...

16 Maximilian Igl, et al. ∙

research

∙ 10/05/2019

Towards Deployment of Robust AI Agents for Human-Machine Partnerships

We study the problem of designing AI agents that can robustly cooperate ...

0 Ahana Ghosh, et al. ∙

research

∙ 08/13/2019

Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model

In this paper we introduce the ice-start problem, i.e., the challenge of...

3 Wenbo Gong, et al. ∙

research

∙ 06/02/2019

Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints

Inverse reinforcement learning (IRL) enables an agent to learn complex b...

0 Sebastian Tschiatschek, et al. ∙

research

∙ 12/05/2018

Efficient and Robust Machine Learning for Real-World Systems

While machine learning is traditionally a resource intensive task, embed...

17 Franz Pernkopf, et al. ∙

research

∙ 10/21/2018

Teaching Inverse Reinforcement Learners via Features and Demonstrations

Learning near-optimal behaviour from an expert's demonstrations typicall...

0 Luis Haug, et al. ∙

research

∙ 10/15/2018

Successor Uncertainties: exploration and uncertainty in temporal difference learning

We consider the problem of balancing exploration and exploitation in seq...

0 David Janz, et al. ∙

research

∙ 09/28/2018

EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE

Making decisions requires information relevant to the task at hand. Many...

6 Chao Ma, et al. ∙

research

∙ 07/06/2018

Sum-Product Networks for Sequence Labeling

We consider higher-order linear-chain conditional random fields (HO-LC-C...

0 Martin Ratajczak, et al. ∙

research

∙ 05/23/2018

Variational Inference for Data-Efficient Model Learning in POMDPs

Partially observable Markov decision processes (POMDPs) are a powerful a...

0 Sebastian Tschiatschek, et al. ∙

research

∙ 03/05/2018

Differentiable Submodular Maximization

We consider learning of submodular functions from data. These functions ...

0 Sebastian Tschiatschek, et al. ∙

research

∙ 11/17/2017

Learning User Preferences to Incentivize Exploration in the Sharing Economy

We study platforms in the sharing economy and discuss the need for incen...

0 Christoph Hirnschall, et al. ∙

research

∙ 02/09/2017

Coordinated Online Learning With Applications to Learning User Preferences

We study an online multi-task learning setting, in which instances of re...

0 Christoph Hirnschall, et al. ∙

research

∙ 05/23/2016

Actively Learning Hemimetrics with Applications to Eliciting User Preferences

Motivated by an application of eliciting users' preferences, we investig...

0 Adish Singla, et al. ∙

research

∙ 11/23/2015

Noisy Submodular Maximization via Adaptive Sampling with Applications to Crowdsourced Image Collection Summarization

We address the problem of maximizing an unknown submodular function that...

0 Adish Singla, et al. ∙

Sebastian Tschiatschek

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