Travelled to:1 × Canada
1 × Italy
8 × USA
Collaborated with:Y.Mansour S.P.Singh L.G.Valiant M.Li ∅ S.Kakade J.Langford T.G.Dietterich D.Ron R.Rubinfeld R.E.Schapire L.Sellie K.Myers M.A.Walker L.Pitt A.Blum M.L.Furst J.C.Jackson S.Rudich Y.Freund
Talks about:learn (9) algorithm (2) learnabl (2) automata (2) statist (2) formula (2) boolean (2) effici (2) queri (2) optim (2)
Person: Michael J. Kearns
DBLP: Kearns:Michael_J=
Contributed to:
2003200019981996
199619941993198919881987Wrote 13 papers:
- ICML-2003-KakadeKL #metric
- Exploration in Metric State Spaces (SK, MJK, JL), pp. 306–312.
- ICML-2000-MyersKSW #approach #topic
- A Boosting Approach to Topic Spotting on Subdialogues (KM, MJK, SPS, MAW), pp. 655–662.
- ICML-1998-KearnsM #algorithm #bottom-up #performance
- A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization (MJK, YM), pp. 269–277.
- ICML-1998-KearnsS #learning
- Near-Optimal Reinforcement Learning in Polynominal Time (MJK, SPS), pp. 260–268.
- ICML-1996-DietterichKM #framework #learning
- Applying the Waek Learning Framework to Understand and Improve C4.5 (TGD, MJK, YM), pp. 96–104.
- STOC-1996-KearnsM #algorithm #learning #on the #top-down
- On the Boosting Ability of Top-Down Decision Tree Learning Algorithms (MJK, YM), pp. 459–468.
- STOC-1994-BlumFJKMR #analysis #fourier #learning #query #statistics #using
- Weakly learning DNF and characterizing statistical query learning using Fourier analysis (AB, MLF, JCJ, MJK, YM, SR), pp. 253–262.
- STOC-1994-KearnsMRRSS #on the
- On the learnability of discrete distributions (MJK, YM, DR, RR, RES, LS), pp. 273–282.
- STOC-1993-FreundKRRSS #automaton #finite #learning #performance #random
- Efficient learning of typical finite automata from random walks (YF, MJK, DR, RR, RES, LS), pp. 315–324.
- STOC-1993-Kearns #learning #performance #query #statistics
- Efficient noise-tolerant learning from statistical queries (MJK), pp. 392–401.
- STOC-1989-KearnsV #automaton #encryption #finite #learning
- Cryptographic Limitations on Learning Boolean Formulae and Finite Automata (MJK, LGV), pp. 433–444.
- STOC-1988-KearnsL #fault #learning
- Learning in the Presence of Malicious Errors (MJK, ML), pp. 267–280.
- STOC-1987-KearnsLPV #on the
- On the Learnability of Boolean Formulae (MJK, ML, LP, LGV), pp. 285–295.