Author name cluster

Tim Klinger

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

11 papers

1 author row

AAAI Conference 2025 Conference Paper

Neural Reasoning Networks: Efficient Interpretable Neural Networks with Automatic Textual Explanations

Stephen Carrow
Kyle Erwin
Olga Vilenskaia
Parikshit Ram
Tim Klinger
Naweed Khan
Ndivhuwo Makondo
Alexander G. Gray

Recent advances in machine learning have led to a surge in adoption of neural networks for various tasks, but lack of interpretability remains an issue for many others in which an understanding of the features influencing the prediction is necessary to ensure fairness, safety, and legal compliance. In this paper we consider one class of such tasks, tabular dataset classification, and propose a novel neuro-symbolic architecture, Neural Reasoning Networks (NRN), that is scalable and generates logically sound textual explanations for its predictions. NRNs are connected layers of logical neurons that implement a form of real valued logic. A training algorithm (R-NRN) learns the weights of the network as usual using gradient descent optimization with backprop, but also learns the network structure itself using a bandit-based optimization. Both are implemented in an extension to PyTorch that takes full advantage of GPU scaling and batched training. Evaluation on a diverse set of 22 open-source datasets for tabular classification demonstrates performance (measured by ROC AUC) which improves over Multilayer Perceptron (MLP) and is statistically similar to other state-of-the-art approaches such as Random Forest, XGBoost and Gradient Boosted Trees, while offering 43% faster training and a more than 2 orders of magnitude reduction in the number of parameters required, on average. Furthermore, R-NRN explanations are shorter than the compared approaches while producing more accurate feature importance scores.

PDF Details DOI

NeurIPS Conference 2025 Conference Paper

Transformers Learn Faster with Semantic Focus

Parikshit Ram
Kenneth Clarkson
Tim Klinger
Shashanka Ubaru
Alexander Gray

Various forms of sparse attention have been explored to mitigate the quadratic computational and memory cost of the attention mechanism in transformers. We study sparse transformers not through a lens of efficiency but rather in terms of learnability and generalization. Empirically studying a range of attention mechanisms, we find that input-dependent sparse attention models appear to converge faster and generalize better than standard attention models, while input-agnostic sparse attention models show no such benefits -- a phenomenon that is robust across architectural and optimization hyperparameter choices. This can be interpreted as demonstrating that concentrating a model's "semantic focus" with respect to the tokens currently being considered (in the form of input-dependent sparse attention) accelerates learning. We develop a theoretical characterization of the conditions that explain this behavior. We establish a connection between the stability of the standard softmax and the loss function's Lipschitz properties, then show how sparsity affects the stability of the softmax and the subsequent convergence and generalization guarantees resulting from the attention mechanism. This allows us to theoretically establish that input-agnostic sparse attention does not provide any benefits. We also characterize conditions when semantic focus (input-dependent sparse attention) can provide improved guarantees, and we validate that these conditions are in fact met in our empirical evaluations.

PDF Details

IJCAI Conference 2024 Conference Paper

What Makes Models Compositional? A Theoretical View

Parikshit Ram
Tim Klinger
Alexander G. Gray

Compositionality is thought to be a key component of language, and various compositional benchmarks have been developed to empirically probe the compositional generalization of existing sequence processing models. These benchmarks often highlight failures of existing models, but it is not clear why these models fail in this way. In this paper, we seek to theoretically understand the role the compositional structure of the models plays in these failures and how this structure relates to their expressivity and sample complexity. We propose a general neuro-symbolic definition of compositional functions and their compositional complexity. We then show how various existing general and special purpose sequence processing models (such as recurrent, convolution and attention-based ones) fit this definition and use it to analyze their compositional complexity. Finally, we provide theoretical guarantees for the expressivity and systematic generalization of compositional models that explicitly depend on our proposed definition and highlighting factors which drive poor empirical performance.

PDF Details DOI

NeurIPS Conference 2022 Conference Paper

Compositional generalization through abstract representations in human and artificial neural networks

Takuya Ito
Tim Klinger
Doug Schultz
John Murray
Michael Cole
Mattia Rigotti

Humans have a remarkable ability to rapidly generalize to new tasks that is difficult to reproduce in artificial learning systems. Compositionality has been proposed as a key mechanism supporting generalization in humans, but evidence of its neural implementation and impact on behavior is still scarce. Here we study the computational properties associated with compositional generalization in both humans and artificial neural networks (ANNs) on a highly compositional task. First, we identified behavioral signatures of compositional generalization in humans, along with their neural correlates using whole-cortex functional magnetic resonance imaging (fMRI) data. Next, we designed pretraining paradigms aided by a procedure we term primitives pretraining to endow compositional task elements into ANNs. We found that ANNs with this prior knowledge had greater correspondence with human behavior and neural compositional signatures. Importantly, primitives pretraining induced abstract internal representations, excellent zero-shot generalization, and sample-efficient learning. Moreover, it gave rise to a hierarchy of abstract representations that matched human fMRI data, where sensory rule abstractions emerged in early sensory areas, and motor rule abstractions emerged in later motor areas. Our findings give empirical support to the role of compositional generalization in humans behavior, implicate abstract representations as its neural implementation, and illustrate that these representations can be embedded into ANNs by designing simple and efficient pretraining procedures.

PDF Details

PRL Workshop 2021 Workshop Paper

AI Planning Annotation in Reinforcement Learning: Options and Beyond

Junkyu Lee
Michael Katz
Don Joven Agravante
Miao Liu
Tim Klinger
Murray Campbell
Shirin Sohrabi
Gerald Tesauro

AI planning and reinforcement learning (RL) both solve sequential decision-making problems, taking fundamentally different approaches. In this work, we aim to bring AI planning and RL closer by investigating the relationship between abstractions in AI planning and the options framework in RL. To this end, we propose annotating RL tasks with AI planning models, allowing us to define options based purely on the planning model. Our experimental investigation shows that these options can be quickly trained offline and can improve the sample efficiency of a reinforcement learning algorithm.

PDF Details

IJCAI Conference 2021 Conference Paper

Efficient Black-Box Planning Using Macro-Actions with Focused Effects

Cameron Allen
Michael Katz
Tim Klinger
George Konidaris
Matthew Riemer
Gerald Tesauro

The difficulty of deterministic planning increases exponentially with search-tree depth. Black-box planning presents an even greater challenge, since planners must operate without an explicit model of the domain. Heuristics can make search more efficient, but goal-aware heuristics for black-box planning usually rely on goal counting, which is often quite uninformative. In this work, we show how to overcome this limitation by discovering macro-actions that make the goal-count heuristic more accurate. Our approach searches for macro-actions with focused effects (i. e. macros that modify only a small number of state variables), which align well with the assumptions made by the goal-count heuristic. Focused macros dramatically improve black-box planning efficiency across a wide range of planning domains, sometimes beating even state-of-the-art planners with access to a full domain model.

PDF Details DOI

AAAI Conference 2021 Short Paper

RL Generalization in a Theory of Mind Game Through a Sleep Metaphor (Student Abstract)

Tyler Malloy
Tim Klinger
Miao Liu
Gerald Tesauro
Matthew Riemer
Chris R. Sims

Training agents to learn efficiently in multi-agent environments can benefit from the explicit modelling of other agent’s beliefs, especially in complex limited-information games such as the Hanabi card game. However, generalization is also highly relevant to performance in these games, though model comparisons at large training timescales can be difficult. In this work, we address this by introducing a novel model trained using a sleep metaphor on a reduced complexity version of the Hanabi game. This sleep metaphor consists an altered training regiment, as well as an informationtheoretic constraint on the agent’s policy. Results from experimentation demonstrate improved performance through this sleep-metaphor method, and provide a promising motivation for using similar techniques in more complex methods that incorporate explicit models of other agent’s beliefs.

PDF Details

AAAI Conference 2019 Conference Paper

Scalable Recollections for Continual Lifelong Learning

Matthew Riemer
Tim Klinger
Djallel Bouneffouf
Michele Franceschini

Given the recent success of Deep Learning applied to a variety of single tasks, it is natural to consider more human-realistic settings. Perhaps the most difficult of these settings is that of continual lifelong learning, where the model must learn online over a continuous stream of non-stationary data. A successful continual lifelong learning system must have three key capabilities: it must learn and adapt over time, it must not forget what it has learned, and it must be efficient in both training time and memory. Recent techniques have focused their efforts primarily on the first two capabilities while questions of efficiency remain largely unexplored. In this paper, we consider the problem of efficient and effective storage of experiences over very large time-frames. In particular we consider the case where typical experiences are O(n) bits and memories are limited to O(k) bits for k << n. We present a novel scalable architecture and training algorithm in this challenging domain and provide an extensive evaluation of its performance. Our results show that we can achieve considerable gains on top of state-of-the-art methods such as GEM. 1

PDF Details

AAAI Conference 2018 Conference Paper

R 3: Reinforced Ranker-Reader for Open-Domain Question Answering

Shuohang Wang
Mo Yu
Xiaoxiao Guo
Zhiguo Wang
Tim Klinger
Wei Zhang
Shiyu Chang
Gerry Tesauro

In recent years researchers have achieved considerable success applying neural network methods to question answering (QA). These approaches have achieved state of the art results in simpliﬁed closed-domain settings1 such as the SQuAD (Rajpurkar et al. 2016) dataset, which provides a preselected passage, from which the answer to a given question may be extracted. More recently, researchers have begun to tackle open-domain QA, in which the model is given a question and access to a large corpus (e. g. , wikipedia) instead of a pre-selected passage (Chen et al. 2017a). This setting is more complex as it requires large-scale search for relevant passages by an information retrieval component, combined with a reading comprehension model that “reads” the passages to generate an answer to the question. Performance in this setting lags well behind closed-domain performance. In this paper, we present a novel open-domain QA system called Reinforced Ranker-Reader (R3 ), based on two algorithmic innovations. First, we propose a new pipeline for open-domain QA with a Ranker component, which learns to rank retrieved passages in terms of likelihood of extracting the ground-truth answer to a given question. Second, we propose a novel method that jointly trains the Ranker along with an answer-extraction Reader model, based on reinforcement learning. We report extensive experimental results showing that our method signiﬁcantly improves on the state of the art for multiple open-domain QA datasets. 2

PDF Details

AAAI Conference 2017 Conference Paper

Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation

Iulian Serban
Tim Klinger
Gerald Tesauro
Kartik Talamadupula
Bowen Zhou
Yoshua Bengio
Aaron Courville

We introduce a new class of models called multiresolution recurrent neural networks, which explicitly model natural language generation at multiple levels of abstraction. The models extend the sequence-to-sequence framework to generate two parallel stochastic processes: a sequence of high-level coarse tokens, and a sequence of natural language words (e. g. sentences). The coarse sequences follow a latent stochastic process with a factorial representation, which helps the models generalize to new examples. The coarse sequences can also incorporate task-speciﬁc knowledge, when available. In our experiments, the coarse sequences are extracted using automatic procedures, which are designed to capture compositional structure and semantics. These procedures enable training the multiresolution recurrent neural networks by maximizing the exact joint log-likelihood over both sequences. We apply the models to dialogue response generation in the technical support domain and compare them with several competing models. The multiresolution recurrent neural networks outperform competing models by a substantial margin, achieving stateof-the-art results according to both a human evaluation study and automatic evaluation metrics. Furthermore, experiments show the proposed models generate more ﬂuent, relevant and goal-oriented responses.

PDF Details

AAAI Conference 2015 Conference Paper

Towards Cognitive Automation of Data Science

Alain Biem
Maria Butrico
Mark Feblowitz
Tim Klinger
Yuri Malitsky
Kenney Ng
Adam Perer
Chandra Reddy

A Data Scientist typically performs a number of tedious and time-consuming steps to derive insight from a raw data set. The process usually starts with data ingestion, cleaning, and transformation (e. g. outlier removal, missing value imputation), then proceeds to model building, and finally a presentation of predictions that align with the end-users objectives and preferences. It is a long, complex, and sometimes artful process requiring substantial time and effort, especially because of the combinatorial explosion in choices of algorithms (and platforms), their parameters, and their compositions. Tools that can help automate steps in this process have the potential to accelerate the time-to-delivery of useful results, expand the reach of data science to non-experts, and offer a more systematic exploration of the available options. This work presents a step towards this goal.

PDF Details